Patent Publication Number: US-2022223165-A1

Title: Selective suppression of noises in a sound signal

Description:
FIELD OF DISCLOSURE 
     Disclosed aspects are directed to filtering and suppressing noises that occur during calls. 
     More particularly, exemplary aspects are directed to selectively filtering and suppressing different types of noises that occur during calls especially conference calls. 
     BACKGROUND 
     During voice calls, video calls, conference calls or various online meeting calls, listeners on the calls may hear different types of noises from the speaker including background noises such as wind noise, traffic noise, noises from children, pets or household activities and any other types of noises that may cause disturbance to the conversations occurring on various calls. Such noises decrease the quality of the calls and may cause delays because the listeners may not properly hear the speaker. Since more and more people are working from home, various noises may affect the productivity of the workers while communicating with other workers through various calls including voice calls, video calls, conference calls and online meeting calls. 
     Accordingly, there is a need for systems and methods that suppress and filter various types of noises that may occur while conducting calls. 
     SUMMARY 
     Exemplary aspects of the disclosure are directed to systems and methods for detecting and identifying noises in a sound signal occurring during a call and selectively filtering and suppressing the noises in the sound signal. For example, an exemplary aspect is directed to a method of suppressing noises in a sound signal in a mobile device, the method comprising: receiving the sound signal; detecting the noises in the received sound signal; identifying the noises in the received sound signal; displaying the identified noises in a user interface (UI); receiving a selection of the displayed identified noises from the UI; and filtering the received selection of the displayed identified noises from the received sound signal 
     The method further comprises: utilizing a machine learning module with a neural network to detect and identify the noises in the received sound signal; transmitting the filtered sound signal; using a location of the mobile device to identify the noises in the received sound signal; converting the received sound signal from an analog signal to a digital signal; and converting the filtered sound signal from a digital signal to an analog signal. 
     Another exemplary aspect is directed to a mobile device comprising: a memory; and a processor communicatively coupled to the memory, the processor configured to: receive a sound signal, detect noises in the received sound signal, identify the noises in the received sound signal, display the identified noises in a user interface (UI), receive a selection of the displayed identified noises from the UI, and filter the received selection of the displayed identified noises from the received sound signal. The processor is further configured to: utilize a machine learning module with a neural network to detect and identify the noises in the received sound signal and use a location of the mobile device to identify the noises in the received sound signal. 
     Another exemplary aspect is directed to a mobile device comprising: means for receiving a sound signal; means for detecting noises in the received sound signal; means for identifying the noises in the received sound signal; means for displaying the identified noises in a user interface (UI); means for receiving a selection of the displayed identified noises from the UI; and means for filtering the received selection of the displayed identified noises from the received sound signal. 
     The mobile device further comprises: means for utilizing a machine learning module with a neural network to detect and identify the noises in the received sound signal; means for transmitting the filtered sound signal; and means for using a location of the mobile device to identify the noises in the received sound signal. 
     Yet another exemplary aspect is directed to a non-transitory computer-readable storage medium comprising code, which, when executed by a processor, causes the processor to suppress noises in a sound signal, the non-transitory computer-readable storage medium comprising: code for receiving a sound signal; code for detecting noises in the received sound signal; code for identifying the noises in the received sound signal; code for displaying the identified noises in a user interface (UI); code for receiving a selection of the displayed identified noises from the UI; code for filtering the received selection of the displayed identified noises from the received sound signal; code for utilizing a machine learning module with a neural network to detect and identify the noises in the received sound signal. code for transmitting the filtered sound signal; and code for using a location of the mobile device to identify the noises in the received sound signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings are presented to aid in the description of aspects and are provided solely for illustration of the aspects and not limitation thereof. 
         FIG. 1  illustrates an exemplary wireless communications system, according to various aspects. 
         FIGS. 2A-2B  illustrate an exemplary user interface (UI) for a mobile device. 
         FIGS. 3A-3B  illustrate flowcharts corresponding to one or more methods of filtering and suppressing noises in a sound signal occurring during a call, according to various aspects of the disclosure. 
         FIG. 4  illustrates an exemplary implementation of a wireless communication device configured for filtering and suppressing noises in a sound signal occurring during a call. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term “aspects of the disclosure” does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation. 
