Patent Publication Number: US-2023164510-A1

Title: Electronic device, method and computer program

Description:
TECHNICAL FIELD 
     The present disclosure generally pertains to the field of audio processing, and in particular, to devices, methods and computer programs for distraction level minimization. 
     TECHNICAL BACKGROUND 
     There is a lot of audio content available, for example, in the form of compact disks (CD), tapes, audio data files which can be downloaded from the internet, but also in the form of sound tracks of videos, e.g. stored on a digital video disk or the like, etc. 
     In an automotive environment, different driving/passengers situations may occur where the playback of audio content is disturbing, which poses safety problems (e.g., harsh sounds from the back during driving a car). However, there exist ways to minimize a distraction level of an audio stream, by analyzing the distraction level and adapting the playout accordingly, for example by reducing the volume of music played back by the vehicle audio system. 
     With the arrival of spatial audio object oriented systems like Dolby Atmos, DTS-X or more recently Sony 360 Reality Audio (360RA), there is a need to find some methods to reduce possible safety problems of playing back 360RA audio material in the automotive field. Especially because the audio content created in 360RA format (MPEG-H) could contain disruptive sounds (e.g., impulsive effect from the door sides or voices from the back) which can be localized by the driver and, hence, could cause distraction. 
     Although there exist techniques for audio object stream modification, it is generally desirable to improve devices and methods for audio object stream modification. 
     SUMMARY 
     According to a first aspect, the disclosure provides an electronic device comprising circuitry configured to estimate a distraction level of an audio object stream, and to modify the audio object stream based on the estimated distraction level to obtain a modified audio object stream. 
     According to a second aspect, the disclosure provides a method comprising estimating a distraction level of an audio object stream and modifying the audio object stream based on the estimated distraction level to obtain a modified audio object stream. 
     According to a third aspect, the disclosure provides a computer program comprising instructions, the instructions when executed on a processor causing the processor to estimate a distraction level of an audio object stream, and to modify the audio object stream based on the estimated distraction level to obtain a modified audio object stream. 
     Further aspects are set forth in the dependent claims, the following description and the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments are explained by way of example with respect to the accompanying drawings, in which: 
         FIG.  1    schematically illustrates an exemplary method in accordance with the present disclosure ; 
         FIG.  2   a    illustrates an exemplary embodiment of a device implementing a process of a distraction minimization in an audio object stream, as described in  FIG.  1   ; 
         FIG.  2   b    illustrates another exemplary embodiment of a device implementing a process of a distraction minimization in an audio object stream, as described in  FIG.  1   ; 
         FIG.  3    schematically describes in more detail an embodiment of a field of listening estimation as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b   ; 
         FIG.  4    visualizes how position distraction level of an audio object stream is related to the position of a driver in an in-vehicle-scenario; 
         FIG.  5   a    schematically describes in more detail an embodiment of a sound signature estimation as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b   ; 
         FIG.  5   b    schematically describes an embodiment of a process for determining a distraction level, such as the aural distraction level  31  of  FIG.  5   a    and the position distraction level  21  of  FIG.  3   , based on the power spectrum P f (n); 
         FIG.  6    schematically describes in more detail an embodiment of a distance calculation as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b   ; 
         FIG.  7    schematically describes in more detail an embodiment of a process of a driving situation analyzer as performed in the process of distraction minimization in an audio object stream described in  FIG.  2   b   ; 
         FIG.  8    schematically describes in more detail an embodiment of a process of novelty factor estimation as performed in the process of distraction minimization in an audio object stream described in  FIG.  2   b   ; 
         FIG.  9    schematically describes in more detail an embodiment of a process of audio source and coordinate extraction as performed in the process of distraction minimization in an audio object stream; 
         FIG.  10    schematically describes in more detail an embodiment of determining a list of actions by a decision tree as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b   ; 
         FIG.  11   a    illustrates a process of the embodiment described in  FIG.  2   a   , implemented by a decision tree and an action block; 
         FIG.  11   b    illustrates a process of the embodiment described in  FIG.  2   b   , implemented by a decision tree and an action block; 
         FIG.  12   a    shows a flow diagram visualizing an exemplary method for performing audio stream modification as described in  FIG.  2   a   ; 
         FIG.  12   b    shows a flow diagram visualizing an exemplary method for performing audio stream modification as described in  FIG.  2   b   ; 
         FIG.  13    shows a block diagram depicting an example of schematic configuration of a vehicle control system; 
         FIG.  14    shows an example of installation positions of the imaging section and the outside-vehicle information detecting section; and 
         FIG.  15    shows a block diagram depicting an example of schematic configuration of a device implementing a distraction minimization system. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     Before a detailed description of the embodiments under reference of  FIGS.  1  to  15    is given, some general explanations are made. 
     The embodiments disclose an electronic device comprising circuitry configured to estimate a distraction level of an audio object stream, and to modify the audio object stream based on the estimated distraction level to obtain a modified audio object stream. 
     The electronic device may for example be an electronic control unit (ECU) within the vehicle. ECUs are typically used in vehicles e.g. as a Door Control Unit (DCU), an Engine Control Unit (ECU), an Electric Power Steering Control Unit (PSCU), a Human-Machine Interface (HMI), a Powertrain Control Module (PCM), a Seat Control Unit, a Speed Control Unit (SCU), a Telematic Control Unit (TCU), a Trans-mission Control Unit (TCU), a Brake Control Module (BCM; ABS or ESC), a Battery Management System (BMS), and/or a 3D audio rendering system. The electronic device may be an ECU that is specifically used for the purpose of controlling a vehicle audio system. Alternatively, an ECU that performs any of the functions described above, or any other function, may be used simultaneously for the purpose of controlling a vehicle audio system. Moreover, the electronic device may for example be a smart speaker capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, providing weather, traffic, sports, and other real-time information, such as news or the like. The electronic device may also have the functions of a home automation system, for example, for playback in a living room. The electronic device may thus provide audio content, such as a modified audio object stream having a reduced distraction level, consisting of spatial audio objects, such as audio monopoles or the like. 
     The circuitry of the electronic device may include a processor, may, for example, be a CPU, a memory (RAM, ROM or the like), and/or storage, interfaces, etc. Circuitry may also comprise or may be connected with input means (mouse, keyboard, camera, etc.), output means (display (e.g. liquid crystal, (organic) light emitting diode, etc.)), loudspeakers, etc., a (wireless) interface, etc., as it is generally known for electronic devices (computers, smartphones, etc.). Moreover, circuitry may comprise or may be connected with sensors for sensing still images or video image data (image sensor, camera sensor, video sensor, etc.), for sensing environmental parameters (e.g. radar, humidity, light, temperature), etc. Furthermore, the electronic device may be an audio-enabled product, which generates some multi-channel spatial rendering. The electronic device may be an audio-object playback system e.g. a 360RA head-unit in automotive environment, Home A/V receivers, TV, sound-bar, multi-channels (playback) system, virtualizer on headphones, Binaural Headphones, or the like. 
     An audio object stream, such as audio formats like 360 Reality Audio (360RA), is object-based instead of channel-based. The audio format, which is object-based (MPEG-H), may contain sound sources at arbitrary positions on a sphere. Thereby, sound sources are placed at arbitrary positions in the three-dimensional (3D) space, and this may give the content creator more flexibility in his artistic process. The audio stream may be obtained over a communication bus in a vehicle, from a multimedia system within the vehicle, from a digital radio receiver, from an MPEG player, a CD player, or the like. Besides 360RA, there are also other audio formats (Dolby Atmos, Auro3D, ...) which allow placing audio sources in the full 3D space. 
     3D audio may manipulate the sound produced by stereo speakers, surround-sound speakers, speaker-arrays, or headphones and involves the virtual placement of sound sources anywhere in three-dimensional space, including behind, above or below the listener. In this way, individual sounds such as vocals, chorus, piano, guitar, bass and even sounds of the live audience can be placed in a e.g. 360 spherical sound field. 
     There are different specifications for coding 3D audio, for example MPEG-H 3D Audio (ISO/IEC 23008-3, MPEG-H Part 3), Dolby Digital (AC-3), MP3, AAC, 360 Reality Audio, etc. All these specifications support coding audio as audio objects, audio channels, Ambisonics (HOA), etc. Channels, objects, and Ambisonics components may be used to transmit immersive sound as well as mono, stereo, or surround sound. 
     From a data coding point of view, audio objects consist of audio data which is comprised in the audio object stream as an audio bitstream plus associated metadata (object position, gain, etc.). The audio bitstream may, for example, be encoded according to an audio bitstream format such as the Waveform Audio File Format (WAV) or a compressed audio bitstream such as MP3 or the like. 
     The circuitry may be further configured to evaluate the decision tree to determine a list of actions. For example, the list of actions may include an amplitude reduction of an audio object, a low-pass/median filtering, a modification of position, and/or the like. The list of actions may contain any number of actions, for example, the list of actions may contain one action or more than one actions. The list of actions may be determined for each set of distraction levels by evaluating the decision tree. Additionally, the list of actions may be applied to one or any number of audio objects in the stream. 
     The circuitry may be further configured to perform an action block to obtain the modified audio object stream. The action block may execute, on the audio object stream, the list of actions determined by the decision tree to obtain the modified audio object stream. 
     The circuitry may be configured to estimate a distraction level of an audio object stream, such as for example, a position distraction level, an aural distraction level, a distance estimation, and/or the like. 
     The circuitry may be configured to modify the audio object stream based on the estimated distraction level to obtain a modified audio object stream. The modified audio object stream may be an audio object stream with minimized distraction levels, and thereby, for example, distraction from the driving situation in a car may be reduced, or stress in a home environment for sick people may be prevented, for example, the playback may be adapted to avoid high stress levels for persons with a heart disease, or the like. 
     In some embodiments, the circuitry may be further configured to modify the audio object stream based on the estimated distraction level by an audio object stream modification including a decision tree. The audio object stream modification may be an audio object stream modifier, which includes for example a decision tree for determining a list of actions and/or an action block for modifying the audio object stream by executing the list of actions on the audio object stream, or the like. 
     The circuitry may be configured to perform a field of listening evaluation on the audio object stream to estimate a position distraction level. For example, the field of listening of a user may be divided in regions, wherein each region may be associated with a different position distraction level. For performing the field of listening evaluation, the coordinates of audio objects, such as audio monopoles or the like, may be extracted to obtain audio object positions in the 3D space. The circuitry may be further configured to perform coordinate extraction to obtain coordinates of an audio object in the audio object stream, wherein the coordinates of an audio object may represent a field of listening.The field of listening evaluation may estimate the position distraction level based on extracted coordinates of an audio object in the audio object stream. 
     Additionally, or alternatively, the circuitry may be configured to perform a sound signature estimation on the audio object stream to estimate an aural distraction level. For performing the sound signature estimation, a bitstream is extracted from the audio object stream. The audio bitstream may be encoded according to e.g. the Waveform Audio File Format, WAV, or the like. For performing the sound signature estimation, a sound signature estimator analyzes the spectral and temporal characteristics of the audio objects, to estimate an aural distraction. 
     The sound signature estimation may also comprise performing a transient detection. Alternatively, sound signature estimation may also be performed by determining a normalized energy, or by using a neural network detector or the like. The neural network detector may be implemented by a neural network, such as a Deep Neural Network (DNN) or the like. 
     Additionally, or alternatively, the circuitry may be configured to perform a distance calculation on the audio object stream to obtain a distance estimation. A distance calculator may estimate a perceived distance. For example, in an in-vehicle scenario, the perceived distance may be a distance between a position of a driver (x,y,z) and a position of an audio object (x,y,z) of the audio object stream. A distance distraction level may be estimated based on the distance estimation. The distance calculation may be performed by extracting spatial, temporal and spectral characteristics while analyzing an audio object stream. The distance estimation may comprise a perceived distance, a perceived velocity vector, a cross-correlation, an auto-correlation related to an audio bitstream, and/or the like. 
     The circuitry may be further configured to extract coordinates and ab audio bitstream to obtain the perceived distance, the perceived velocity vector, the cross-correlation, and/or the auto-correlation. 
     The circuitry may be further configured to perform a driving situation analysis based on acquired vehicle data to estimate a driving situation. A driving distraction level may be estimated based on the estimated driving situation. The estimated driving situation may express the criticalness of the current driving situation by concerning different kind of vehicle data. For example, if the current driving situation is estimated as critical, the modified audio object stream may be an audio object stream with minimized distraction levels related to the driving situation in a car. The vehicle data may be data acquired by various sensors inside and outside a vehicle. The in-vehicle sensors may be, for example, a sensor array that comprises a plurality of sensors, each one arranged at a respective seat of the vehicle. The plurality of sensors may be any kind of sensors such as a pressure sensor capable of obtaining a respective presence of passengers/driver at the front and rear seats of the vehicle. The vehicle data may be collected, for example, from a cloud regarding traffic situation, traffic lights and the like. The sensors outside the vehicle may be Time-of-Flight sensors, ultrasonic sensors, radar device and the like. The vehicle data may be stored in a database and collected from the database. 
     The circuitry may be further configured to perform a song history analysis on a history of songs to estimate a novelty factor related to the audio object stream. The novelty factor which is related to the audio object stream may be estimated based on a history of songs, for example, by comparing the audio object stream and a history of songs which is e. g. stored in a database, for example a user’s playlist or the like. The novelty factor may express how familiar a driver is with an audio material, such as a song that is played-back, and thus, a novelty distraction level may be estimated based on the novelty factor. A user’s distraction may be higher for a new audio material than for an older audio material. In particular, the novelty factor depends on how often the user has heard the song, for example, for the user’s distraction may be higher for a song that the user has heard one or two times and lower for a song that the user has heard many times. Determining whether or not the audio object stream is new may for example be realized by comparing the novelty factor with a predefined threshold value, for example with a value 0.5, or the like. For example, if the estimated novel factor is more than 0.5, the song that is played-back is considered as new audio material, and thus, the user’s distraction level may be high. If the estimated novel factor is less than 0.5, the song that is played-back is considered that the user is familiar with that song, and thus, the user’s distraction level may be low. 
     In one embodiment, the circuitry may be further configured to perform distraction minimization in the audio object stream to obtain the modified audio object stream. The modified audio object stream may have a distraction minimization by which the distraction/stress that is caused by object-audio material is reduced. 
     In one embodiment, the circuitry may be further configured to output the modified audio object stream to a loudspeaker system. In particular, the circuitry may be further configured to reduce a distraction level of a driver based on the modified audio object stream outputted to a loudspeaker system of a vehicle. 
     The embodiments also disclose a method comprising estimating a distraction level of an audio object stream and modifying the audio object stream based on the estimated distraction level to obtain a modified audio object stream. 
     The embodiments also disclose a computer program comprising instructions, the instructions when executed on a processor causing the processor to estimate a distraction level of an audio object stream, and to modify the audio object stream based on the estimated distraction level to obtain a modified audio object stream. 
     Embodiments are now described by reference to the drawings. 
       FIG.  1    schematically illustrates an exemplary method in accordance with the present disclosure. An audio object stream  1  is input to a distraction level estimator  2 . The distraction level estimator  2  analyzes the audio object stream  1  to estimate a distraction level of the audio object stream  1 . The distraction level of the audio object stream  1  obtained by the distraction level estimator  2  is input to an audio object stream modification  3 . Based on the distraction level obtained by the distraction level estimator  2 , the audio object stream modification  3  modifies the audio object stream  1  to obtain a modified audio object stream  4 . 
     In an embodiment, the audio object stream  1  encodes audio using a 3D audio technique and thus describes a spatial sound scene by placing sound objects, which describe virtual sound sources, at certain sound object positions in space. For example, the audio object stream  1  may be encoded according to MPEG-H 3D Audio (ISO/IEC 23008-3, MPEG-H Part 3), Dolby Digital (AC-3), MP3, AAC, 360 Reality Audio, etc. The audio object stream  1  encodes audio as audio objects and describes a spatial sound scene by placing audio objects, which describe virtual sound sources, at a certain sound object position in space. 
     An exemplary process of the distraction level estimation  2  is described in more detail with regard to  FIGS.  2  to  6    below. An exemplary process of the audio object stream modification  3  is described in more detail with regard to  FIG.  2   a    below. 
       FIG.  2   a    illustrates an exemplary embodiment of a device implementing a process of a distraction minimization in an audio object stream such as described in  FIG.  1    above. An object-based audio material, such as audio object stream  1 , is analyzed by distraction level estimators (see  2  in  FIG.  1   ), namely a field-of-listening estimator  10 , a sound signature estimator  11 , and a distance calculator  12 . Each one of the field of listening estimator  10 , the sound signature estimator  11  and the distance calculator  12  estimates a respective distraction level, here a position distraction level, an aural distraction level and a distance estimation, respectively. The estimated distance is related with a distraction level, for example, the bigger the distance, the smaller the distraction level. Based on the estimated position distraction level, the aural distraction level and the distance estimation obtained by the field of listening estimator  10 , the sound signature estimator  11  and the distance calculator  12 , respectively, the decision tree  5  determines a list of actions. The list of actions output by the decision tree  5  is input to an action block  6  together with the audio object stream  1 . Based on the list of actions obtained from the decision tree  5  and based on the audio object stream  1 , the action block  6  produces a modified audio object stream  4 .In the present embodiment, there are three distraction level estimators, here the field of listening estimator  10 , the sound signature estimator  11 , and the distance calculator  12 , without limiting the present invention in that regard. The number of the distraction level estimators may be one, two, three or more. 
       FIG.  2   b    illustrates another exemplary embodiment of a device implementing a process of a distraction minimization in an audio object stream such as described in  FIG.  1    above. An object-based audio material, such as audio object stream  1 , is analyzed by distraction level estimators (see  2  in  FIG.  1   ), namely a field of listening estimator  10 , a sound signature estimator  11 , and a distance calculator  12 . Each one of the field-of-listening estimator  10 , the sound signature estimator  11  and the distance calculator  12  estimates a respective distraction level, here position distraction level, an aural distraction level and a distance estimation, respectively. In addition, vehicle data  13  is collected and input to a driving situation analyzer  14 , which analyzes and estimates the driving situation. A song history analyzer  17  estimates a novelty factor related to the audio object stream based on a history of songs  16 , e.g. by comparing the audio object stream  1  and the history of songs  16  stored in a database. Based on the estimated driving situation obtained by the driving situation analyzer  14 , based on the novelty factor obtained by the song history analyzer  17 , and based on the position distraction level, the aural distraction level and the distance estimation, obtained by the field-of-listening  10 , the sound signature estimator  11  and the distance calculator  12 , respectively, the decision tree  5  obtains a list of actions. The list of actions output by the decision tree  5  is input to an action block  6  together with the audio object stream  1 . Based on the list of actions obtained from the decision tree  5  and based on the audio object stream  1 , the action block  6  obtains a modified audio object stream  4 . The action block  6  thus performs a decision tree based audio object stream modification (see  3  in  FIG.  1   ). 
       FIG.  3    schematically describes in more detail an embodiment of a field-of-listening estimation as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b    above. An audio object stream  1  is analyzed by a coordinate extraction  19  to obtain coordinates (x, y, z)  20  of an audio object, such as for example an audio monopole, in the audio object stream  1 . The coordinates (x, y, z)  20  of the audio object represent a position of the audio object, and thus, a field of listening. Based on the coordinates (x, y, z)  20  of the audio object obtained by the coordinate extraction  19 , a field of listening evaluation, which acts as a field of listening estimator  10  (see  FIGS.  2   a  and  2   b   ) estimates a position distraction level  21  of the audio object in the audio object stream  1 . The position distraction level  21  is described in more detail in  FIG.  4   , in the following. 
     As stated with regard to  FIG.  1    above, the audio object stream  1  of the embodiment of  FIG.  3    encodes audio using a 3d audio technique. The audio object stream  1  thus encodes audio as audio objects and describes a spatial sound scene by placing audio objects, which describe virtual sound sources at a certain sound object position in space. 
     As stated in the introductory part of the description, from a data coding point of view, audio objects consist of audio data which is comprised in the audio object stream as an audio bitstream plus associated metadata (object position, gain, etc.). The associated metadata related to audio objects for example comprises positioning information related to the audio objects, i.e. information describing where an audio object should be position in the 3D audio scene. This positioning information may for example be expressed as 3d coordinates (x, y, z) of the audio object (see  20  in  FIG.  3   ). According to the embodiment of  FIG.  3   , the coordinate extraction  19  obtains the coordinates (x, y, z) of the audio objects within the audio object stream. These extracted coordinates (x, y, z) of the audio objects represent the field of listening in which the driver is immersed. 
     Audio objects streams are typically described by a structure of a metadata model that allows the format and content of audio files to be reliably described. In the following embodiment, it is described as an example of a metadata model, the Audio Definition Model (ADM) specified in ITU Recommendation ITU-R BS.2076-1 Audio Definition Model. This Audio Definition Model specifies how XML metadata can be generated to provide the definitions of audio objects. 
     As described in ITU-R BS.2076-1, an audio object stream is described by an audio stream format, such as audioChannelFormat including a typeDefinition attribute, which is used to define what the type of a channel is. ITU-R BS.2076-1 defines five types for channels, namely DirectSpeakers, Matrix, Objects, HOA, and Binaural, as described on Table 10 of ITU-R BS.2076-1, which we reproduce below:  
     
