PATENT DOCUMENT

Publication Number: US-10446167-B2
Application Number: US-201414165523-A
Country: US
Kind Code: B2

Title: User-specific noise suppression for voice quality improvements

Abstract:
Systems, methods, and devices for user-specific noise suppression are provided. For example, when a voice-related feature of an electronic device is in use, the electronic device may receive an audio signal that includes a user voice. Since noise, such as ambient sounds, also may be received by the electronic device at this time, the electronic device may suppress such noise in the audio signal. In particular, the electronic device may suppress the noise in the audio signal while substantially preserving the user voice via user-specific noise suppression parameters. These user-specific noise suppression parameters may be based at least in part on a user noise suppression preference or a user voice profile, or a combination thereof.

Claims:
What is claimed is: 
     
       1. A method, comprising:
 at an electronic device with one or more processors and memory:
 receiving an audio signal that includes a voice of a near-end user of the electronic device when a voice-related feature of the electronic device is in use; 
 suppressing noise in the audio signal using the electronic device while substantially preserving the voice of the near-end user based at least in part on user-specific noise suppression parameters,
 wherein the user-specific noise suppression parameters are based at least in part on a user noise suppression preference, 
 wherein the user noise suppression preference is based on a user selection between a first filtered audio signal generated by applying a first set of noise suppression parameters to a test audio signal and a second filtered audio signal generated by applying a second set of noise suppression parameters to the test audio signal, and 
 wherein the test audio signal includes speech of the near-end user and one or more distracters, the test audio signal being output by the electronic device responsive to receiving a user input indicating initiation of a voice training mode of the electronic device; and 
 
 transmitting the audio signal to a remote device for receipt by a far-end user. 
 
 
     
     
       2. The method of  claim 1 , wherein the user-specific noise suppression parameters suppress noise in the audio signal while substantially preserving the voice at least in part by amplifying frequencies associated with a user voice profile. 
     
     
       3. The method of  claim 1 , wherein the user-specific noise suppression parameters suppress noise in the audio signal while substantially preserving the voice at least in part by suppressing frequencies not associated with a user voice profile. 
     
     
       4. The method of  claim 1 , wherein a first noise suppression strength associated with the first set of noise suppression parameters is greater than a second noise suppression strength associated with the second set of noise suppression parameters. 
     
     
       5. The method of  claim 1 , further comprising:
 determining a user voice profile based on the audio signal; and 
 determining the user-specific noise suppression parameters based on the user voice profile. 
 
     
     
       6. The method of  claim 1 , further comprising:
 adjusting the user-specific noise suppression parameters based on user input received while receiving the audio signal. 
 
     
     
       7. The method of  claim 1 , wherein the first filtered audio signal is generated after applying a third set of noise suppression parameters to the test audio signal and wherein the second filtered audio signal is generated after applying a fourth set of noise suppression parameters to the test audio signal. 
     
     
       8. The method of  claim 1 , wherein the user selection includes a selection of a displayed menu item. 
     
     
       9. The method of  claim 1 , wherein the user selection is responsive to a prompt, output by the electronic device, prompting user selection between the first filtered audio signal and the second filtered audio signal. 
     
     
       10. The method of  claim 1 , wherein the user noise suppression preference is based at least in part on a user noise suppression training sequence. 
     
     
       11. The method of  claim 10 , wherein the user noise suppression training sequence comprises receiving at the electronic device a user selection of preferred noise parameters after noise suppression parameters have been tested on a second test audio signal and played back to the near-end user. 
     
     
       12. The method of  claim 10 , wherein the user noise suppression training sequence comprises testing noise suppression parameters as applied to a test audio signal that includes a voice sample of the near-end user and at least one distractor. 
     
     
       13. The method of  claim 1 , wherein the user noise suppression preference is further based on a user-selected noise suppression setting. 
     
     
       14. The method of  claim 13 , wherein the user-selected noise suppression setting comprises a noise suppression strength setting. 
     
     
       15. The method of  claim 13 , wherein the user-selected noise suppression setting is user selectable in real time while the voice-related feature of the electronic device is in use. 
     
     
       16. An electronic device, comprising:
 one or more processors; 
 one or more microphones; and 
 memory storing one or more programs for execution by the at least one processor, the one or more programs including instructions for:
 receiving an audio signal that includes a voice of a near-end user of the electronic device when a voice-related feature of the electronic device is in use; 
 suppressing noise in the audio signal using the electronic device while substantially preserving the voice of the near-end user based at least in part on user-specific noise suppression parameters,
 wherein the user-specific noise suppression parameters are based at least in part on a user noise suppression preference, 
 wherein the user noise suppression preference is based on a user selection between a first filtered audio signal generated by applying a first set of noise suppression parameters to a test audio signal and a second filtered audio signal generated by applying a second set of noise suppression parameters to the test audio signal, and 
 wherein the test audio signal includes speech of the near-end user and one or more distractors, the test audio signal being output by the electronic device responsive to receiving a user input indicating initiation of a voice training mode of the electronic device; and 
 
 transmitting the audio signal to a remote device for receipt by a far-end user. 
 
 
     
     
       17. The electronic device of  claim 16 , wherein the user noise suppression preference is further based on a user-selected noise suppression setting, and
 wherein the user-selected noise suppression setting is user selectable in real time while the voice-related feature of the electronic device is in use. 
 