     The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting of aspects of the disclosure. As used herein, the singular forms “a”, “an”, and “the”, are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “includes”, and/or “including”, when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     Further, many aspects are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be recognized that various actions described herein can be performed by specific circuits (e.g., application specific integrated circuits (ASICs)), by program instructions being executed by one or more processors, or by a combination of both. Additionally, these sequence of actions described herein can be considered to be embodied entirely within any form of computer readable storage medium having stored therein a corresponding set of computer instructions that upon execution would cause an associated processor to perform the functionality described herein. Thus, the various aspects of the disclosure may be embodied in a number of different forms, all of which have been contemplated to be within the scope of the claimed subject matter. In addition, for each of the aspects described herein, the corresponding form of any such aspects may be described herein as, for example, “logic configured to” perform the described action. 
     Aspects of the present disclosure are directed to detecting and identifying noises in sound signals that occur during calls and selectively filtering and suppressing the noises. In an aspect, a machine learning module with a neural network may be utilized to detect and identify the noises. In another aspect, a user may be allowed to select the noises that the user wants to filter and suppress during calls. 
     According to various aspects,  FIG. 1  illustrates an exemplary wireless communications system  100 A. The wireless communications system  100 A (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations  102 A and various UEs  104 A. The base stations  102 A may include macro cells (high power cellular base stations) and/or small cells (low power cellular base stations), wherein the macro cells may include Evolved NodeBs (eNBs), where the wireless communications system  100 A corresponds to an LTE network, or gNodeBs (gNBs), where the wireless communications system  100 A corresponds to a 5G network or a combination of both, and the small cells may include femtocells, picocells, microcells, etc. 
     The base stations  102 A may collectively form a Radio Access Network (RAN) and interface with an Evolved Packet Core (EPC) or Next Generation Core (NGC) through backhaul links. In addition to other functions, the base stations  102 A may perform functions that relate to one or more of transferring user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations  102 A may communicate with each other directly or indirectly (e.g., through the EPC/NGC) over backhaul links  134 A, which may be wired or wireless. 
     The base stations  102 A may wirelessly communicate with the UEs  104 A. Each of the base stations  102 A may provide communication coverage for a respective geographic coverage area  110 A. In an aspect, although not shown in  FIG. 1A , geographic coverage areas  110 A may be subdivided into a plurality of cells (e.g., three), or sectors, each cell corresponding to a single antenna or array of antennas of a base station  102 A. As used herein, the term “cell” or “sector” may correspond to one of a plurality of cells of a base station  102 A, or to the base station  102 A itself, depending on the context. 
     While neighboring macro cell geographic coverage areas  110 A may partially overlap (e.g., in a handover region), some of the geographic coverage areas  110 A may be substantially overlapped by a larger geographic coverage area  110 A. For example, a small cell base station  102 A′ may have a geographic coverage area  110 A′ that substantially overlaps with the geographic coverage area  110 A of one or more macro cell base stations  102 A. A network that includes both small cell and macro cells may be known as a heterogeneous network. A heterogeneous network may also include Home eNBs (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG). The communication links  120 A between the base stations  102 A and the UEs  104 A may include uplink (UL)(also referred to as reverse link) transmissions from a UE  104 A to a base station  102 A and/or downlink (DL) (also referred to as forward link) transmissions from a base station  102 A to a UE  104 A. The communication links  120 A may use MIMO antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links may be through one or more carriers. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or less carriers may be allocated for DL than for UL). 
     The wireless communications system  100 A may further include a wireless local area network (WLAN) access point (AP)  150 A in communication with WLAN stations (STAs)  152 A via communication links  154 A in an unlicensed frequency spectrum (e.g., 5 GHz). When communicating in an unlicensed frequency spectrum, the WLAN STAs  152 A and/or the WLAN AP  150 A may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available. 
     The small cell base station  102 A′ may operate in a licensed and/or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station  102 A′ may employ LTE or 5G technology and use the same 5 GHz unlicensed frequency spectrum as used by the WLAN AP  150 A. The small cell base station  102 A′, employing LTE/5G in an unlicensed frequency spectrum, may boost coverage to and/or increase capacity of the access network. LTE in an unlicensed spectrum may be referred to as LTE-unlicensed (LTE-U), licensed assisted access (LAA), or MulteFire. 