       
         
          TABLE 10
           
               
               
               
             
               
                 typeDefinitions 
               
               
                 typeDefinition 
                 typeLabel 
                 Description 
               
             
            
               
                 DirectSpeakers 
                 0001 
                 For channel-based audio, where each channel feeds a speaker directly 
               
               
                 Matrix 
                 0002 
                 For channel-based audio where channels are matrixed together, such as Mid-Side, Lt/Rt 
               
               
                 Objects 
                 0003 
                 For object-based audio where channels represent audio objects (or parts of objects) and so include positional information 
               
               
                 HOA 
                 0004 
                 For scene-based audio where Ambisonics and HOA are used 
               
               
                 Binaural 
                 0005 
                 For binaural audio, where playback is over headphones 
               
            
           
         
       
     
     In this embodiment, it is focused on type definition “Objects” which are described in section  5.4.3.3 of ITU-R BS.2076-1. In this section of ITU-R BS.2076-1 it is described that object-based audio comprises parameters that describe a position of the audio object (which may change dynamically), as well as the object’s size, and whether it is a diffuse or coherent sound. The position and object size parameters definitions depend upon the coordinate system used and they are individually described in Tables 14, 15 and 16, of the ITU Recommendation ITU-R BS.2076-1 Audio Definition Model. 
     The position of the audio object is described in a sub-element “position” of the audioBlockFormat for “Objects”. ITU-R BS.2076-1 provides two alternative ways of describing the position of an audio object, namely in the Polar coordinate system, and, alternatively, in the Cartesian coordinate system. A coordinate sub-element “cartesian” is defined in Table  16  of ITU-R BS.2076-1 with value 0 or 1. This coordinate parameter specifies which of these types of coordinate systems is used.  
     