     
     
       18. The electronic device of  claim 16 , wherein the user-specific noise suppression parameters suppress noise in the audio signal while substantially preserving the voice at least in part by amplifying frequencies associated with a user voice profile. 
     
     
       19. The electronic device of  claim 16 , wherein the user-specific noise suppression parameters suppress noise in the audio signal while substantially preserving the voice at least in part by suppressing frequencies not associated with a user voice profile. 
     
     
       20. The electronic device of  claim 16 , wherein the one or more programs further include instructions for:
 determining a user voice profile based on the audio signal; and 
 determining the user-specific noise suppression parameters based on the user voice profile. 
 
     
     
       21. The electronic device of  claim 16 , wherein the user selection is responsive to a prompt, output by the electronic device, prompting user selection between the first filtered audio signal and the second filtered audio signal. 
     
     
       22. A non-transitory computer-readable storage medium, storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for:
 receiving an audio signal that includes a voice of a near-end user of the electronic device when a voice-related feature of the electronic device is in use; 
 suppressing noise in the audio signal using the electronic device while substantially preserving the voice of the near-end user based at least in part on user-specific noise suppression parameters,
 wherein the user-specific noise suppression parameters are based at least in part on a user noise suppression preference, 
 wherein the user noise suppression preference is based on a user selection between a first filtered audio signal generated by applying a first set of noise suppression parameters to a test audio signal and a second filtered audio signal generated by applying a second set of noise suppression parameters to the test audio signal, and 
 wherein the test audio signal includes speech of the near-end user and one or more distracters, the test audio signal being output by the electronic device responsive to receiving a user input indicating initiation of a voice training mode of the electronic device; and 
 
 transmitting the audio signal to a remote device for receipt by a far-end user. 
 
     
     
       23. The non-transitory computer-readable storage medium of  claim 22 , wherein the user noise suppression preference is further based on a user-selected noise suppression setting, and
 wherein the user-selected noise suppression setting is user selectable in real time while the voice-related feature of the electronic device is in use. 
 
     
     
       24. The non-transitory computer-readable storage medium of  claim 22 , wherein the user-specific noise suppression parameters suppress noise in the audio signal while substantially preserving the voice at least in part by amplifying frequencies associated with a user voice profile. 
     
     
       25. The non-transitory computer-readable storage medium of  claim 22 , wherein the user-specific noise suppression parameters suppress noise in the audio signal while substantially preserving the voice at least in part by suppressing frequencies not associated with a user voice profile. 
     
     
       26. The non-transitory computer-readable storage medium of  claim 22 , wherein the one or more programs further include instructions for:
 determining a user voice profile based on the audio signal; and 
 determining the user-specific noise suppression parameters based on the user voice profile. 
 
     
     
       27. The non-transitory computer-readable storage medium of  claim 22 , wherein the user selection is responsive to a prompt, output by the electronic device, prompting user selection between the first filtered audio signal and the second filtered audio signal. 
     
     
       28. An electronic device, comprising:
 one or more processors; 
 one or more microphones; and 
 memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for:
 determining whether a signal-to-noise ratio of a first audio signal obtained while a voice-related feature of the electronic device is in use exceeds a threshold; 
 in accordance with a determination that the signal-to-noise ratio exceeds the threshold, obtaining a user voice sample from the first audio signal; 
 determining a user voice profile based at least in part on the user voice sample; 
 determining user-specific noise suppression parameters based at least in part on the user voice profile; 
 obtaining a second audio signal that includes a user voice and ambient sounds; and 
 applying noise suppression to the second audio signal based at least in part on the user-specific noise suppression parameters to suppress the ambient sounds of the second audio signal. 
 
 
     
     
       29. The electronic device of  claim 28 , wherein the one or more programs further include instructions for:
 determining the user voice profile further based at least in part on another user voice sample obtained during an activation period of the electronic device. 
 
     
     
       30. The electronic device of  claim 28 , wherein the one or more programs further include instructions for:
 determining whether the user voice corresponds to a known user and, when the user voice corresponds to the known user, recalling the user voice profile associated with the user voice. 
 
     
     
       31. The electronic device of  claim 28 , wherein the one or more programs further include instructions for:
 determining whether the user voice corresponds to a known user, wherein determining whether the signal-to-noise ratio of the first audio signal obtained while the voice-related feature of the electronic device is in use exceeds the threshold is performed in accordance with a determination that the user voice does not correspond to the known user. 
 
     
     
       32. A method, comprising:
 at an electronic device with one or more processors and memory:
 determining whether a signal-to-noise ratio of a first audio signal obtained while a voice-related feature of the electronic device is in use exceeds a threshold; 
 in accordance with a determination that the signal-to-noise ratio exceeds the threshold, obtaining a user voice sample from the first audio signal; 
 determining a user voice profile based at least in part on the user voice sample; 
 determining user-specific noise suppression parameters based at least in part on the user voice profile; 
 obtaining a second audio signal that includes a user voice and ambient sounds; and 
 applying noise suppression to the second audio signal based at least in part on the user-specific noise suppression parameters to suppress the ambient sounds of the second audio signal. 
 