     The wireless communications system  100 A may further include a mmW base station  180 A that may operate in mmW frequencies and/or near mmW frequencies in communication with a UE  182 A. Extremely high frequency (EHF) is part of the RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength between 1 millimeter and 10 millimeters. Radio waves in this band may be referred to as a millimeter wave (mmW). Near mmW may extend down to a frequency of 3 GHz with a wavelength of 100 millimeters. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, also referred to as centimeter wave. Communications using the mmW/near mmW radio frequency band have high path loss and a relatively short range. The mmW base station  180 A may utilize beamforming  184 A with the UE  182 A to compensate for the extremely high path loss and short range. Further, it will be appreciated that in alternative configurations, one or more base stations  102 A may also transmit using mmW or near mmW and beamforming. Accordingly, it will be appreciated that the foregoing illustrations are merely examples and should not be construed to limit the various aspects disclosed herein. 
     The wireless communications system  100 A may further include one or more UEs, such as UE  190 A, that connects indirectly to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links. In the embodiment of  FIG. 1 , UE  190 A has a D2D P2P link  192 A with one of the UEs  104 A connected to one of the base stations  102 A (e.g., through which UE  190 A may indirectly obtain cellular connectivity) and a D2D P2P link  194 A with WLAN STA  152 A connected to the WLAN AP  150 A (through which UE  190 A may indirectly obtain WLAN-based Internet connectivity). In an example, the D2D P2P links  192 A- 194 A may be supported with any well-known D2D radio access technology (RAT), such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), BLUETOOTH, and so on. 
     With reference now to  FIG. 4 , a simplified schematic of an exemplary mobile device  400  that detects and identifies noises in a sound signal occurring during a call and selectively filters and suppresses the noises is illustrated. Mobile device  400  is similar to UE  104 A or other UEs shown in  FIG. 1  in many exemplary aspects, and the depiction and description of mobile device  400  includes various additional exemplary components not shown with relation to UE  104 A and other UEs shown in  FIG. 1 . In an aspect, mobile device  400  may be similar to WLAN STAs  152 A shown in  FIG. 1 . Furthermore, mobile device  400  maybe similar to PC computers, laptops, tablets or other smart devices that are able to communicate with other UEs and mobile devices over the internet. 
     As shown in  FIG. 4 , mobile device  400  includes digital signal processor (DSP)  464  and a general purpose processor, depicted as processor  465 . Processor  465  includes machine learning (ML) module  468 . In an aspect, ML module  468  may be included in DSP  464  instead of processor  465  or may be a separate module located outside of processor  465  and DSP  464 . The below described functions and methods related to detecting and identifying noises in a sound signal occurring during a call and selectively filtering and suppressing the noises can be performed in DSP  464 , processor  465 . ML module  468  or any combination of the processing elements thereof. Accordingly, in some aspects, processor  465  and the included ML module  468  may be configured to perform operations described with regard to detecting and identifying noises in a sound signal occurring during a call and selectively filtering and suppressing the noises, but it will be understood that some of the operations related to detecting and identifying noises in a sound signal occurring during a call and selectively filtering and suppressing the noises can be performed in DSP  464 , and moreover, these operations can be implemented in any suitable combination of hardware and software. 
     ML module  468  may be an artificial neural network adapted to detecting and identifying different types of noises that occur during calls. An artificial neural network is particularly suited for detecting and identifying different types of noises because a neural network may be trained to recognize different types of noises that may occur in different situations and environment. In an aspect, a neural network implemented by ML module  468  may be a deep convolutional network. A deep convolutional network is particularly well suited for detecting and identifying noises and for its robustness and ability to efficiently train its network to adapt to the environment to detecting and identifying noises that may occur during a call. However, ML module  468  is not limited to a deep convolutional network but may implement other types of neural network such as a recurrent neural network (Ex. Long short-term memory (LSTM) network) or spiking neural network. A recurrent neural network is also well suited for detecting and identifying noises in calls. 