       
         
          TABLE 16
           
               
               
               
               
               
               
               
             
               
                 audioBlockFormat sub-elements for Objects 
               
               
                 Sub-element 
                 Attribute 
                 Description 
                 Units 
                 Example 
                 Quantity 
                 Default 
               
             
            
               
                 cartesian 
                   
                 Specifies coordinate system, if the flag is set to 1 the Cartesian coordinate system is used, otherwise spherical coordinates are used. 
                 1/0 flag 
                 1 
                 0 or 1 
                 0 
               
               
                 gain 
                   
                 Apply a gain to the audio in the object 
                 linear gain value 
                 0.5 
                 0 or 1 
                 1.0 
               
               
                 diffuse 
                   
                 Describes the diffuseness of an audioObject (if it is diffuse or direct sound) 
                 0.0 to 1.0 
                 0.5 
                 0 or 1 
                 0 
               
            
           
         
       
     
     If the “cartesian” parameter is zero (which is the default), a Polar Coordinate system is used. Thus, the primary coordinate system defined in ITU-R BS.2076-1 is the Polar coordinate system, which uses azimuth, elevation and distance parameters as defined in Table 14 of ITU-R BS.2076-1, which is reproduced below:  
     
       
         
          TABLE 14
           
               
               
               
               
               
               
               
             
               
                 audioBlockFormat sub-elements for Objects (polar) 
               
               
                 Sub-element 
                 Attribute 
                 Description 
                 Units 
                 Example 
                 Quantity 
                 Default 
               
             
            
               
                 position 
                 coordinate= “azimuth” 
                 azimuth “theta” of sound location 
                 Degrees (-180 ≤ theta ≤ 180) 
                 -22.5 
                 1 
                   
               
               
                 position 
                 coordinate= “elevation” 
                 elevation “phi” of sound location 
                 Degrees (-90 ≤ phi ≤ 90) 
                 5.0 
                 1 
                   
               
               
                 position 
                 coordinate= “distance” 
                 distance “r” from origin 
                 abs(r) 
                 0.9 
                 0 or 1 
                 1.0 
               
               
                 width 
                   
                 horizontal extent 
                 Degrees 
                 45 
                 0 or 1 
                 0.0 
               
               
                 height 
                   
                 vertical extent 
                 Degrees 
                 20 
                 0 or 1 
                 0.0 
               
               
                 depth 
                   
                 distance extent 
                 Ratio 
                 0.2 
                 0 or 1 
                 0.0 
               
            
           
         
       
     
     Alternatively, it is possible to specify the position of an audio object in the Cartesian coordinate system. For a Cartesian coordinate system, the position values (X, Y and Z) and the size values are normalized to a cube:  
     
       
         
          TABLE  15 

           
               
               
               
               
               
               
               
             
               
                 audioBlockFormat sub-elements for Objects (Cartesian) 
               
               
                 Sub-element 
                 Attribute 
                 Description 
                 Units 
                 Example 
                 Quantity 
                 Default 
               
             
            
               
                 position 
                 coordinate=“X” 
                 left/right dimension 
                 Normalized Units 
                 -0.2 
                 1 
                   
               
               
                 position 
                 coordinate=“Y” 
                 back/front dimension 
                 Normalized Units 
                 0.1 
                 1 
                   
               
               
                 position 
                 coordinate=“Z” 
                 bottom/top dimension 
                 Normalized Units 
                 -0.5 
                 0 or 1 
                 0.0 
               
               
                 width 
                   
                 X-width 
                 Normalized Units 
                 0.03 
                 0 or 1 
                 0.0 
               
               
                 depth 
                   
                 Y-width 
                 Normalized Units 
                 0.05 
                 0 or 1 
                 0.0 
               
               
                 height 
                   
                 Z-width 
                 Normalized Units 
                 0.07 
                 0 or 1 
                 0.0 
               
            
           
         
       
     
     A sample XML code which illustrates the position coordinates (x,y,z) is given in section 5.4.3.3.1 of ITU-R BS.2076-1 by  
     
       
         
           
               
            
               
                 &lt;audioBlockFormat . . . &gt; 
               
               
                         &lt;position coordinate=“azimuth” &gt;-22.5&lt;/position&gt; 
               
               
                         &lt;position coordinate=“elevation”&gt;-5.0&lt;/position&gt; 
               
               
                         &lt;position coordinate=“distance”&gt;-0.9&lt;/position&gt; 
               
               
                         &lt;depth&gt;0.2&lt;/depth&gt; 
               
               
                 &lt;/audioBlockFormat&gt; 
               
            
           
         
       
     
     Based on the description of ITU-R BS.2076-1 audio definition model described above in more detail, the coordinate extraction process described with regard to  FIG.  3    above (see reference sign  19  in  FIG.  3   ) may for example be realized by reading these coordinate attributes (x, y, z) or (azimuth, elevation, distance) from the position sub-element of an audioBlockFormat definition included in the metadata of the audio object stream. The set of positions of audio objects obtained in this way defines a field of listening, which is evaluated in order to determine a position distraction level as described in more details below. 
       FIG.  4    visualizes how the position distraction level of an audio object stream is related to the position of a driver in an in-vehicle-scenario. A field of listening of the driver  25  is divided into four regions, which are related with the position of the driver  25 . Each one of the four regions, here R front,  R left , R right , and R rear , is associated with a predetermined position distraction level, here the field of view without head turn (R front ), the field of view after a right head turn (R right ), the field of view after a left head turn (R left ) and the field of view otherwise (R rear ), for example, after a 100° head turn. The position distraction level is equal to 0, when the driver  25  is looking straight to the direction of the vehicle without turning his head, e.g. looking in the front. The position distraction level is equal to 0.5, when the driver  25  is turning his head right/left, for example, after a 90° head turn, and the position distraction level = 1, otherwise. Thus, as described, inside the field of view of the driver  25  without head turn, the distraction level is small and otherwise is large. 
     The relationship between the position distraction level and the regions in the field of listening, being associated with the position of the driver  25 , is given by 
     
       
         
           
             d 
             
               
                 
                   
                     x 
                     , 
                     y 
                     , 
                     z 
                   
                 
               
               i 
             
             = 
             
               
                 
                   
                     
                       
                           
                           
                           
                         0 
                           
                         i 
                         f 
                           
                         
                           
                             
                               
                                 x 
                                 , 
                                 y 
                                 , 
                                 z 
                               
                             
                           
                           i 
                         
                         ∈ 
                         
                           R 
                           
                             f 
                             r 
                             o 
                             n 
                             t 
                           
                         
                       
                     
                   
                   
                     
                       
                         0.5 
                           
                         i 
                         f 
                           
                         
                           
                             
                               
                                 x 
                                 , 
                                 y 
                                 , 
                                 z 
                               
                             
                           
                           i 
                         
                         ∈ 
                         
                           R 
                           
                             l 
                             e 
                             f 
                             t 
                           
                         
                           
                         o 
                         r 
                           
                         
                           
                             
                               
                                 x 
                                 , 
                                 y 
                                 , 
                                 z 
                               
                             
                           
                           i 
                         
                         ∈ 
                         
                           R 
                           
                             r 
                             i 
                             g 
                             h 
                             t 
                           
                         
                       
                     
                   
                   
                     
                       
                           
                           
                           
                           
                         1 
                           
                         i 
                         f 
                           
                         
                           
                             
                               
                                 x 
                                 , 
                                 y 
                                 , 
                                 z 
                               
                             
                           
                           i 
                         
                         ∈ 
                         
                           R 
                           
                             r 
                             e 
                             a 
                             r 
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
      where R front , R left , R right , and R rear  are the regions in the field of listening. 
     As described in  FIG.  3    above, the coordinates (x, y, z)  20  of the audio object represent a position of the audio object, and thus, a field of listening. A distraction level of a field of listening is computed by summing the position distraction level of an audio object over all audio objects in an audio object stream. The distraction level of the field of listening obtained by the field of listening evaluation  10  is given by 
     
       
         
           
             D 
             = 
             
               
                 ∑ 
                 
                   i 
                   ∈ 
                   S 
                 
               
               
                 d 
                 
                   
                     
                       
                         x 
                         , 
                         y 
                         , 
                         z 
                       
                     
                   
                   i 
                 
               
             
           
         
       
     
      where  
     
       
         
           S 
         
       
     
     is a set of all audio objects in an audio stream which define the field of listening, (x, y, z) i  is the position of an audio object i, d(x, y, z) i  is the distraction level of an audio object i, and D is the position distraction level of field of listening. This position distraction level D is then evaluated decision tree (5 in  FIG.  2   b   ) to obtain a list of actions which modify the audio object stream such that it is less distracting with respect to the field of the listening, as it is described in more detail with regard to  FIGS.  11   a  and  11   b    below. 
     In addition, or alternatively to the position distraction level (see  10  in  FIGS.  2   a , or  2   b   ) described above in more detail, an aural distraction level (see  11  in  FIGS.  2   a , or  2   b   ) may be determined as described in  FIGS.  2   a , or  2   b    above. 
     Additionally, all distraction levels, such as position distraction level (see  10  in  FIGS.  2   a , or  2   b   ), aural distraction level (see  11  in  FIGS.  2   a , or  2   b   ) and the like, are computed for a single audio stream and the action that is taken influence one audio object. For example, a song includes many audio objects, however, nor all of them are distracting. Only a few may be distracting and only these may be altered based on the list of actions to obtain the modified audio stream. 
       FIG.  5   a    schematically describes in more detail an embodiment of a sound signature estimation as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b    above. An audio object stream  1  is analyzed by a bitstream extraction  29  to obtain a WAV, such as an audio bitstream  30  (encoded according to e.g. the Waveform Audio File Format, WAV). The audio bitstream  30  represents spectral and temporal characteristics of an audio object e.g. of an audio monopole, in the audio object stream  1 . Based on the audio bitstream  30  of the audio object stream  1 , the sound signature estimator  11  estimates an aural distraction level  31  related to the audio bitstream. 
     The sound signature estimator  11  determines characteristics of the audio bitstream which have an influence on the distraction level the sound encoded in the audio bitstream exerts on a driver. It may for example output a high value of the aural distraction level for abrupt spectral/dynamic changes (e.g., impulsive sounds) or for voices/human speech based on the estimated aural distraction level  31 . There are several possibilities for realizing a sound signature estimation of a waveform x(n) encoded by the audio bitstream of length N, for example using a transient detector, an energy detector, a neural-network detector, and the like. 
     In the case of a transient detector, transients, which are portions of audio signals that evolve fast and unpredictably over a short time period, are detected. A quantity that describes transients may for example be obtained by comparing characteristics of an audio signal such as short-term energy and long-term energy. 
     For example, performing a transient detection may comprise a computation of a ratio τ 1  between short-term energy and long-term energy according to: 
     
       
         
           
             
               τ 
               1 
             
             = 
             
               
                 
                   1 
                   
                     2 
                     
                       M 
                       1 
                     
                     + 
                     1 
                   
                 
                 
                   ∑ 
                   
                     
                         
                       
                         m 
                         = 
                         − 
                         
                           M 
                           1 
                         
                       
                       
                         
                           M 
                           1 
                         
                       
                     
                     x 
                     
                       
                         
                           
                             n 
                             + 
                             m 
                           
                         
                       
                       2 
                     
                   
                 
               
               
                 
                   1 
                   
                     2 
                     
                       M 
                       2 
                     
                     + 
                     1 
                   
                 
                 