 
     
     
       33. The method of  claim 32 , further comprising:
 determining the user voice profile further based at least in part on another user voice sample obtained during an activation period of the electronic device. 
 
     
     
       34. The method of  claim 32 , further comprising:
 determining whether the user voice corresponds to a known user and, when the user voice corresponds to the known user, recalling the user voice profile associated with the user voice. 
 
     
     
       35. The method of  claim 32 , further comprising:
 determining whether the user voice corresponds to a known user, wherein determining whether the signal-to-noise ratio of the first audio signal obtained while the voice-related feature of the electronic device is in use exceeds the threshold is performed in accordance with a. determination that the user voice does not correspond to the known user. 
 
     
     
       36. A non-transitory computer-readable storage medium, storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for:
 determining whether a signal-to-noise ratio of a first audio signal obtained while a voice-related feature of the electronic device is in use exceeds a threshold; 
 in accordance with a determination that the signal-to-noise ratio exceeds the threshold, obtaining a user voice sample from the first audio signal; 
 determining a user voice profile based at least in part on the user voice sample; 
 determining user-specific noise suppression parameters based at least in part on the user voice profile; 
 obtaining a second audio signal that includes a user voice and ambient sounds; and 
 applying noise suppression to the second audio signal based at least in part on the user-specific noise suppression parameters to suppress the ambient sounds of the second audio signal. 
 
     
     
       37. The non-transitory computer-readable storage medium of  claim 36 , wherein the one or more programs further include instructions for:
 determining the user voice profile further based at least in part on another user voice sample obtained during an activation period of the electronic device. 
 
     
     
       38. The non-transitory computer-readable storage medium of  claim 36 , wherein the one or more programs further include instructions for:
 determining whether the user voice corresponds to a known user and, when the user voice corresponds to the known user, recalling the user voice profile associated with the user voice. 
 
     
     
       39. The non-transitory computer-readable storage medium of  claim 36 , wherein the one or more programs further include instructions for:
 determining whether the user voice corresponds to a known user, wherein determining whether the signal-to-noise ratio of the first audio signal obtained while the voice-related feature of the electronic device is in use exceeds the threshold is performed in accordance with a determination that the user voice does not correspond to the known user.