     Both DSP  464  and processor  465  may be coupled to memory  240 . Navigation engine  408  can be coupled to DSP  464  and processor  465  and used to provide location data to DSP  464  and processor  465 . Codec  402  can be coupled to DSP  464 , processor  465 , microphone  430  and speaker  435 . Codec  402  may convert analog signals from microphone  430  to digital signals and provide the digital signals to processor  465  and DSP  464 . In addition, codec  402  may convert digital signals from processor  465  and DSP  464  to analog signals and provide the analog signals to speaker  435 . Display controller  426  can be coupled to DSP  464 , processor  465 , and to display  428 . Display controller  426  may display user interface (UI) on display  428  as directed by processor  465  and/or DSP  464 . 
     Other components, such as transceiver  440  (which may be part of a modem) and receiver  441  are also illustrated. Transceiver  440  can be coupled to wireless antenna  442 , which may be configured to receive wireless signals from a calibrated terrestrial source such as WWAN, CDMA, etc. Transceiver  440  may receive and/or transmit signals to and from base station  102 A. Receiver  441  can be coupled to a satellite or GNSS antenna  443 , which may be configured to receive wireless signals from satellites or GNSS signals. In a particular aspect, DSP  464 , processor  465 , machine learning module  468 , display controller  426 , memory  240 , navigation engine  408 , transceiver  440 , receiver  441  and codec  402  are included in a system-in-package or system-on-chip device  422 . 
     In a particular aspect, input device  437  and power supply  444  are coupled to the system-on-chip device  422 . Moreover, in a particular aspect, as illustrated in  FIG. 4 , display  428 , input device  437 , microphone  430 , speaker  435 , wireless antenna  442 , GNSS antenna  443 , and power supply  444  are external to the system-on-chip device  422 . However, each of display  428 , input device  437 , microphone  430 , speaker  435 , wireless antenna  442 , GNSS antenna  443 , and power supply  444  can be coupled to a component of the system-on-chip device  422 , such as an interface or a controller. 
     In an aspect, when the user of mobile device  400  starts to speak to other participants of a call, microphone  430  receives the sound coming from the user and any other sounds that may be present while the user is speaking into microphone  430 . In other words, the user of mobile device  400  becomes the originator on the call when the user is speaking into microphone  430 . Calls made on mobile device  400  may be voice calls, video calls, conference calls or other various online meeting calls. The sound received by microphone  430  may include user&#39;s voice and various noises that may be present while the user is speaking into microphone  430 . Microphone  430  transmits the received sound signal that includes the user&#39;s voice and noises to codec  402 . Codec  402  processes the received sound signal that may be an analog signal and converts the received sound signal to a digital sound signal. Codec  402  transmits the converted digital sound signal to processor  465 . Codec  402  may also transmit the converted digital sound signal to DSP  464 . 
     In an aspect, processor  465  receives the sound signal from codec  402  and may process the sound signal by utilizing ML module  468 . ML module  468  may have been pretrained to detect and identify different types of noises that commonly occur during calls. For example, different types of commonly occurring noises may include road noise, children noise, traffic noise, wind noise, animal noise, household noise, urban environment noise, etc. In addition, navigation engine  408  may provide the location information of mobile device  400  to processor  465  to assist in the identification of noises that may occur in the background based on the location of mobile device  400 . For example, if the user of mobile device  400  is located at a railway station, navigation engine  408  may provide this location information to processor  465 . Processor  465  and ML module  468  may use this location information to assist in the identification and detection of the background noises that may occur at a railway station such as noises made by moving trains and crowds at a railway station. In other words, ML module  468  may use the location of mobile device  400  to assist in the identification of noises that may occur when the user of mobile device  400  is speaking on mobile device  400  because the location of mobile device  400  may dictate the likelihood of certain noises that may occur during the call. For example, if mobile device  400  is located next to a busy street, there is high probability that microphone  430  will pick up traffic noises. 
     ML module  468  may have a training database (not shown) that contains the training that allows ML module  468  to detect and identify various type of noises that occur during calls. ML module  468  may use the training in the training database to identify and detect the noises that are present in the sound signal received from codec  402 . As stated above, ML module  468  and processor  465  may use the location information received from navigation engine  408  to assist in the identification of the noises that are present in the received sound signal. 