                   ∑ 
                   
                     
                         
                       
                         m 
                         = 
                         − 
                         
                           M 
                           2 
                         
                       
                       
                         
                           M 
                           2 
                         
                       
                     
                     x 
                     
                       
                         
                           
                             n 
                             + 
                             m 
                           
                         
                       
                       2 
                     
                   
                 
               
             
           
         
       
     
      where x(n) is the audio signal encoded in the audio bitstream, [-M 1 , M 1 ] is a first time window in which the short-time energy is calculated and [-M 2 , M 2 ] is a second time window in which the long-time energy is calculated, with M 1 &lt;M 2 , and where m is an index which runs over the audio samples in the respective time windows in which the long-time energy and the short-time energy is calculated. 
     A transient may for example be detected if this ratio τ 1  is large which may result in distractions (“impulsive sound”) being caused by the audio signal to which the calculated ratio is related. Determining whether or not the ratio τ 1  is large may for example be realized by comparing the ratio τ 1  with a predefined threshold value γ. For example, τ 1 , ≥ γ yields an aural distraction level of “1.0” whereas τ 1 , &lt; γ yields an aural distraction level of “0.0”. A possible value for γ is γ = 4.0. As an alternative to comparing τ 1  with a threshold value, one could also use the ratio τ 1  itself as measure of the distraction level. For example, one can use τ 1 , itself as a soft value that describes the transient level. In order to have a value in the range 0 to 1, a squashing function like the ‘tanh’ function may be used which maps the value range [0, ∞] to [0,1]. 
     As stated above, there are other possibilities for realizing a sound signature estimation of a waveform x(n) encoded by the audio bitstream of length N. In addition, or as an alternative to the transient detector described above, also an energy detector may be used for performing a sound signature estimation. 
     An energy detector may for example be realized by determining the normalized energy τ 2  =  
     
       
         
           
             
               1 
               N 
             
             
               ∑ 
               
                 
                     
                   
                     n 
                     = 
                     1 
                   
                   N 
                 
               
             
             x 
             
               
                 
                   n 
                 
               
               2 
             
             , 
           
         
       
     
     which is used as distraction level (high energy τ 2  ≈  1 ) means a lot of distraction). 
     In addition, or alternatively, a Neural-network detector may be realized by collecting, as a first step, human labels for the perceived distraction of sounds (on a scale from 0 to 1). A neural network DNN is then trained based on this collected data, such that a distraction level to be estimated based on previously unseen waveforms. I.e., the neural network DNN maps samples x(1), ..., x(N) of an audio window of length N onto a distraction level τ 3 : 
     
       
         
           
             
               τ 
               3 
             
             = 
             D 
             N 
             N 
             
               
                 x 
                 
                   1 
                 
                 , 
                 … 
                 , 
                 x 
                 
                   N 
                 
               
             
             . 
           
         
       
     
     As stated above, the audio bitstream  30  represents spectral and temporal characteristics of an audio object e.g. of an audio monopole, in the audio object stream  1 . Based on the audio bitstream  30  of the audio object stream  1 , the sound signature estimator  11  estimates an aural distraction level  31  related to the audio bitstream. 
     The spectral characteristics of the audio bitstream may for example be obtained by computing a discrete Fourier transformation of each audio window of an audio object stream. That is, each audio window is converted into a respective short-term power spectrum P f (n) using the Fourier transformation, also known as power spectral density, may be obtained by 
     
       
         
           
             
               P 
               f 
             
             
               n 
             
             = 
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       0 
                     
                     
                       N 
                       − 
                       1 
                     
                   
                   
                     
                       X 
                       n 
                     
                     
                       i 
                     
                     
                       e 
                       
                         − 
                         
                           
                             j 
                             2 
                             π 
                             f 
                             i 
                           
                           N 
                         
                       
                     
                   
                 
               
             
           
         
       
     
      where X n (i) is the signal in each audio window X n  of an audio object stream, f are the frequencies in the frequency domain, P f (n) are the components of the short-term power spectrum P(n) and N is the numbers of samples in an audio window X n . 
     The signal in each audio window X n  of an audio object stream can be obtained by 
     
       
         
           
             
               X 
               n 
             
             
               i 
             
             = 
             x 
             
               
                 n 
                 + 
                 i 
               
             
             h 
             
               i 
             
           
         
       
     
      where x(n + i) represents the discretized audio object signal (i representing the sample number and thus time) shifted by n samples. h(i) is a windowing function around time n (respectively sample n), like for example the hamming function, which is well-known to the skilled person. 
     For example, a spectral flatness detection may be used in the sound signature estimation  11  of  FIG.  5   a    to detect noise based on the power spectrum P f (n). Existing noise in an audio object stream may be distracting for the driver. The sound signature estimation  11  may thus estimate the aural distraction level  31  based on a level of noise detected in the audio object stream  1  by the spectral flatness detection. The spectral flatness F may for example be measured in decibels, and may be used here to characterize the audio bitstream of audio object stream  1 , and to thus quantify how tone-like a sound is, as opposed to being noise-like. 
     The spectral flatness F may for example be calculated by dividing the geometric mean of the power spectrum by the arithmetic mean of the power spectrum P f (n), i.e.: 
     
       
         
           
             F 
             = 
             
               
                 
                   
                     
                       
                         ∏ 
                         
                           f 
                           = 
                           0 
                         
                         
                           N 
                           − 
                           1 
                         
                       
                       
                         
                           P 
                           f 
                         
                         
                           n 
                         
                       
                     
                   
                   N 
                 
               
               
                 
                   
                     
                       
                         ∑ 
                         
                           f 
                           = 
                           0 
                         
                         
                           N 
                           − 
                           1 
                         
                       
                       
                         
                           P 
                           f 
                         
                         
                           n 
                         
                       
                     
                   
                   N 
                 
               
             
             = 
             
               
                 e 
                 x 
                 p 
                 
                   
                     
                       1 
                       N 
                     
                     
                       
                         ∑ 
                         
                           f 
                           = 
                           0 
                         
                         
                           N 
                           − 
                           1 
                         
                       
                       
                         ln 
                         
                           P 
                           f 
                         
                         
                           n 
                         
                       
                     
                   
                 
               
               
                 
                   1 
                   N 
                 
                 
                   
                     ∑ 
                     
                       f 
                       = 
                       0 
                     
                     
                       N 
                       − 
                       1 
                     
                   
                   
                     
                       P 
                       f 
                     
                     
                       n 
                     
                   
                 
               
             
           
         
       
     
      where P f (n) represents the magnitude of bin number n. 
     A high spectral flatness F (approaching 1.0 for white noise) may indicate that the spectrum has a similar amount of power in all spectral bands - similar to white noise, and the graph of the spectrum would appear relatively flat and smooth. A low spectral flatness F (approaching 0.0 for a pure tone) indicates that the spectral power is concentrated in a relatively small number of bands - like a mixture of sine waves, and the spectrum may appear “spiky”, e.g. having many peaks. That is, the spectral flatness F can be directly used to express the aural distraction level  31 , the spectral flatness F is high for noise-like signals which are disturbing to the driver. In other words, the spectral flatness detector may “look” in the power spectrum P f (n) of the audio object stream  1  and to determine whether or not a noise exists and its level. For example, the more noise detected in the audio object stream, the higher the distraction level, here the aural distraction level  31 , in  FIG.  5   a   . 
     The ratio F produced by this calculation may be converted to a decibel scale for reporting, with a maximum of 0 dB and a minimum of -∞ dB. The spectral flatness F may also be measured within a specified sub band, rather than across the whole band. A single (or more) empty bin may result to a flatness F of 0. 
       FIG.  5   b    schematically describes an embodiment of a process for determining a distraction level, such as the aural distraction level  31  of  FIG.  5   a    and the position distraction level  21  of  FIG.  3   , based on the power spectrum P f (n), as computed above under the reference of  FIG.  5   a   . 
     For example, a voice activity detection may be used in the sound signature estimation  11  of  FIG.  5   a    to detect human speech based on the power spectrum P f (n)  32 . Human speech may be distracting for the driver, depending on the position of a person utters the speech relatively to the position of the driver. The sound signature estimation  11  may thus estimate the aural distraction level  31  based on a speech detection  35  by voice activity detection. 
     In the present embodiment, the power spectrum P f (n)  32  of the audio object stream  1  is used to perform MFCC(n) computation  33  to obtain time-varying coefficients, such as Mel-scale filterbank cepstral coefficients MFCC(n)  34  for each audio window. That is, the Mel-scale filterbank cepstral coefficients MFCC(n) may be obtained by 
     
       
         
           
             M 
             F 
             C 
             C 
             
               n 
             
               
             = 
               
             D 
             C 
             T 
             
               
                 l 
                 o 
                 g 
                 
                   
                     M 
                     ⋅ 
                     P 
                     
                       n 
                     
                       
                   
                 
                   
               
             
           
         
       
     
      where P(n) is a vector of P f (n) values, which is the short-term power spectrum for a windowed frame n (around a respective time instant) as obtained by the Discrete Fourier Transformation, M is a matrix having filters of a Mel-filterbank as rows and DCT is the Discrete Cosine transform matrix. 
     Subsequently, speech detection may be performed by analyzing the MFCC(n)  34 , as also described by Ben Milner and Xu Shao in “Speech Reconstruction From Mel-Frequency Cepstral Coefficients Using a Source-Filter Model”, wherein, index n, may represent a time scale. The Mel-scale filterbank cepstral coefficients MFCC(n)  34  obtained by this process may represent characteristic feature vectors of the audio object stream  1  in each audio window. If speech detected  35 , the aural distraction level  31  is estimated. If speech is not detected, the process ends  36 , e.g. “Do nothing”. 
     In the present embodiment, the aural distraction level  31  comprises the position distraction level  21  obtained by the field-of-listening estimation  10  of  FIG.  3   . The position distraction level  21  is estimated as described under the reference of  FIG.  3   . Therefore, the aural distraction level  31  including the position distraction level  21  of the recognized human speech is estimated. For example, if the voice is coming, e.g., from behind the driver, as described in  FIGS.  3  and  4    above, it is determined that a human speech has a high distortion level, such as high aural distraction level  31  comprising high position distraction level  21 , regarding the position distraction level of an audio object. Voice activity detection may be performed in the frequency domain. 
     In addition, or alternatively to the position distraction level (see  10  in  FIGS.  2   a , or  2   b   ) and the aural distraction level (see  11  in  FIGS.  2   a , or  2   b   ) described above in more detail, also a distance estimation may be performed as described in  FIGS.  2   a , or  2   b    above (see  12  in  FIGS.  2   a , or  2   b   ). 
       FIG.  6    schematically describes in more detail an embodiment of a distance calculation as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b    above. An audio object stream  1  is analyzed by a coordinate and bitstream extraction  39  to obtain coordinates (x, y, z)  20  of audio objects, such as for example monopoles, together with an audio bitstream  30  (encoded according to e.g. the Waveform Audio File Format, WAV), which represents spectral and temporal characteristics of the audio object in the audio object stream  1 . Based on the coordinates (x, y, z) and the bitstream (waveform), a distance calculator  12  estimates a perceived distance  40 , a perceived velocity vector  41 , a cross-correlation  42 , and an auto-correlation  43  related to the audio bitstream. 
     In the present embodiment, the distance calculator  12  determines a distance estimation, and thus, a distance distraction level is estimated based on the distance estimation. 
     For example, in an in-vehicle scenario, the perceived distance  40  is the distance between a position of a driver (x, y, z) driver  and a position of an audio object (x, y, z) audio  of the audio object stream. The position of the driver (x, y, z) driver  may be detected by in-vehicle sensors, for example, by a sensor array that comprises a plurality of sensors, each one arranged at a respective seat of the vehicle. The plurality of sensors may be any kind of sensors such as a pressure sensor capable of obtaining a respective presence of passengers/driver at the front and rear seats of the vehicle. The position of the audio objects (x, y, z) audio  is estimated by the extracted coordinates (x, y, z) as described in more detail in the embodiments of  FIGS.  3  and  6    above. The perceived distance  40  is calculated by computing the difference between the position of the driver (x, y, z) driver  and the position of the audio objects (x, y, z) audio , given by 
     