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 12/794,643, filed Jun. 4, 2010, which application is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     The present disclosure relates generally to techniques for noise suppression and, more particularly, for user-specific noise suppression. 
     This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art. 
     Many electronic devices employ voice-related features that involve recording and/or transmitting a user&#39;s voice. Voice note recording features, for example, may record voice notes spoken by the user. Similarly, a telephone feature of an electronic device may transmit the user&#39;s voice to another electronic device. When an electronic device obtains a user&#39;s voice, however, ambient sounds or background noise may be obtained at the same time. These ambient sounds may obscure the user&#39;s voice and, in some cases, may impede the proper functioning of a voice-related feature of the electronic device. 
     To reduce the effect of ambient sounds when a voice-related feature is in use, electronic devices may apply a variety of noise suppression schemes. Device manufactures may program such noise suppression schemes to operate according to certain predetermined generic parameters calculated to be well-received by most users. However, certain voices may be less well suited for these generic noise suppression parameters. Additionally, some users may prefer stronger or weaker noise suppression. 
     SUMMARY 
     A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. 
     Embodiments of the present disclosure relate to systems, methods, and devices for user-specific noise suppression. For example, when a voice-related feature of an electronic device is in use, the electronic device may receive an audio signal that includes a user voice. Since noise, such as ambient sounds, also may be received by the electronic device at this time, the electronic device may suppress such noise in the audio signal. In particular, the electronic device may suppress the noise in the audio signal while substantially preserving the user voice via user-specific noise suppression parameters. These user-specific noise suppression parameters may be based at least in part on a user noise suppression preference or a user voice profile, or a combination thereof. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which: 
         FIG. 1  is a block diagram of an electronic device capable of performing the techniques disclosed herein, in accordance with an embodiment; 
         FIG. 2  is a schematic view of a handheld device representing one embodiment of the electronic device of  FIG. 1 ; 
         FIG. 3  is a schematic block diagram representing various context in which a voice-related feature of the electronic device of  FIG. 1  may be used, in accordance with an embodiment; 
         FIG. 4  is a block diagram of noise suppression that may take place in the electronic device of  FIG. 1 , in accordance with an embodiment; 
         FIG. 5  is a block diagram representing user-specific noise suppression parameters, in accordance with an embodiment; 
         FIG. 6  is a flow chart describing an embodiment of a method for applying user-specific noise suppression parameters in the electronic device of  FIG. 1 ; 
         FIG. 7  is a schematic diagram of the initiation of a voice training sequence when the handheld device of  FIG. 2  is activated, in accordance with an embodiment; 
         FIG. 8  is a schematic diagram of a series of screens for selecting the initiation of a voice training sequence using the handheld device of  FIG. 2 , in accordance with an embodiment; 
         FIG. 9  is a flowchart describing an embodiment of a method for determining user-specific noise suppression parameters via a voice training sequence; 
         FIGS. 10 and 11  are schematic diagrams for a manner of obtaining a user voice sample for voice training, in accordance with an embodiment; 
         FIG. 12  is a schematic diagram illustrating a manner of obtaining a noise suppression user preference during a voice training sequence, in accordance with an embodiment; 
         FIG. 13  is a flowchart describing an embodiment of a method for obtaining noise suppression user preferences during a voice training sequence; 
         FIG. 14  is a flowchart describing an embodiment of another method for performing a voice training sequence; 
         FIG. 15  is a flowchart describing an embodiment of a method for obtaining a high signal-to-noise ratio (SNR) user voice sample; 
         FIG. 16  is a flowchart describing an embodiment of a method for determining user-specific noise suppression parameters via analysis of a user voice sample; 
         FIG. 17  is a factor diagram describing characteristics of a user voice sample that may be considered while performing the method of  FIG. 16 , in accordance with an embodiment; 
         FIG. 18  is a schematic diagram representing a series of screens that may be displayed on the handheld device of  FIG. 2  to obtain a user-specific noise parameters via a user-selectable setting, in accordance with an embodiment; 
         FIG. 19  is a schematic diagram of a screen on the handheld device of  FIG. 2  for obtaining user-specified noise suppression parameters in real-time while a voice-related feature of the handheld device is in use, in accordance with an embodiment; 
         FIGS. 20 and 21  are schematic diagrams representing various sub-parameters that may form the user-specific noise suppression parameters, in accordance with an embodiment; 
         FIG. 22  is a flowchart describing an embodiment of a method for applying certain sub-parameters of the user-specific parameters based on detected ambient sounds; 
         FIG. 23  is a flowchart describing an embodiment of a method for applying certain sub-parameters of the noise suppression parameters based on a context of use of the electronic device; 
         FIG. 24  is a factor diagram representing a variety of device context factors that may be employed in the method of  FIG. 23 , in accordance with an embodiment; 
         FIG. 25  is a flowchart describing an embodiment of a method for obtaining a user voice profile; 
         FIG. 26  is a flowchart describing an embodiment of a method for applying noise suppression based on a user voice profile; 
         FIGS. 27-29  are plots depicting a manner of performing noise suppression of an audio signal based on a user voice profile, in accordance with an embodiment; 
         FIG. 30  is a flowchart describing an embodiment of a method for obtaining user-specific noise suppression parameters via a voice training sequence involving per-recorded voices; 
         FIG. 31  is a flowchart describing an embodiment of a method for applying user-specific noise suppression parameters to audio received from another electronic device; 
         FIG. 32  is a flowchart describing an embodiment of a method for causing another electronic device to engage in noise suppression based on the user-specific noise parameters of a first electronic device, in accordance with an embodiment; and 
         FIG. 33  is a schematic block diagram of a system for performing noise suppression on two electronic devices based on user-specific noise suppression parameters associated with the other electronic device, in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS 