     After identifying the noises in the sound signal received from codec  402 , processor  465  may display user interface (UI)  200  as shown in  FIG. 2A . UI  200  may be shown on display  428 . UI  200  includes menu  210  as illustrated in  FIG. 2A . Menu  210  may list the types of noises that have been identified by processor  465  and ML module  468 . In the example shown in  FIG. 2A , processor  465  and ML module  468  have identified the following noises in the sound signal received from codec  402 : dog barking  220 A, baby crying  220 B, cooker/kettle whistle  220 C and kids screaming  220 D. Menu  210  lists and shows all of the identified noises as shown in  FIG. 2A . Each of the listed noises  220 A- 220 D has a respective check boxes  225 A- 225 D next to them. UI  200  allows the user of mobile device  400  to check off the noises  220 A- 220 D that the user wants to filter out of the user&#39;s communication during the call by inputting a check mark in the respective check boxes  225 A- 225 D. The type of noises shown in  FIG. 2A  is just an exemplary list, and ML module  468  is not limited to identifying the noises shown on  FIG. 2A . ML module  468  may detect and identify many other types of noises based on the training received. In other instances, menu  210  may list other types of noises not shown in  FIG. 2A  based on the identification of the noises in the received sound signal. 
     For example, if the user of mobile device  400  wants to filter out and suppress noises  220 A,  220 C and  220 D, the user may place check marks in the respective check boxes  225 A,  225 C and  225 D as shown in  FIG. 2A . However, if the user does not want to filter out noise  220 B, the user of mobile device  400  should not place a check mark in check box  225 B as shown in  FIG. 2A . 
     Processor  465  receives from UI  200  the noises chosen by the user of mobile device  400  for filtering. Processor  465  may filter the chosen noises from the sound signal received from codec  402  thereby suppressing the chosen noises. In an aspect, processor  465  may use DSP  464  to filter the noises chosen by the user of mobile device  400 . In the example shown in  FIG. 2A , processor  465  filters noises  220 A,  220 C and  220 D from the sound signal received from codec  402 . 
     After the noises have been filtered, processor  465  transmits the filtered sound signal to transceiver  440 . Transceiver  440  transmits the filtered sound signal to other UE(s) and mobile devices that are on the call with mobile device  400 . Thus, target or listener mobile devices and UEs on the call would receive a sound signal without the filtered noises chosen by the user of mobile device  400 . 
     In another aspect, the user of mobile device  400  may be a listener (i.e., a target) rather than a speaker (i.e., originator) when the user of mobile device  400  is on a call with one or more UEs and mobile devices. When the user of mobile device  400  is the listener, transceiver  440  receives a signal sent by another UE through antenna  442 . The received signal may include a sound signal comprising the sound spoken by the speaker and noises that occurred while the speaker was speaking into the speaker&#39;s UE. Transceiver  440  transmits the received sound signal to processor  465  to be processed. 
     Processor  465  receives the sound signal from transceiver  440  and may process the sound signal by utilizing ML module  468 . As described above, ML module  468  may have been pretrained to recognize and identify different types of noises that commonly occur during calls. For example, different types of commonly occurring noises may include road noise, children noise, traffic noise, wind noise, animal noise, household noise, etc. ML module  468  may detect and identify the noises that are present in the sound signal received from transceiver  440 . 
     After detecting and identifying the noises in the sound signal received from transceiver  440 , processor  465  may display user interface (UI)  250  as shown in  FIG. 2B . UI  250  may be shown on display  428 . UI  250  includes menu  260  as illustrated in  FIG. 2B . Menu  260  may list the types of noises that have been identified by processor  465  and ML module  468 . In the example shown in  FIG. 2B , processor  465  and ML module  468  have detected and identified the following noises in the sound signal received from transceiver  440 : dog barking  265 A, baby crying  265 B, cooker/kettle whistle  265 C and kids screaming  265 D. Menu  260  lists and shows all of the identified noises as shown in  FIG. 2B . Each of the listed noises  265 A- 265 D has a respective check boxes  270 A- 270 D next to them. UI  250  allows the user of mobile device  400  to check off the noises  265 A- 265 D that the user wants to filter out of the received sound signal by inputting a check mark in the respective check boxes  270 A- 270 D. The type of noises shown in  FIG. 2B  is just an exemplary list, and ML module  468  is not limited to identifying the noises shown on  FIG. 2B . ML module  468  may detect and identify many other types of noises based on the training received. 