       
         
           
             d 
               
             = 
               
             
               
                 r 
                 → 
               
             
               
             = 
               
             
               
                 
                   
                     
                       
                         x 
                         , 
                           
                         y 
                         , 
                           
                         z 
                       
                     
                   
                   
                     a 
                     u 
                     d 
                     i 
                     o 
                   
                 
                 − 
                 
                   
                     
                       
                         x 
                         , 
                           
                         y 
                         , 
                         z 
                       
                     
                   
                   
                     d 
                     r 
                     i 
                     v 
                     e 
                     r 
                   
                 
               
             
           
         
       
     
     In addition, the perceived velocity vector  41  is calculated by computing the derivative of the position, here the perceived distance  40 , with respect to time: 
     
       
         
           
             
               v 
               → 
             
             = 
             
               d 
               
                 d 
                 t 
               
             
             
               r 
               → 
             
             = 
             
               d 
               
                 d 
                 t 
               
             
             
               
                 
                   
                     
                       
                         x 
                         , 
                         y 
                         , 
                         z 
                       
                     
                   
                   
                     a 
                     u 
                     d 
                     i 
                     o 
                   
                 
                 − 
                 
                   
                     
                       
                         x 
                         , 
                         y 
                         , 
                         z 
                       
                     
                   
                   
                     d 
                     r 
                     i 
                     v 
                     e 
                     r 
                   
                 
               
             
           
         
       
     
     Additionally, the cross-correlation  42  may be calculated by an inter-object cross-correlation coefficient (IOCC) as follows 
     
       
         
           
             I 
             O 
             C 
             C 
             
               τ 
             
             = 
             
               
                 
                   
                     
                       ∫ 
                       
                         − 
                         ∞ 
                       
                       
                         + 
                         ∞ 
                       
                     
                     
                       
                         s 
                         i 
                       
                       
                         
                           t 
                           − 
                           τ 
                         
                       
                       
                         s 
                         j 
                       
                       
                         τ 
                       
                       d 
                       t 
                     
                   
                 
               
               
                 
                   
                     
                       
                         
                           ∫ 
                           
                             − 
                             ∞ 
                           
                           
                             + 
                             ∞ 
                           
                         
                         
                           
                             s 
                             i 
                             2 
                           
                           d 
                           t 
                           
                             
                               
                                 ∫ 
                                 
                                   − 
                                   ∞ 
                                 
                                 
                                   + 
                                   ∞ 
                                 
                               
                               
                                 
                                   s 
                                   j 
                                   2 
                                 
                                 d 
                                 t 
                               
                             
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
      where s i (t), s j (t) are the audio object signal of the audio bitstream. 
     The normalized cross-correlation function is bounded between -1 and +1, wherein a cross-correlation coefficient of +1 indicates that s i (t), s j (t) are coherent, e.g. identical, signals, a cross-correlation coefficient of -1 indicates that s i (t), s j (t) are coherent, e.g. identical, signals, with a phase shift of 180°, and a cross-correlation coefficient of 0 indicates that s i (t), s j (t) are incoherent signals. Intermediate values may indicate partial coherence or incoherence between the s i (t), s j (t) signals. 
     Optionally, in order to compute the perceived distance, the reverb level (intra-channel) may be estimated based on an inter-channel correlation. The inter-channel correlation as computed above may be used to see whether audio objects are correlated. In an audio object stream, one “source” like the vocals may be represented by several audio objects, where one audio object represents the direct path and the other audio objects represent the reflections. Thus, it is possible to determine the perceived distance of the audio object. 
       FIG.  7    schematically describes in more detail an embodiment of a process of a driving situation analyzer as performed in the process of distraction minimization in an audio object stream described in  FIG.  2   b    above. Based on vehicle data  13 , a driving situation analyzer  14  analyzes the vehicle data  13  to obtain a driving situation  45 . The driving situation analyzer  14  estimates the criticalness of the current driving situation  45  by considering different kind of vehicle data, such as time of day, driving speed, other driving related parameters and the like. In the present embodiment, a driving distraction level is estimated based on the driving situation ( 45 ). 
     The vehicle data  13  are collected for example, from a cloud regarding traffic situation, traffic lights and the like, or acquired for example by vehicle sensors, inside and outside the vehicle, such as Time-of-Flight sensors, ultrasonic sensors, radar device and the like. The vehicle data  13  may be stored in a database and collected from the database. 
       FIG.  8    schematically describes in more detail an embodiment of a process of novelty factor estimation as performed in the process of distraction minimization in an audio object stream described in  FIG.  2   b    above. Based on a history of songs  16  and an audio object stream  1 , a song history analyzer  17  estimates a novelty factor  46 . The novelty factor, which is related to the audio object stream  1 , is estimated based on a history of songs  16 , e.g. by comparing the audio object stream  1  and the history of songs  16  stored in a database, for example a user’s playlist. The novelty factor  46  expresses how familiar a driver is with an audio material, such as a song that is played-back, and that user’s distraction is higher for a new audio material than for an already known audio material. Determining whether or not the audio object stream is new may for example be realized by comparing the novelty factor  46  with a predefined threshold value, for example with a value 0.5. In the present embodiment, a novelty distraction level is estimated based on the novelty factor ( 46 ). 
       FIG.  9    schematically describes in more detail an embodiment of a process of audio source and coordinate extraction as performed in the process of distraction minimization in an audio object stream. Based on an audio object stream  1 , an audio source and coordinate extraction  46  obtains audio sources  1 , ..., N and coordinates (x, y, z) 1,...,N  of the audio objects in the audio object stream  1 . Each one of the audio sources  1 , ..., N and coordinates (x, y, z) 1,...,N , here audio source_1, (x, y, z), 47-1, ..., audio source_1, (x, y, z) N  47-N are input to an action block  6 . 
       FIG.  10    schematically describes in more detail an embodiment of determining a list of actions by a decision tree as performed in the process of distraction minimization in an audio object stream described in  FIGS.  2   a  and  2   b    above. A decision tree  5  determines a list of actions, such as countermeasures, here an amplitude reduction  48  of the audio-object, a low-pass/median filtering  49  or any other kind of filter, which reduces the harshness of a sound, and a modification of position  50 . The list of actions is input in an action block  6 . In the present embodiment, the list of actions contains three actions, without limiting the scope of protection in that regard. The list of actions may contain any number of actions, less, equal, or more than three actions. The list of actions may contain any number of actions, for example, the list of actions may contain one action or more than one actions. Additionally, the list of actions may be applied to one or any number of audio objects in the stream. 
     As mentioned in  FIGS.  2   a  and  2   b   , also based on the estimated distraction levels (see also  FIGS.  3 ,  5   a  and  6   ), the driving situation criticalness (see also  FIG.  7   ) and the novelty factor (see also  FIG.  8   ), the decision tree  5  determines as a final decision, the list of actions. The list of actions is used to alter the playback of the audio-object stream. In addition, during the modification of position  50 , the (x, y, z) coordinates of the audio-object are altered. For example, all sources which are outside of the field of view are warped to the front, which reduces the perceived distraction. 
     As described, in the present embodiment an amplitude reduction  48  of the audio-object is performed. Alternatively, abrupt dynamic changes are smoothened by slowly blending between different amplitudes. 
       FIG.  11   a    illustrates a process of the embodiment described in  FIG.  2   a   , implemented by a decision tree and an action block. In this implementation, a decision tree  5  determines a list of actions by merging different distraction levels and inputting the list of actions to an action block  6 . In the present embodiment, an audio object stream is analyzed, and, at  51 , a position distraction level is estimated (see  FIG.  3   ). If the position distraction level is less than 0.5, the method proceeds at  52 . If the position distraction level is more than 0.5, the method proceeds at  57 . At  52 , an aural distraction level is estimated (see  FIG.  5   a   ). If the aural distraction level is less than 0.5, the method proceeds at  53 . If the aural distraction level is more than 0.5, the method proceeds at  57 . At  53 , a perceived distance is estimated based on distance calculation (see  FIG.  6   ). If the perceived distance is small (here “close?”), for example if the source is inside the car, the method proceeds at  54 . If the perceived distance is not small, for example if the source is outside the car, the method proceeds at  57 . At  54 , a transient level τ 1  is computed. If the transient level τ 1  is high, for example, if τ 1 =  4 , the method proceeds at  55 . If the transient level τ 1  is not high, the method proceeds at  56  (see ratio τ 1 , in the description of  FIG.  5   a   ). At  55 , a low-pass filtering (see  49  in  FIG.  10   ) is performed based on a low-pass filter waveform, which reduces the harshness of a sound. At  56 , the volume is reduced, for example, by reducing the amplitude of the audio-object (see  48  in  FIG.  10   ). At  57 , the method ends, e.g. “Do nothing”. 
     In the present embodiment, if the transient level τ 1 , is not high, the list of actions, obtained for each set of distraction levels, here position distraction level, aural distraction level, and distraction level based on distance estimation, is one list of actions which contains one action, here volume reduction performed at  56 . Also, if the transient level τ 1  is high, the list of actions, obtained for each set of distraction levels, here position distraction level, aural distraction level, and distraction level based on distance estimation, is one list of actions which contains one action, here low-pass filtering performed at  55 . Alternatively, the list of actions, obtained for each set of distraction levels, is one list of actions which may contain any number of actions, for example, the list of actions may contain one action or more than one actions. 
     In the present embodiment, in the case where the computed transient level τ 1  is not high, the volume of the audio object stream is reduced, for example, by scaling the sample by a predetermined value. If the predetermined value, which is a gain factor, is, for example, G, then the modified audio object stream is given by 
     
       
         
           
             
               x 
               ′ 
             
             
               n 
             
             = 
             G 
             ∗ 
             x 
             
               n 
             
           
         
       
     
      where x′(n) is the modified audio object stream, x(n) is the audio object stream, and G is the scaling factor. 
     For example, if the predetermined value, which is the gain factor, is G = 0.5, the volume of the audio object stream is reduced by 6 dB, and if the gain factor is G = 0.25, the volume of the audio object stream is reduced by 12 dB. The above described values of the gain factor are exemplary values, without limiting the scope of protection in that regard. 
     In the present embodiment, in the case where the computed transient level τ 1  is high, a low-pass filtering is performed to reduce the harshness of a sound. That is, a filter that passes signals with a frequency lower than a selected cutoff frequency and eliminates all frequencies above the cutoff frequency. Determining which frequency is the low-pass filter threshold may for example be realized by comparing the frequency with a predefined threshold value, for example the threshold value may be a cut-off frequency f c  = 4 kHz, without limiting the scope of protection in that regard. 
     The low-pass filter is given by 
     
       
         
           
             
               
                 
                   x 
                   ′ 
                 
                 
                   n 
                 
               
               
                 x 
                 
                   n 
                 
               
             
             = 
             
               
                 
                   A 
                   F 
                 
               
               
                 
                   
                     
                       
                         1 
                         + 
                         
                           
                             