     One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     Present embodiments relate to suppressing noise in an audio signal associated with a voice-related feature of an electronic device. Such a voice-related feature may include, for example, a voice note recording feature, a video recording feature, a telephone feature, and/or a voice command feature, each of which may involve an audio signal that includes a user&#39;s voice. In addition to the user&#39;s voice, however, the audio signal also may include ambient sounds present while the voice-related feature is in use. Since these ambient sounds may obscure the user&#39;s voice, the electronic device may apply noise suppression to the audio signal to filter out the ambient sounds while preserving the user&#39;s voice. 
     Rather than employ generic noise suppression parameters programmed at the manufacture of the device, noise suppression according to present embodiments may involve user-specific noise suppression parameters that may be unique to a user of the electronic device. These user-specific noise suppression parameters may be determined through voice training, based on a voice profile of the user, and/or based on a manually selected user setting. When noise suppression takes place based on user-specific parameters rather than generic parameters, the sound of the noise-suppressed signal may be more satisfying to the user. These user-specific noise suppression parameters may be employed in any voice-related feature, and may be used in connection with automatic gain control (AGC) and/or equalization (EQ) tuning. 
     As noted above, the user-specific noise suppression parameters may be determined using a voice training sequence. In such a voice training sequence, the electronic device may apply varying noise suppression parameters to a user&#39;s voice sample mixed with one or more distractors (e.g., simulated ambient sounds such as crumpled paper, white noise, babbling people, and so forth). The user may thereafter indicate which noise suppression parameters produce the most preferable sound. Based on the user&#39;s feedback, the electronic device may develop and store the user-specific noise suppression parameters for later use when a voice-related feature of the electronic device is in use. 
     Additionally or alternatively, the user-specific noise suppression parameters may be determined by the electronic device automatically depending on characteristics of the user&#39;s voice. Different users&#39; voices may have a variety of different characteristics, including different average frequencies, different variability of frequencies, and/or different distinct sounds. Moreover, certain noise suppression parameters may be known to operate more effectively with certain voice characteristics. Thus, an electronic device according to certain present embodiments may determine the user-specific noise suppression parameters based on such user voice characteristics. In some embodiments, a user may manually set the noise suppression parameters by, for example, selecting a high/medium/low noise suppression strength selector or indicating a current call quality on the electronic device. 
     When the user-specific parameters have been determined, the electronic device may suppress various types of ambient sounds that may be heard while a voice-related feature is being used. In certain embodiments, the electronic device may analyze the character of the ambient sounds and apply a user-specific noise suppression parameter that is expected to thus suppress the current ambient sounds. In another embodiment, the electronic device may apply certain user-specific noise suppression parameters based on the current context in which the electronic device is being used. 
     In certain embodiments, the electronic device may perform noise suppression tailored to the user based on a user voice profile associated with the user. Thereafter, the electronic device may more effectively isolate ambient sounds from an audio signal when a voice-related feature is being used because the electronic device generally may expect which components of an audio signal correspond to the user&#39;s voice. For example, the electronic device may amplify components of an audio signal associated with a user voice profile while suppressing components of the audio signal not associated with the user voice profile. 
     User-specific noise suppression parameters also may be employed to suppress noise in audio signals containing voices other than that of the user that are received by the electronic device. For example, when the electronic device is used for a telephone or chat feature, the electronic device may employ the user-specific noise suppression parameters to an audio signal from a person with whom the user is corresponding. Since such an audio signal may have been previously processed by the sending device, such noise suppression may be relatively minor. In certain embodiments, the electronic device may transmit the user-specific noise suppression parameters to the sending device, so that the sending device may modify its noise suppression parameters accordingly. In the same way, two electronic devices may function systematically to suppress noise in outgoing audio signals according to each other&#39;s user-specific noise suppression parameters. 
     With the foregoing in mind, a general description of suitable electronic devices for performing the presently disclosed techniques is provided below. In particular,  FIG. 1  is a block diagram depicting various components that may be present in an electronic device suitable for use with the present techniques.  FIG. 2  represents one example of a suitable electronic device, which may be, as illustrated, a handheld electronic device having noise suppression capabilities. 
     Turning first to  FIG. 1 , an electronic device  10  for performing the presently disclosed techniques may include, among other things, one or more processor(s)  12 , memory  14 , nonvolatile storage  16 , a display  18 , noise suppression  20 , location-sensing circuitry  22 , an input/output (I/O) interface  24 , network interfaces  26 , image capture circuitry  28 , accelerometers/magnetometer  30 , and a microphone  32 . The various functional blocks shown in  FIG. 1  may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium) or a combination of both hardware and software elements. It should further be noted that  FIG. 1  is merely one example of a particular implementation and is intended to illustrate the types of components that may be present in electronic device  10 . 
     By way of example, the electronic device  10  may represent a block diagram of the handheld device depicted in  FIG. 2  or similar devices. Additionally or alternatively, the electronic device  10  may represent a system of electronic devices with certain characteristics. For example, a first electronic device may include at least a microphone  32 , which may provide audio to a second electronic device including the processor(s)  12  and other data processing circuitry. It should be noted that the data processing circuitry may be embodied wholly or in part as software, firmware, hardware or any combination thereof. Furthermore the data processing circuitry may be a single contained processing module or may be incorporated wholly or partially within any of the other elements within electronic device  10 . The data processing circuitry may also be partially embodied within electronic device  10  and partially embodied within another electronic device wired or wirelessly connected to device  10 . Finally, the data processing circuitry may be wholly implemented within another device wired or wirelessly connected to device  10 . As a non-limiting example, data processing circuitry might be embodied within a headset in connection with device  10 . 