     For example, if the user of mobile device  400  wants to filter out and suppress noises  265 A,  265 C and  265 D, the user may place check marks in the respective check boxes  270 A,  270 C and  270 D as shown in  FIG. 2B . However, if the user does not want to filter out noise  265 B, the user of mobile device  400  should not place a check mark in check box  270 B as shown in  FIG. 2B . 
     Processor  465  receives from UI  250  the noises chosen by the user of mobile device  400  for filtering. Processor  465  may filter the chosen noises from the sound signal received from transceiver  440 . In an aspect, processor  465  may use DSP  464  to filter the noises chosen by the user of mobile device  400 . In the example shown in  FIG. 2B , processor  465  filters noises  265 A,  265 C and  265 D from the sound signal received from transceiver  440 . 
     In an aspect, while the user of mobile device  400  is listening to the speaker on the call, ML module  468  may detect and identify the background noises that exist around mobile device  400  in case the user of mobile device  400  becomes the speaker later. In some aspect, the detection may occur even though microphone  430  is muted. In other aspect, the detection may occur only when the microphone  430  is unmuted. 
     After the noises have been filtered, processor  465  transmits the filtered sound signal to codec  402 . Codec  402  converts the filtered sound signal to an analog sound signal and transmits the analog sound signal to speaker  435 . Consequently, the user of mobile device  400  will hear a sound signal that have filtered and suppressed the noises that the user of mobile device  400  have chosen to filter. Therefore, the user of mobile device  400  will not hear and be distracted by the noises that may have been transmitted from the speaker&#39;s UE. 
     It will be appreciated that aspects include various methods for performing the processes, functions and/or algorithms disclosed herein. For example,  FIG. 3A  shows method  300  for detecting and identifying noises in a sound signal and filtering and suppressing the noises in the sound signal. The method may be performed by a device such as mobile device  400 , UE  104 A and other UEs shown in  FIG. 1  such as WLAN STAs  152 A. Furthermore, mobile device  400  maybe similar to PC computers, laptops, tablets or other smart devices that are able to communicate with other UEs and mobile devices over the internet. 
     At block  302 , the method  300  joins a call. The call may be initiated by the user of mobile device  400  or UE  104 A or received from other one or more UEs including mobile devices. 
     At block  304 , the method  300  receives a sound signal including words spoken by the user of mobile device  400  and noises that may have occurred when the user was speaking into microphone  430 . Microphone  430  may receive the words spoken by the user of mobile device  400  and noises that may have occurred while the user was speaking into microphone  430 . Microphone  430  transmits the received sound signal to codec  402 . 
     At block  306 , the method  300  converts the received sound signal into a digital sound signal. Codec  402  may convert the sound signal received from microphone  430  into a digital sound signal. The analog sound signal from microphone  430  may be converted into a digital sound signal by codec  402 . Codec  402  transmits the converted digital sound signal to processor  465 . 
     At block  308 , the method  300  detects and identifies noises in the sound signal. Processor  465  and ML module  468  may detect and identify the noises in the sound signal received from codec  402 . ML module  486  may use trained artificial neural network to detect and identify the noises in the sound signal. 
     At block  310 , the method  300  displays the identified noises on a display. Processor  465  may display UI  200  including menu  210  that lists the identified noises in the sound signal. UI  200  may further display checkboxes that allows the user of mobile device  400  to select the noises that the user wants to filter and suppress. 
     At block  312 , the method  300  receives a selection of noises from the user. UI  200  may receive the noises that are selected by the user of mobile device  400  to be filtered and suppressed based on the checkboxes  225 A- 225 D that were checked by the user of mobile device  400 . UI  200  transmits the selection to processor  465 . 
     At block  314 , the method  300  filters and suppresses the noises chosen by the user. Processor  465  may filter and suppress the noises chosen by the user of mobile device  400 . Processor  465  may use DSP  464  to filter the chosen noises in the sound signal. The filtered sound signal is transmitted to transceiver  440 . 
     At block  316 , the method  300  transmits the filtered sound signal to other UEs and mobile devices on the call. Transceiver  440  transmits the filtered sound signal received from processor  465  to other UEs and mobile devices on the call with mobile device  400 . 