                               
                                 
                                   f 
                                   
                                     
                                       f 
                                       c 
                                     
                                   
                                 
                               
                             
                           
                           2 
                         
                       
                     
                   
                 
               
             
           
         
       
     
      where x′(n) is the modified audio object stream, x(n) is the audio object stream, A F  is the passband gain of the filter, f is the frequency of the audio object stream x(n), and f c  is the cut-off frequency. For example, A F  may be A F  = 1, in order to have a gain of 0 dB for f = 0 Hz. 
     In other words, low-pass filter has a gain A F  at DC from 0 Hz to the high-cut-off frequency limit f c . After f c , the gain A F  decreases constantly with increasing frequency. 
     Active low-pass filters are used in audio amplifiers, equalizers or speaker systems to direct the low-frequency bass signals to the larger bass speakers or to reduce high-frequency interference or distortion. 
       FIG.  11   b    illustrates a process of the embodiment described in  FIG.  2   b   , implemented by a decision tree and an action block. In this implementation, a decision tree  5  determines a list of actions by merging different distraction levels, a novelty factor and an estimation of a driving situation. In the present embodiment, a history of songs is analyzed, and, at  61 , a novelty level is estimated (see novelty factor in  FIG.  8   ). If the novelty level is more than 0.5, the method proceeds at  62 . If the novelty level is less than 0.5, the method proceeds at  69 . At  62 , a position distraction level is estimated (see  FIG.  3   ). If the position distraction level is less than 0.5, the method proceeds at  63 . If the position distraction level is more than 0.5, the method proceeds at  69 . At  63 , an aural distraction level is estimated (see  FIG.  5   a   ). If the aural distraction level is less than 0.5, the method proceeds at  64 . If the aural distraction level is more than 0.5, the method proceeds at  69 . At  64 , a perceived distance is estimated based on distance calculation (see  FIG.  6   ). If the perceived distance is small (here “close?”), the method proceeds at  65 . If the perceived distance is large, the method proceeds at  59 . At  65 , the driving situation is estimated (see  FIG.  7   ). If the driving situation is critical, the method proceeds at  66 . If the driving situation is not critical, the method proceeds at  69 . At  66 , a transient level is computed (see ratio τ 1  in the description of  FIG.  5   a   ). If the transient level τ 1  is high, the method proceeds at  67 . If the transient level τ 1 , is not high, the method proceeds at  68 . At  67 , a low-pass filtering (see  49  in  FIG.  10   ) is performed based on a low-pass filter waveform, which reduces the harshness of a sound. At  68 , the volume is reduced by reducing the amplitude of the audio-object (see  48  in  FIG.  10   ). At  69 , the method ends, e.g. “Do nothing”. 
     In the present embodiment, if the transient level τ 1  is not high, the list of actions, obtained for each set of distraction levels, here novelty level, criticalness of driving situation, position distraction level, aural distraction level, and distraction level based on distance estimation, is one list of actions which contains one action, here volume reduction performed at  68 . Also, if the transient level τ 1 , is high, the list of actions, obtained for each set of distraction levels, here novelty level, criticalness of driving situation, position distraction level, aural distraction level, and distraction level based on distance estimation, is one list of actions which contains one action, here low-pass filtering performed at  67 . Alternatively, the list of actions, obtained for each set of distraction levels, is one list of actions which may contain any number of actions, for example, the list of actions may contain one action or more than one actions. 
       FIG.  12   a    shows a flow diagram visualizing an exemplary method for performing audio stream modification as described in  FIG.  2   a   . 
     At  70 , an audio object stream (see  1  in  FIG.  2   a   ) is received and at  71 , a field of listening evaluation (see  10  in  FIGS.  2   a  and  3   ) is performed on the received audio object stream based on coordinates (x, y, z) of audio objects to estimate a position distraction level (see  21  in  FIGS.  2   a  and  3   ). At  72 , a sound signature estimation (see  11  in  FIGS.  2   a  and  5   a   ) is performed on the audio object stream based on an audio bitstream (see  30  in  FIG.  5   a   ) of the audio object stream to estimate an aural distraction level (see  31  in  FIG.  5   a   ) related to the audio bitstream. At  73 , a distance calculation (see  12  in  FIGS.  2   a  and  6   ) is performed on the audio object stream based on coordinates (x, y, z) of audio objects and on an audio bitstream to obtain a distance estimation (see  40 ,  41 ,  42 ,  43  in  FIG.  6   ). At  74 , a list of actions is determined by a decision tree (see  5  in  FIG.  2   a   ) based on input estimations, such as the estimated position distraction level, the estimated aural distraction level, and/or the distance estimation. At  75 , the received audio object stream is modified by an action block (see  6  in  FIG.  2   a   ) based on the determined list of actions and the audio object stream. At  76 , the modified audio object stream is output, for example to a loudspeaker array of a vehicle, in an in-vehicle scenario. 
       FIG.  12   b    shows a flow diagram visualizing an exemplary method for performing audio stream modification as described in  FIG.  2   b   ; 
     At  80 , an audio object stream (see  1  in  FIG.  2   b   ) is received and at  81 , a field of listening evaluation (see  10  in  FIGS.  2   b  and  3   ) is performed on the received audio object stream based on coordinates (x, y, z) of audio objects to estimate a position distraction level (see  21  in  FIGS.  2   b  and  3   ). At  82 , a sound signature estimation (see  11  in  FIGS.  2   b  and  5   a   ) is performed on the audio object stream based on an audio bitstream (see  30  in  FIG.  5   a   ) of the audio object stream to estimate an aural distraction level (see  31  in  FIG.  5   a   ) related to the audio bitstream. At  83 , a distance calculation (see  12  in  FIGS.  2   b  and  6   ) is performed on the audio object stream based on coordinates (x, y, z) of audio objects and on an audio bitstream to obtain a distance estimation (see  40 ,  41 ,  42 ,  43  in  FIG.  6   ). At  84 , a song history analysis (see  17  in  FIGS.  2   b  and  8   ) is performed based on history of songs (see  16  in  FIGS.  2   b  and  8   ) and based on the audio object stream to estimate novelty factor (see  46  in  FIGS.  2   b  and  8   ). At  85 , a driving situation analysis (see  14  in  FIGS.  2   b  and  7   ) is performed based on acquired vehicle data to estimate a driving situation (see  45  in  FIGS.  2   b  and  8   ). At  86 , a list of actions is determined by a decision tree (see  5  in  FIG.  2   b   ) based on input estimations, such as the estimated position distraction level, the estimated aural distraction level, the distance estimation, the novelty factor and/or the driving situation. At  87 , the received audio object stream is modified by an action block (see  6  in  FIG.  2   b   ) based on the determined list of actions and the audio object stream. At  88 , the modified audio object stream is output, for example to a loudspeaker array of a vehicle, in an in-vehicle scenario. 
     Automotive Implementation 
     The technology according to an embodiment of the present disclosure is applicable to various products. For example, the technology according to an embodiment of the present disclosure may be implemented as a device included in a mobile body that is any of kinds of automobiles, electric vehicles, hybrid electric vehicles, motorcycles, bicycles, personal mobility vehicles, airplanes, drones, ships, robots, construction machinery, agricultural machinery (tractors), and the like. 
       FIG.  13    shows a block diagram depicting an example of schematic configuration of a vehicle control system  7000  as an example of a mobile body control system to which the technology according to an embodiment of the present disclosure can be applied. The vehicle control system  7000  includes a plurality of electronic control units connected to each other via a communication network  7010 . In the example depicted in  FIG.  13   , the vehicle control system  7000  includes a driving system control unit  7100 , a body system control unit  7200 , a battery control unit  7300 , an outside-vehicle information detecting unit  7400 , an in-vehicle information detecting unit  7500 , and an integrated control unit  7600 . The communication network  7010  connecting the plurality of control units to each other may, for example, be a vehicle-mounted communication network compliant with an arbitrary standard such as controller area network (CAN), local interconnect network (LIN), local area network (LAN), FlexRay (registered trademark), or the like. 
     Each of the control units includes: a microcomputer that performs arithmetic processing according to various kinds of programs; a storage section that stores the programs executed by the microcomputer, parameters used for various kinds of operations, or the like; and a driving circuit that drives various kinds of control target devices. Each of the control units further includes: a network interface (I/F) for performing communication with other control units via the communication network  7010 ; and a communication I/F for performing communication with a device, a sensor, or the like within and without the vehicle by wire communication or radio communication. A functional configuration of the integrated control unit  7600  illustrated in  FIG.  13    includes a microcomputer  7610 , a general-purpose communication I/F  7620 , a dedicated communication I/F  7630 , a positioning section  7640 , a beacon receiving section  7650 , an in-vehicle device I/F  7660 , a sound/image output section  7670 , a vehicle-mounted network I/F  7680 , and a storage section  7690 . The other control units similarly include a microcomputer, a communication I/F, a storage section, and the like. 
     The driving system control unit  7100  controls the operation of devices related to the driving system of the vehicle in accordance with various kinds of programs. The driving system control unit  7100  may have a function as a control device of an antilock brake system (ABS), electronic stability control (ESC), or the like. 
     The driving system control unit  7100  is connected with a vehicle state detecting section  7110 . The driving system control unit  7100  performs arithmetic processing using a signal input from the vehicle state detecting section  7110 , and controls the internal combustion engine, the driving motor, an electric power steering device, the brake device, and the like. 
     The body system control unit  7200  controls the operation of various kinds of devices provided to the vehicle body in accordance with various kinds of programs. For example, the body system control unit  7200  functions as a control device for a keyless entry system, a smart key system, a power window device, or various kinds of lamps such as a headlamp, a backup lamp, a brake lamp, a turn signal, a fog lamp, or the like. 
     The battery control unit  7300  controls a secondary battery  7310 , which is a power supply source for the driving motor, in accordance with various kinds of programs. 
     The outside-vehicle information detecting unit  7400  detects information (see vehicle data  13  in  FIGS.  2   b  and  7   ) about the outside of the vehicle including the vehicle control system  7000 . For example, the outside-vehicle information detecting unit  7400  (see driving situation analyzer  14  in  FIGS.  2   b  and  7   ) is connected with at least one of an imaging section  7410  and an outside-vehicle information detecting section  7420 . The imaging section  7410  includes at least one of a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, and other cameras. The outside-vehicle information detecting section  7420 , for example, includes at least one of an environmental sensor for detecting current atmospheric conditions or weather conditions and a peripheral information detecting sensor for detecting another vehicle, an obstacle, a pedestrian, or the like on the periphery of the vehicle including the vehicle control system  7000 . 
     The in-vehicle information detecting unit  7500  detects information about the inside of the vehicle. The in-vehicle information detecting unit  7500  may collect any information related to a situation related to the vehicle. The in-vehicle information detecting unit  7500  is, for example, connected with a driver and/or passengers state detecting section  7510  that detects the state of a driver and/or passengers. The driver state detecting section  7510  may include a camera that images the driver, a biosensor that detects biological information of the driver, a microphone that collects sound within the interior of the vehicle, or the like. The biosensor is, for example, disposed in a seat surface, the steering wheel, or the like, and detects biological information of an occupant sitting in a seat or the driver holding the steering wheel. On the basis of detection information input from the driver state detecting section  7510 , the in-vehicle information detecting unit  7500  (see driving situation analyzer  14  in  FIGS.  2   b  and  7   ) may calculate a degree of fatigue of the driver or a degree of concentration of the driver or may determine whether the driver is dozing. The in-vehicle information detecting unit  7500  may subject an audio signal obtained by the collection of the sound to processing such as noise canceling processing or the like. 