     In the electronic device  10  of  FIG. 1 , the processor(s)  12  and/or other data processing circuitry may be operably coupled with the memory  14  and the nonvolatile memory  16  to perform various algorithms for carrying out the presently disclosed techniques. Such programs or instructions executed by the processor(s)  12  may be stored in any suitable manufacture that includes one or more tangible, computer-readable media at least collectively storing the instructions or routines, such as the memory  14  and the nonvolatile storage  16 . Also, programs (e.g., an operating system) encoded on such a computer program product may also include instructions that may be executed by the processor(s)  12  to enable the electronic device  10  to provide various functionalities, including those described herein. The display  18  may be a touch-screen display, which may enable users to interact with a user interface of the electronic device  10 . 
     The noise suppression  20  may be performed by data processing circuitry such as the processor(s)  12  or by circuitry dedicated to performing certain noise suppression on audio signals processed by the electronic device  10 . For example, the noise suppression  20  may be performed by a baseband integrated circuit (IC), such as those manufactured by Infineon, based on externally provided noise suppression parameters. Additionally or alternatively, the noise suppression  20  may be performed in a telephone audio enhancement integrated circuit (IC) configured to perform noise suppression based on externally provided noise suppression parameters, such as those manufactured by Audience. These noise suppression ICs may operate at least partly based on certain noise suppression parameters. Varying such noise suppression parameters may vary the output of the noise suppression  20 . 
     The location-sensing circuitry  22  may represent device capabilities for determining the relative or absolute location of electronic device  10 . By way of example, the location-sensing circuitry  22  may represent Global Positioning System (GPS) circuitry, algorithms for estimating location based on proximate wireless networks, such as local Wi-Fi networks, and so forth. The I/O interface  24  may enable electronic device  10  to interface with various other electronic devices, as may the network interfaces  26 . The network interfaces  26  may include, for example, interfaces for a personal area network (PAN), such as a Bluetooth network, for a local area network (LAN), such as an 802.11x Wi-Fi network, and/or for a wide area network (WAN), such as a 3G cellular network. Through the network interfaces  26 , the electronic device  10  may interface with a wireless headset that includes a microphone  32 . The image capture circuitry  28  may enable image and/or video capture, and the accelerometers/magnetometer  30  may observe the movement and/or a relative orientation of the electronic device  10 . 
     When employed in connection with a voice-related feature of the electronic device  10 , such as a telephone feature or a voice recognition feature, the microphone  32  may obtain an audio signal of a user&#39;s voice. Though ambient sounds may also be obtained in the audio signal in addition to the user&#39;s voice, the noise suppression  20  may process the audio signal to exclude most ambient sounds based on certain user-specific noise suppression parameters. As described in greater detail below, the user-specific noise suppression parameters may be determined through voice training, based on a voice profile of the user, and/or based on a manually selected user setting. 
       FIG. 2  depicts a handheld device  34 , which represents one embodiment of the electronic device  10 . The handheld device  34  may represent, for example, a portable phone, a media player, a personal data organizer, a handheld game platform, or any combination of such devices. By way of example, the handheld device  34  may be a model of an iPod® or iPhone® available from Apple Inc. of Cupertino, Calif. 
     The handheld device  34  may include an enclosure  36  to protect interior components from physical damage and to shield them from electromagnetic interference. The enclosure  36  may surround the display  18 , which may display indicator icons  38 . The indicator icons  38  may indicate, among other things, a cellular signal strength, Bluetooth connection, and/or battery life. The I/O interfaces  24  may open through the enclosure  36  and may include, for example, a proprietary I/O port from Apple Inc. to connect to external devices. As indicated in  FIG. 2 , the reverse side of the handheld device  34  may include the image capture circuitry  28 . 
     User input structures  40 ,  42 ,  44 , and  46 , in combination with the display  18 , may allow a user to control the handheld device  34 . For example, the input structure  40  may activate or deactivate the handheld device  34 , the input structure  42  may navigate user interface  20  to a home screen, a user-configurable application screen, and/or activate a voice-recognition feature of the handheld device  34 , the input structures  44  may provide volume control, and the input structure  46  may toggle between vibrate and ring modes. The microphone  32  may obtain a user&#39;s voice for various voice-related features, and a speaker  48  may enable audio playback and/or certain phone capabilities. Headphone input  50  may provide a connection to external speakers and/or headphones. 
     As illustrated in  FIG. 2 , a wired headset  52  may connect to the handheld device  34  via the headphone input  50 . The wired headset  52  may include two speakers  48  and a microphone  32 . The microphone  32  may enable a user to speak into the handheld device  34  in the same manner as the microphones  32  located on the handheld device  34 . In some embodiments, a button near the microphone  32  may cause the microphone  32  to awaken and/or may cause a voice-related feature of the handheld device  34  to activate. A wireless headset  54  may similarly connect to the handheld device  34  via a wireless interface (e.g., a Bluetooth interface) of the network interfaces  26 . Like the wired headset  52 , the wireless headset  54  may also include a speaker  48  and a microphone  32 . Also, in some embodiments, a button near the microphone  32  may cause the microphone  32  to awaken and/or may cause a voice-related feature of the handheld device  34  to activate. Additionally or alternatively, a standalone microphone  32  (not shown), which may lack an integrated speaker  48 , may interface with the handheld device  34  via the headphone input  50  or via one of the network interfaces  26 . 
     A user may use a voice-related feature of the electronic device  10 , such as a voice-recognition feature or a telephone feature, in a variety of contexts with various ambient sounds.  FIG. 3  illustrates many such contexts  56  in which the electronic device  10 , depicted as the handheld device  34 , may obtain a user voice audio signal  58  and ambient sounds  60  while performing a voice-related feature. By way of example, the voice-related feature of the electronic device  10  may include, for example, a voice recognition feature, a voice note recording feature, a video recording feature, and/or a telephone feature. The voice-related feature may be implemented on the electronic device  10  in software carried out by the processor(s)  12  or other processors, and/or may be implemented in specialized hardware. 