     In another aspect.  FIG. 3B  shows a method  350  for detecting and identifying noises in a sound signal received from another mobile device or UE and filtering and suppressing the noises in the received sound signal. The method may be performed by a device such as mobile device  400 , UE  104 A and other UEs shown in  FIG. 1  such as WLAN STAs  152 A. Furthermore, mobile device  400  maybe similar to PC computers, laptops, tablets or other smart devices that are able to communicate with other UEs and mobile devices over the internet. 
     At block  352 , the method  350  receives a sound signal from another UE or a mobile device during a call. Transceiver  440  may receive a signal that includes a sound signal from another UE or a mobile device. The signal may be received through antenna  442 . Transceiver  440  may transmit the received sound signal to processor  465 . 
     At block  354 , the method  350  detects and identifies noises in the received sound signal. Processor  465  and ML module  468  may detect and identify the noises in the sound signal received from transceiver  440 . ML module  468  may use trained artificial neural network to detect and identify the noises in the sound signal. 
     At block  356 , the method  350  displays the identified noises on a display. Processor  465  may display UI  250  including menu  260  that lists the identified noises in the sound signal. UI  250  may further display checkboxes  270 A- 270 D that allows the user of mobile device  400  to select the noises that the user wants to filter and suppress. 
     At block  358 , the method  350  receives a selection of noises from the user. UI  250  may receive the noises that are selected by the user of mobile device  400  to be filtered and suppressed based on the checkboxes  270 A- 270 D that were checked by the user. UI  250  transmits the selection to processor  465 . 
     At block  360 , the method  350  filters and suppresses the noises chosen by the user. Processor  465  may filter and suppress the noises chosen by the user of mobile device  400 . Processor  465  may use DSP  464  to filter the chosen noises in the sound signal. The filtered sound signal is transmitted to codec  402 . 
     At block  362 , the method  350  transmits the filtered sound signal to the speaker. Codec  402  may convert the filtered sound signal to an analog signal and transmit the analog signal to speaker  435 . Speaker  435  may play the filtered sound signal for the user&#39;s listening. Therefore, the user will hear a sound that does not include the noises chosen by the user. 
     In the detailed description above it can be seen that different features are grouped together in examples. This manner of disclosure should not be understood as an intention that the example clauses have more features than are explicitly mentioned in each clause. Rather, the various aspects of the disclosure may include fewer than all features of an individual example clause disclosed. Therefore, the following clauses should hereby be deemed to be incorporated in the description, wherein each clause by itself can stand as a separate example. Although each dependent clause can refer in the clauses to a specific combination with one of the other clauses, the aspect(s) of that dependent clause are not limited to the specific combination. It will be appreciated that other example clauses can also include a combination of the dependent clause aspect(s) with the subject matter of any other dependent clause or independent clause or a combination of any feature with other dependent and independent clauses. The various aspects disclosed herein expressly include these combinations, unless it is explicitly expressed or can be readily inferred that a specific combination is not intended (e.g., contradictory aspects, such as defining an element as both an insulator and a conductor). Furthermore, it is also intended that aspects of a clause can be included in any other independent clause, even if the clause is not directly dependent on the independent clause. 
     Implementation Examples are Described in the Following Numbered Clauses: 
     Clause 1. A method of suppressing noises in a sound signal in a mobile device, the method comprising: receiving the sound signal; detecting the noises in the received sound signal; identifying the noises in the received sound signal; displaying the identified noises in a user interface (UI); receiving a selection of the displayed identified noises from the UI; and filtering the received selection of the displayed identified noises from the received sound signal. 
     Clause 2. The method of clause 1, further comprising: utilizing a machine learning module with a neural network to detect and identify the noises in the received sound signal. 
     Clause 3. The method of clause 2, further comprising: transmitting the filtered sound signal. 
     Clause 4. The method of clause 3, wherein the sound signal is received from a microphone. 
     Clause 5. The method of any of clauses 3 to 4, wherein the sound signal is received from another mobile device. 
     Clause 6. The method of any of clauses 4 to 5, wherein the filtered sound signal is transmitted to another mobile device. 