     The integrated control unit  7600  controls general operation within the vehicle control system  7000  in accordance with various kinds of programs. The integrated control unit  7600  is connected with an input section  7800 . The input section  7800  is implemented by a device capable of input operation by an occupant, such, for example, as a touch panel, a button, a microphone, a switch, a lever, or the like. The integrated control unit  7600  may be supplied with data obtained by voice recognition of voice input through the microphone. The input section  7800  may, for example, be a remote control device using infrared rays or other radio waves, or an external connecting device such as a mobile telephone, a personal digital assistant (PDA), or the like that supports operation of the vehicle control system  7000 . The input section  7800  may be, for example, a camera. In that case, an occupant can input information by gesture. Alternatively, data may be input which is obtained by detecting the movement of a wearable device that an occupant wears. Further, the input section  7800  may, for example, include an input control circuit or the like that generates an input signal on the basis of information input by an occupant or the like using the above-described input section  7800 , and which outputs the generated input signal to the integrated control unit  7600 . An occupant or the like inputs various kinds of data or gives an instruction for processing operation to the vehicle control system  7000  by operating the input section  7800 . 
     The storage section  7690  may include a read only memory (ROM) that stores various kinds of programs executed by the microcomputer and a random access memory (RAM) that stores various kinds of parameters, operation results, sensor values, or the like. In addition, the storage section  7690  may be implemented by a magnetic storage device such as a hard disc drive (HDD) or the like, a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like. 
     The general-purpose communication I/F  7620  is a communication I/F used widely, which communication I/F mediates communication with various apparatuses present in an external environment  7750 . The general-purpose communication I/F  7620  may implement a cellular communication protocol such as global system for mobile communications (GSM (registered trademark)), worldwide interoperability for microwave access (WiMAX (registered trademark)), long term evolution (LTE (registered trademark)), LTE-advanced (LTE-A), or the like, or another wireless communication protocol such as wireless LAN (referred to also as wireless fidelity (Wi-Fi (registered trademark)), Bluetooth (registered trademark), or the like. The general-purpose communication I/F  7620  may, for example, connect to an apparatus (for example, an application server or a control server) present on an external network (for example, the Internet, a cloud network, or a company-specific network) via a base station or an access point. In addition, the general-purpose communication I/F  7620  may connect to a terminal present in the vicinity of the vehicle (which terminal is, for example, a terminal of the driver, a pedestrian, or a store, or a machine type communication (MTC) terminal) using a peer to peer (P2P) technology, for example. 
     The dedicated communication I/F  7630  is a communication I/F that supports a communication protocol developed for use in vehicles. The dedicated communication I/F  7630  may implement a standard protocol such, for example, as wireless access in vehicle environment (WAVE), which is a combination of institute of electrical and electronic engineers (IEEE) 802.11p as a lower layer and IEEE 1609 as a higher layer, dedicated short range communications (DSRC), or a cellular communication protocol. The dedicated communication I/F  7630  typically carries out V2X communication as a concept including one or more of communication between a vehicle and a vehicle (Vehicle to Vehicle), communication between a road and a vehicle (Vehicle to Infrastructure), communication between a vehicle and a home (Vehicle to Home), and communication between a pedestrian and a vehicle (Vehicle to Pedestrian). 
     The positioning section  7640  (see position calculator  12  in  FIG.  6   ), for example, performs positioning by receiving a global navigation satellite system (GNSS) signal from a GNSS satellite (for example, a GPS signal from a global positioning system (GPS) satellite), and generates positional information including the latitude, longitude, and altitude of the vehicle. Incidentally, the positioning section  7640  may identify a current position by exchanging signals with a wireless access point or may obtain the positional information from a terminal such as a mobile telephone, a personal handphone system (PHS), or a smart phone that has a positioning function. 
     The beacon receiving section  7650 , for example, receives a radio wave or an electromagnetic wave transmitted from a radio station installed on a road or the like, and thereby obtains information about the current position, congestion, a closed road, a necessary time, or the like. Incidentally, the function of the beacon receiving section  7650  may be included in the dedicated communication I/F  7630  described above. 
     The in-vehicle device I/F  7660  is a communication interface that mediates connection between the microcomputer  7610  and various in-vehicle devices  7760  present within the vehicle. The in-vehicle device I/F  7660  may establish wireless connection using a wireless communication protocol such as wireless LAN, Bluetooth (registered trademark), near field communication (NFC), or wireless universal serial bus (WUSB). In addition, the in-vehicle device I/F  7660  may establish wired connection by universal serial bus (USB), high-definition multimedia interface (HDMI (registered trademark)), mobile high-definition link (MHL), or the like via a connection terminal (and a cable if necessary) not depicted in the figures. The in-vehicle devices  7760  may, for example, include at least one of a mobile device and a wearable device possessed by an occupant and an information device carried into or attached to the vehicle. The in-vehicle devices  7760  may also include a navigation device that searches for a path to an arbitrary destination. The in-vehicle device I/F  7660  exchanges control signals or data signals with these in-vehicle devices  7760 . 
     The vehicle-mounted network I/F  7680  is an interface that mediates communication between the microcomputer  7610  and the communication network  7010 . The vehicle-mounted network I/F  7680  transmits and receives signals or the like in conformity with a predetermined protocol supported by the communication network  7010 . 
     The microcomputer  7610  of the integrated control unit  7600  controls the vehicle control system  7000  in accordance with various kinds of programs on the basis of information obtained via at least one of the general-purpose communication I/F  7620 , the dedicated communication I/F  7630 , the positioning section  7640 , the beacon receiving section  7650 , the in-vehicle device I/F  7660 , and the vehicle-mounted network I/F  7680 . The microcomputer  7610  may implement the functionality described in  FIG.  1    and  FIGS.  2   a  and  2   b    and in particular the processes describes in  FIGS.  3 ,  5   a ,  6 ,  7 ,  8 ,  9    and  FIG.  10   . For example, the microcomputer  7610  may calculate a control target value for the driving force generating device, the steering mechanism, or the braking device on the basis of the obtained information about the inside and outside of the vehicle, and output a control command to the driving system control unit  7100 . For example, the microcomputer  7610  may perform cooperative control intended to implement functions of an advanced driver assistance system (ADAS) which functions include collision avoidance or shock mitigation for the vehicle, following driving based on a following distance, vehicle speed maintaining driving, a warning of collision of the vehicle, a warning of deviation of the vehicle from a lane, or the like. In addition, the microcomputer  7610  may perform cooperative control intended for automatic driving, which makes the vehicle to travel autonomously without depending on the operation of the driver, or the like, by controlling the driving force generating device, the steering mechanism, the braking device, or the like on the basis of the obtained information about the surroundings of the vehicle. 
     The microcomputer  7610  may generate three-dimensional distance information between the vehicle and an object such as a surrounding structure, a person, or the like, and generate local map information including information about the surroundings of the current position of the vehicle, on the basis of information obtained via at least one of the general-purpose communication I/F  7620 , the dedicated communication I/F  7630 , the positioning section  7640 , the beacon receiving section  7650 , the in-vehicle device I/F  7660 , and the vehicle-mounted network I/F  7680 . In addition, the microcomputer  7610  may predict danger such as collision of the vehicle, approaching of a pedestrian or the like, an entry to a closed road, or the like on the basis of the obtained information, and generate a warning signal. The warning signal may, for example, be a signal for producing a warning sound or lighting a warning lamp. 
     The sound/image output section  7670  transmits an output signal, e.g. modified audio signal, (see modified audio object stream  4  in  FIGS.  1 ,  2   a  and  2   b   ) of at least one of a sound and an image to an output device capable of visually or auditorily notifying information to an occupant of the vehicle or the outside of the vehicle. In the example of  FIG.  13    an audio speaker  7710 , a display section  7720 , and an instrument panel  7730  are illustrated as the output device. The display section  7720  may, for example, include at least one of an on-board display and a head-up display. The display section  7720  may have an augmented reality (AR) display function. The output device may be other than these devices, and may be another device such as headphones, a wearable device such as an eyeglass type display worn by an occupant or the like, a projector, a lamp, or the like. In a case where the output device is a display device, the display device visually displays results obtained by various kinds of processing performed by the microcomputer  7610  or information received from another control unit in various forms such as text, an image, a table, a graph, or the like. In addition, in a case where the output device is an audio output device. 
     Incidentally, at least two control units connected to each other via the communication network  7010  in the example depicted in  FIG.  13    may be integrated into one control unit. Alternatively, each individual control unit may include a plurality of control units. Further, the vehicle control system  7000  may include another control unit not depicted in the figures. In addition, part or the whole of the functions performed by one of the control units in the above description may be assigned to another control unit. That is, predetermined arithmetic processing may be performed by any of the control units as long as information is transmitted and received via the communication network  7010 . Similarly, a sensor or a device connected to one of the control units may be connected to another control unit, and a plurality of control units may mutually transmit and receive detection information via the communication network  7010 . 
     Incidentally, a computer program for realizing the functions of the electronic device according to the present embodiment described with reference to  FIGS.  2   a  and  2   b    can be implemented in one of the control units or the like. In addition, a computer readable recording medium storing such a computer program can also be provided. The recording medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, or the like. In addition, the above-described computer program may be distributed via a network, for example, without the recording medium being used. 
       FIG.  14    shows an example of installation positions of the imaging section  7410  and the outside-vehicle information detecting section  7420 . Imaging sections  7910 ,  7912 ,  7914 ,  7916 , and  7918  are, for example, disposed at at least one of positions on a front nose, side-view mirrors, a rear bumper, and a back door of the vehicle  7900  and a position on an upper portion of a windshield within the interior of the vehicle. The imaging section  7910  provided to the front nose and the imaging section  7918  provided to the upper portion of the windshield within the interior of the vehicle obtain mainly an image of the front of the vehicle  7900 . The imaging sections  7912  and  7914  provided to the sideview mirrors obtain mainly an image of the sides of the vehicle  7900 . The imaging section  7916  provided to the rear bumper or the back door obtains mainly an image of the rear of the vehicle  7900 . The imaging section  7918  provided to the upper portion of the windshield within the interior of the vehicle is used mainly to detect a preceding vehicle, a pedestrian, an obstacle, a signal, a traffic sign, a lane, or the like. 