     When the user speaks the voice audio signal  58 , it may enter the microphone  32  of the electronic device  10 . At approximately the same time, however, ambient sounds  60  also may enter the microphone  32 . The ambient sounds  60  may vary depending on the context  56  in which the electronic device  10  is being used. The various contexts  56  in which the voice-related feature may be used may include at home  62 , in the office  64 , at the gym  66 , on a busy street  68 , in a car  70 , at a sporting event  72 , at a restaurant  74 , and at a party  76 , among others. As should be appreciated, the typical ambient sounds  60  that occur on a busy street  68  may differ greatly from the typical ambient sounds  60  that occur at home  62  or in a car  70 . 
     The character of the ambient sounds  60  may vary from context  56  to context  56 . As described in greater detail below, the electronic device  10  may perform noise suppression  20  to filter the ambient sounds  60  based at least partly on user-specific noise suppression parameters. In some embodiments, these user-specific noise suppression parameters may be determined via voice training, in which a variety of different noise suppression parameters may be tested on an audio signal including a user voice sample and various distractors (simulated ambient sounds). The distractors employed in voice training may be chosen to mimic the ambient sounds  60  found in certain contexts  56 . Additionally, each of the contexts  56  may occur at certain locations and times, with varying amounts of electronic device  10  motion and ambient light, and/or with various volume levels of the voice signal  58  and the ambient sounds  60 . Thus, the electronic device  10  may filter the ambient sounds  60  using user-specific noise suppression parameters tailored to certain contexts  56 , as determined based on time, location, motion, ambient light, and/or volume level, for example. 
       FIG. 4  is a schematic block diagram of a technique  80  for performing the noise suppression  20  on the electronic device  10  when a voice-related feature of the electronic device  10  is in use. In the technique  80  of  FIG. 4 , the voice-related feature involves two-way communication between a user and another person and may take place when a telephone or chat feature of the electronic device  10  is in use. However, it should be appreciated that the electronic device  10  also may perform the noise suppression  20  on an audio signal either received through the microphone  32  or the network interface  26  of the electronic device when two-way communication is not occurring. 
     In the noise suppression technique  80 , the microphone  32  of the electronic device  10  may obtain a user voice signal  58  and ambient sounds  60  present in the background. This first audio signal may be encoded by a codec  82  before entering noise suppression  20 . In the noise suppression  20 , transmit noise suppression (TX NS)  84  may be applied to the first audio signal. The manner in which noise suppression  20  occurs may be defined by certain noise suppression parameters (illustrated as transmit noise suppression (TX NS) parameters  86 ) provided by the processor(s)  12 , memory  14 , or nonvolatile storage  16 , for example. As discussed in greater detail below, the TX NS parameters  86  may be user-specific noise suppression parameters determined by the processor(s)  12  and tailored to the user and/or context  56  of the electronic device  10 . After performing the noise suppression  20  at numeral  84 , the resulting signal may be passed to an uplink  88  through the network interface  26 . 
     A downlink  90  of the network interface  26  may receive a voice signal from another device (e.g., another telephone). Certain noise receiver noise suppression (RX NS)  92  may be applied to this incoming signal in the noise suppression  20 . The manner in which such noise suppression  20  occurs may be defined by certain noise suppression parameters (illustrated as receive noise suppression (RX NS) parameters  94 ) provided by the processor(s)  12 , memory  14 , or nonvolatile storage  16 , for example. Since the incoming audio signal previously may have been processed for noise suppression before leaving the sending device, the RX NS parameters  94  may be selected to be less strong than the TX NS parameters  86 . The resulting noise-suppressed signal may be decoded by the codec  82  and output to receiver circuitry and/or a speaker  48  of the electronic device  10 . 
     The TX NS parameters  86  and/or the RX NS parameters  94  may be specific to the user of the electronic device  10 . That is, as shown by a diagram  100  of  FIG. 5 , the TX NS parameters  86  and the RX NS parameters  94  may be selected from user-specific noise suppression parameters  102  that are tailored to the user of the electronic device  10 . These user-specific noise suppression parameters  102  may be obtained in a variety of ways, such as through voice training  104 , based on a user voice profile  106 , and/or based on user-selectable settings  108 , as described in greater detail below. 
     Voice training  104  may allow the electronic device  10  to determine the user-specific noise suppression parameters  102  by way of testing a variety of noise suppression parameters combined with various distractors or simulated background noise. Certain embodiments for performing such voice training  104  are discussed in greater detail below with reference to  FIGS. 7-14 . Additionally or alternatively, the electronic device  10  may determine the user-specific noise suppression parameters  102  based on a user voice profile  106  that may consider specific characteristics of the user&#39;s voice, as discussed in greater detail below with reference to  FIGS. 15-17 . Additionally or alternatively, a user may indicate preferences for the user-specific noise suppression parameters  102  through certain user settings  108 , as discussed in greater detail below with reference to  FIGS. 18 and 19 . Such user-selectable settings may include, for example, a noise suppression strength (e.g., low/medium/high) selector and/or a real-time user feedback selector to provide user feedback regarding the user&#39;s real-time voice quality. 
     In general, the electronic device  10  may employ the user-specific noise suppression parameters  102  when a voice-related feature of the electronic device is in use (e.g., the TX NS parameters  86  and the RX NS parameters  94  may be selected based on the user-specific noise suppression parameters  102 ). In certain embodiments, the electronic device  10  may apply certain user-specific noise suppression parameters  102  during noise suppression  20  based on an identification of the user who is currently using the voice-related feature. Such a situation may occur, for example, when an electronic device  10  is used by other family members. Each member of the family may represent a user that may sometimes use a voice-related feature of the electronic device  10 . Under such multi-user conditions, the electronic device  10  may ascertain whether there are user-specific noise suppression parameters  102  associated with that user. 