     Clause 7. The method of any of clauses 5 to 6, wherein the filtered sound signal is transmitted to a speaker. 
     Clause 8. The method of any of clauses 2 to 7, further comprising: using a location of the mobile device to identify the noises in the received sound signal. 
     Clause 9. The method of any of clauses 4 to 8, further comprising: converting the received sound signal from an analog signal to a digital signal. 
     Clause 10. The method of any of clauses 7 to 9, further comprising: converting the filtered sound signal from a digital signal to an analog signal. 
     Clause 11. An apparatus comprising a memory and at least one processor communicatively coupled to the memory, the memory and the at least one processor configured to perform a method according to any of clauses 1 to 10. 
     Clause 12. An apparatus comprising means for performing a method according to any of clauses 1 to 10. 
     Clause 13. A non-transitory computer-readable medium storing computer-executable instructions, the computer-executable comprising at least one instruction for causing a computer or processor to perform a method according to any of clauses 1 to 10. 
     In one aspect, one or both of DSP  464  and processor  465 , in conjunction with one or more remaining components illustrated in  FIG. 4 , can include logic/means to operate a noise filtering and suppression system by detecting and identifying noises in sound signals that occur during calls and selectively filtering and suppressing the noises, using a machine learning module with a neural network to detect and identify the noises, and allowing a user to select the noises that the user wants to filter and suppress during calls, as shown, for example, in Blocks  302 - 362  of  FIGS. 3A and 3B . For example, DSP  464 , ML module  468  and/or processor  465  can include logic/means to implement functions related to receive a sound signal, detect noises in the received sound signal, identify the noises in the received sound signal, display the identified noises in a user interface (UI), receive a selection of the displayed identified noises from the UI, and filter the received selection of the displayed identified noises from the received sound signal. The logic/means is further configured to: utilize a machine learning module with a neural network to detect and identify the noises in the received sound signal and use a location of the mobile device to identify the noises in the received sound signal. 
     It should be noted that although  FIG. 4  depicts a wireless communications device, DSP  464 , processor  465 . ML module  468  and memory  240  may also be integrated into a device, selected from the group consisting of a set-top box, a music player, a video player, an entertainment unit, a navigation device, a communications device, a personal digital assistant (PDA), a fixed location data unit, laptop, tablet or a computer. Moreover, such a device may also be integrated in a semiconductor die. 
     Accordingly it will be appreciated from the foregoing that at least one aspect includes a mobile device having a memory and a processor configured to: receive a sound signal, detect noises in the received sound signal, identify the noises in the received sound signal, display the identified noises in a user interface (UI), receive a selection of the displayed identified noises from the UI, and filter the received selection of the displayed identified noises from the received sound signal. The processor is further configured to: utilize a machine learning module with a neural network to detect and identify the noises in the received sound signal and use a location of the mobile device to identify the noises in the received sound signal. 
     The various aspects disclosed advantageously allows the mobile device to detect and identify noises in sound signals that occur during calls and selectively filter and suppress the noises, utilize a machine learning module with a neural network to detect and identify the noises, and allow a user to select the noises that the user wants to filter and suppress during calls. 
     Those of skill in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     Further, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. 
     The methods, sequences and/or algorithms described in connection with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. 
     Accordingly, an aspect of the disclosure can include a computer readable media embodying a method for detecting and identifying noises in sound signals that occur during calls and selectively filtering and suppressing the noises, utilizing a machine learning module with a neural network to detect and identify the noises, and allowing a user to select the noises that the user wants to filter and suppress during calls. Accordingly, the disclosure is not limited to illustrated examples and any means for performing the functionality described herein are included in aspects of the disclosure. 
     While the foregoing disclosure shows illustrative aspects of the disclosure, it should be noted that various changes and modifications could be made herein without departing from the scope of the disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the aspects of the disclosure described herein need not be performed in any particular order. Furthermore, although elements of the disclosure may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. 
     The foregoing disclosed devices and methods are typically designed and are configured into GDSII and GERBER computer files, stored on a computer readable media. These files are in turn provided to fabrication handlers who fabricate devices based on these files. The resulting products are semiconductor wafers that are then cut into semiconductor die and packaged into a semiconductor chip. The chips are then employed in devices described above.