     Incidentally,  FIG.  14    depicts an example of photographing ranges of the respective imaging sections  7910 ,  7912 ,  7914 , and  7916 . An imaging range a represents the imaging range of the imaging section  7910  provided to the front nose. Imaging ranges b and c respectively represent the imaging ranges of the imaging sections  7912  and  7914  provided to the side-view mirrors. An imaging range d represents the imaging range of the imaging section  7916  provided to the rear bumper or the back door. Outside-vehicle information detecting sections  7920 ,  7922 ,  7924 ,  7926 ,  7928 , and  7930  provided to the front, rear, sides, and corners of the vehicle  7900  and the upper portion of the windshield within the interior of the vehicle may be, for example, an ultrasonic sensor or a radar device. The outside-vehicle information detecting sections  7920 ,  7926 , and  7930  provided to the front nose of the vehicle  7900 , the rear bumper, the back door of the vehicle  7900 , and the upper portion of the windshield within the interior of the vehicle may be a LIDAR device, for example. These outside-vehicle information detecting sections  7920  to  7930  are used mainly to detect a preceding vehicle, a pedestrian, an obstacle, or the like. 
     Implementation in Intelligent Personal Assistants 
       FIG.  15    schematically describes an embodiment of an electronic device, related to a vehicle or a household, which implements the process of distraction minimization for playing-back object-based audio material, as described above. The electronic device  700 , may also be an electronic system, which comprises a CPU  701  as processor. The electronic device 1300 further comprises a microphone array  711  and a loudspeaker array  710  that are connected to the processor  701 . Processor  701  may for example implement a field of listening estimator  10 , a sound signature estimator  11 , a distance calculator  12 , a driving situation analyzer  14 , a decision tree  5  that realize the processes described with regard to  FIGS.  2   a ,  2   b ,  3 ,  5   a ,  6 ,  7 ,  11   a  and  11   b   , in more detail. The microphone array  711  may be configured to receive speech (voice) commands via automatic speech recognition. Loudspeaker array  710  consists of one or more loudspeakers that are distributed over a predefined space and is configured to render 3D audio as described in the embodiments above. The electronic device  700  further comprises an audio interface  706  and a user interface  709  that are connected to the processor  701 . This user interface  709  acts as a man-machine interface and enables a dialogue between an administrator and the electronic system. For example, an administrator may make configurations to the system using this user interface  709 . The electronic system  700  further comprises an Ethernet interface  707 , a Bluetooth interface  704 , and a WLAN interface  705 . These units  704 ,  705  act as I/O interfaces for data communication with external devices. For example, additional loudspeakers, microphones, and video cameras with Ethernet, WLAN or Bluetooth connection may be coupled to the processor  701  via these interfaces  707 ,  704 , and  705 . 
     The electronic device  700  further comprises a data storage  702  and a data memory  703  (here a RAM). The data memory  703  is arranged to temporarily store or cache data or computer instructions for processing by the processor  701 . The data storage  702  is arranged as a long term storage, e.g., for recording sensor data obtained from the microphone array  711 . The data storage  702  may also store audio data that represents audio messages, which the public announcement system may transport to people moving in the predefined space. 
     The electronic device of  FIG.  15    may for example be used in a smart speaker, or the like. 
     Via the Ethernet interface  707  or the WLAN interface  705 , the electronic device of  FIG.  15    may be connected to a telephone system to receive incoming calls. 
     It should be noted that the description above is only an example configuration. Alternative configurations may be implemented with additional or other sensors, storage devices, interfaces, or the like. 
     It should be recognized that the embodiments describe methods with an exemplary ordering of method steps. The specific ordering of method steps is however given for illustrative purposes only and should not be construed as binding. 
     It should also be recognized that the division of the electronic system of  FIG.  15    into units is only made for illustration purposes and that the present disclosure is not limited to any specific division of functions in specific units. For instance, at least parts of the circuitry could be implemented by a respective programmed processor, field programmable gate array (FPGA), dedicated circuits, and the like. 
     All units and entities described in this specification and claimed in the appended claims can, if not stated otherwise, be implemented as integrated circuit logic, for example on a chip, and functionality provided by such units and entities can, if not stated otherwise, be implemented by software. 
     In so far as the embodiments of the disclosure described above are implemented, at least in part, using software-controlled data processing apparatus, it will be appreciated that a computer program providing such software control and a transmission, storage or other medium by which such a computer program is provided are envisaged as aspects of the present disclosure. 
     Note that the present technology can also be configured as described below.
     (1) An electronic device comprising circuitry configured to estimate ( 2 ) a distraction level of an audio object stream ( 1 ), and to modify ( 3 ) the audio object stream based on the estimated distraction level to obtain a modified audio object stream ( 4 ).   (2) The electronic device of ( 1 ), wherein the circuitry is further configured to modify the audio object stream ( 1 ) based on the estimated distraction level by an audio object stream modification including a decision tree.   (3) The electronic device of anyone of ( 1 ) or ( 2 ), wherein the circuitry is configured to perform a field-of-listening evaluation ( 10 ) on the audio object stream ( 1 ) to estimate a position distraction level ( 21 ).   (4) The electronic device of anyone of ( 1 ) to ( 3 ), wherein the circuitry is configured to perform a sound signature estimation ( 11 ) on the audio object stream ( 1 ) to estimate an aural distraction level ( 31 ).   (5) The electronic device of anyone of ( 1 ) to ( 4 ), wherein the circuitry is configured to perform a distance calculation ( 12 ) on the audio object stream ( 1 ) to obtain a distance estimation (40,  41 ,  42 ,  43 ).   (6) The electronic device of ( 5 ), wherein the distance estimation ( 40 ,  41 ,  42 ,  43 ) comprises a perceived distance ( 40 ), a perceived velocity vector ( 41 ), a cross-correlation ( 42 ), and/or an auto-correlation ( 43 ).   (7) The electronic device of ( 2 ), wherein the circuitry is further configured to evaluate the decision tree ( 5 ) to determine a list of actions.   (8) The electronic device of ( 7 ), wherein the circuitry is further configured to perform an action block ( 6 ) to obtain the modified audio object stream ( 4 ).   (9) The electronic device of anyone of ( 1 ) to ( 8 ), wherein the circuitry is further configured to perform a driving situation analysis ( 14 ) based on acquired vehicle data ( 13 ) to estimate a driving situation ( 45 ).   (10) The electronic device of ( 9 ), wherein a level of criticalness of a current driving situation is estimated based on the driving situation ( 45 ).   (11) The electronic device of anyone of ( 1 ) to ( 9 ), wherein the circuitry is further configured to perform a song history analysis ( 17 ) on a history of songs ( 16 ) to estimate a novelty factor ( 46 ) related to the audio object stream ( 1 ).   (12) The electronic device of anyone of ( 1 ) to ( 11 ), wherein the circuitry is further configured to extract coordinates ( 20 ) of an audio object in the audio object stream ( 1 ).   (13) The electronic device of ( 12 ), wherein the coordinates ( 20 ) of an audio object represent a position of the audio object in a field of listening.   (14) The electronic device of anyone of ( 1 ) to ( 13 ), wherein the circuitry is further configured to extract spectral and temporal characteristics of the audio object stream from a bitstream ( 30 ).   (15) The electronic device of ( 14 ), wherein an aural distraction level ( 31 ) is estimated based on the obtained audio bitstream ( 30 ).   (16) The electronic device of ( 7 ), wherein the list of actions includes an amplitude reduction of an audio object ( 48 ), a low-pass/median filtering ( 49 ), and/or a modification of position ( 50 ).   (17) The electronic device of anyone of ( 1 ) to ( 16 ), wherein the circuitry is further configured to perform distraction minimization in the audio object stream ( 1 ) to obtain the modified audio object stream ( 4 ).   (18) The electronic device of ( 6 ), wherein the circuitry is further configured to extract coordinates and a bitstream to obtain the perceived distance ( 40 ), the perceived velocity vector ( 41 ), the cross-correlation ( 42 ), and/or the auto-correlation ( 43 ).   (19) A computer program comprising instructions, the instructions when executed on a processor causing the processor to estimate ( 2 ) a distraction level of an audio object stream ( 1 ), and to modify   ( 3 ) the audio object stream based on the estimated distraction level to obtain a modified audio object stream ( 4 ).   (20) The electronic device of ( 1 ), wherein the circuitry is further configured to output the modified audio object stream to a loudspeaker system.   (21) The electronic device of ( 1 ), wherein the circuitry is further configured to reduce a distraction level of a driver based on the modified audio object stream outputted to a loudspeaker system of a vehicle.   (22) The electronic device of ( 3 ), wherein the field of listening evaluation estimates the position distraction level ( 21 ) based on extracted coordinates ( 20 ) of an audio object in the audio object stream ( 1 ).   (23) The electronic device of anyone of ( 1 ) to ( 22 ), wherein the distraction level is a position distraction level, a distance estimation, an aural distraction level, a novelty factor and/ or a driving situation.   (24) The electronic device of ( 5 ), wherein a distance distraction level is estimated based on the distance estimation.   (25) The electronic device of ( 9 ), wherein a driving distraction level is estimated based on the driving situation ( 45 ).   (26) The electronic device of ( 11 ), wherein a novelty distraction level is estimated based on the novelty factor ( 46 ).   (27) A method comprising estimating ( 2 ) a distraction level of an audio object stream ( 1 ), and modifying ( 3 ) the audio object stream ( 1 ) based on the estimated distraction level to obtain a modified audio object stream ( 4 ).   (28) The method of ( 27 ), wherein the method comprises modifying the audio object stream ( 1 ) based on the estimated distraction level by an audio object stream modification including a decision tree.   (29) The method of anyone of ( 27 ) or ( 28 ), wherein the method comprises performing a field-of-listening evaluation ( 10 ) on the audio object stream ( 1 ) to estimate a position distraction level ( 21 ).   (30) The method of anyone of ( 27 ) to ( 29 ), wherein the method comprises performing a sound signature estimation ( 11 ) on the audio object stream ( 1 ) to estimate an aural distraction level ( 31 ).   (31) The method of anyone of ( 27 ) to ( 30 ), wherein the method comprises performing a distance calculation ( 12 ) on the audio object stream ( 1 ) to obtain a distance estimation (40,  41 ,  42 ,  43 ).   (32) The method of ( 31 ), wherein the distance estimation (40,  41 ,  42 ,  43 ) comprises a perceived distance ( 40 ), a perceived velocity vector ( 41 ), a cross-correlation ( 42 ), and/or an auto-correlation ( 43 ).   (33) The method of ( 28 ), wherein the method further comprises evaluating the decision tree ( 5 ) to determine a list of actions.   (34) The method of ( 33 ), wherein the method further comprises performing an action block ( 6 ) to obtain the modified audio object stream ( 4 ).   (35) The method of anyone of ( 27 ) to ( 34 ), wherein the method further comprises performing a driving situation analysis ( 14 ) based on acquired vehicle data ( 13 ) to estimate a driving situation ( 45 ).   (36) The method of ( 35 ), wherein a level of criticalness of a current driving situation is estimated based on the driving situation ( 45 ).   (37) The method of anyone of ( 27 ) to ( 36 ), wherein the method further comprises performing a song history analysis ( 17 ) on a history of songs ( 16 ) to estimate a novelty factor ( 46 ) related to the audio object stream ( 1 ).   (38) The method of anyone of ( 27 ) to ( 37 ), wherein the method further comprises extracting coordinates ( 20 ) of an audio object in the audio object stream ( 1 ).   (39) The method of ( 38 ), wherein the coordinates ( 20 ) of an audio object represent a position of the audio object in a field of listening.   (40) The method of anyone of ( 27 ) to ( 39 ), wherein the method further comprises extracting spectral and temporal characteristics of the audio object stream from a bitstream ( 30 ).   (41) The method of ( 40 ), wherein an aural distraction level ( 31 ) is estimated based on the obtained audio bitstream ( 30 ).   (42) The method of ( 33 ), wherein the list of actions includes an amplitude reduction of an audio object ( 48 ), a low-pass/median filtering ( 49 ), and/or a modification of position ( 50 ).   (43) The method of anyone of ( 27 ) to ( 42 ), wherein the method further comprises performing distraction minimization in the audio object stream ( 1 ) to obtain the modified audio object stream ( 4 ).   (44) The method of ( 32 ), wherein the method further comprises extracting coordinates and a bitstream to obtain the perceived distance ( 40 ), the perceived velocity vector ( 41 ), the cross-correlation ( 42 ), and/or the auto-correlation ( 43 ).   (45) The method of ( 27 ), wherein the method further comprises outputting the modified audio object stream to a loudspeaker system.   (46) The method of ( 27 ), wherein the method further comprises reducing a distraction level of a driver based on the modified audio object stream outputted to a loudspeaker system of a vehicle.