     For example,  FIG. 6  illustrates a flowchart  110  for applying certain user-specific noise suppression parameters  102  when a user has been identified. The flowchart  110  may begin when a user is using a voice-related feature of the electronic device  10  (block  112 ). In carrying out the voice-related feature, the electronic device  10  may receive an audio signal that includes a user voice signal  58  and ambient sounds  60 . From the audio signal, the electronic device  10  generally may determine certain characteristics of the user&#39;s voice and/or may identify a user voice profile from the user voice signal  58  (block  114 ). As discussed below, a user voice profile may represent information that identifies certain characteristics associated with the voice of a user. 
     If the voice profile detected at block  114  does not match any known users with whom user-specific noise suppression parameters  102  are associated (block  116 ), the electronic device  10  may apply certain default noise suppression parameters for noise suppression  20  (block  118 ). However, if the voice profile detected in block  114  does match a known user of the electronic device  10 , and the electronic device  10  currently stores user-specific noise suppression parameters  102  associated with that user, the electronic device  10  may instead apply the associated user-specific noise suppression parameters  102  (block  120 ). 
     As mentioned above, the user-specific noise suppression parameters  102  may be determined based on a voice training sequence  104 . The initiation of such a voice training sequence  104  may be presented as an option to a user during an activation phase  130  of an embodiment of the electronic device  10 , such as the handheld device  34 , as shown in  FIG. 7 . In general, such an activation phase  130  may take place when the handheld device  34  first joins a cellular network or first connects to a computer or other electronic device  132  via a communication cable  134 . During such an activation phase  130 , the handheld device  34  or the computer or other device  132  may provide a prompt  136  to initiate voice training. Upon selection of the prompt, a user may initiate the voice training  104 . 
     Additionally or alternatively, a voice training sequence  104  may begin when a user selects a setting of the electronic device  10  that causes the electronic device  10  to enter a voice training mode. As shown in  FIG. 8 , a home screen  140  of the handheld device  34  may include a user-selectable button  142  that, when selected causes the handheld device  34  to display a settings screen  144 . When a user selects a user-selectable button  146  labeled “phone” on the settings screen  144 , the handheld device  34  may display a phone settings screen  148 . The phone settings screen  148  may include, among other things, a user-selectable button  150  labeled “voice training” When a user selects the voice training button  150 , a voice training  104  sequence may begin. 
     A flowchart  160  of  FIG. 9  represents one embodiment of a method for performing the voice training  104 . The flowchart  160  may begin when the electronic device  10  prompts the user to speak while certain distractors (e.g., simulated ambient sounds) play in the background (block  162 ). For example, the user may be asked to speak a certain word or phrase while certain distractors, such as rock music, babbling people, crumpled paper, and so forth, are playing aloud on the computer or other electronic device  132  or on a speaker  48  of the electronic device  10 . While such distractors are playing, the electronic device  10  may record a sample of the user&#39;s voice (block  164 ). In some embodiments, blocks  162  and  164  may repeat while a variety of distractors are played to obtain several test audio signals that include both the user&#39;s voice and one or more distractors. 
     To determine which noise suppression parameters a user most prefers, the electronic device  10  may alternatingly apply certain test noise suppression parameters while noise suppression  20  is applied to the test audio signals before requesting feedback from the user. For example, the electronic device  10  may apply a first set of test noise suppression parameters, here labeled “A,” to the test audio signal including the user&#39;s voice sample and the one or more distractors, before outputting the audio to the user via a speaker  48  (block  166 ). Next, the electronic device  10  may apply another set of test noise suppression parameters, here labeled “B,” to the user&#39;s voice sample before outputting the audio to the user via the speaker  48  (block  168 ). The user then may decide which of the two audio signals output by the electronic device  10  the user prefers (e.g., by selecting either “A” or “B” on a display  18  of the electronic device  10 ) (block  170 ). 
     The electronic device  10  may repeat the actions of blocks  166 - 170  with various test noise suppression parameters and with various distractors, learning more about the user&#39;s noise suppression preferences each time until a suitable set of user noise suppression preference data has been obtained (decision block  172 ). Thus, the electronic device  10  may test the desirability of a variety of noise suppression parameters as actually applied to an audio signal containing the user&#39;s voice as well as certain common ambient sounds. In some embodiments, with each iteration of blocks  166 - 170 , the electronic device  10  may “tune” the test noise suppression parameters by gradually varying certain noise suppression parameters (e.g., gradually increasing or decreasing a noise suppression strength) until a user&#39;s noise suppression preferences have settled. In other embodiments, the electronic device  10  may test different types of noise suppression parameters in each iteration of blocks  166 - 170  (e.g., noise suppression strength in one iteration, noise suppression of certain frequencies in another iteration, and so forth). In any case, the blocks  166 - 170  may repeat until a desired number of user preferences have been obtained (decision block  172 ). 
     Based on the indicated user preferences obtained at block(s)  170 , the electronic device  10  may develop user-specific noise suppression parameters  102  (block  174 ). By way of example, the electronic device  10  may arrive at a preferred set of user-specific noise suppression parameters  102  when the iterations of blocks  166 - 170  have settled, based on the user feedback of block(s)  170 . In another example, if the iterations of blocks  166 - 170  each test a particular set of noise suppression parameters, the electronic device  10  may develop a comprehensive set of user-specific noise suppression parameters based on the indicated preferences to the particular parameters. The user-specific noise suppression parameters  102  may be stored in the memory  14  or the nonvolatile storage  16  of the electronic device  10  (block  176 ) for noise suppression when the same user later uses a voice-related feature of the electronic device  10 . 
       FIGS. 10-13  relate to specific manners in which the electronic device  10  may carry out the flowchart  160  of  FIG. 9 . In particular,  FIGS. 10 and 11  relate to blocks  162  and  164  of the flowchart  160  of  FIG. 9 , and  FIGS. 12 and 13A -B relate to blocks  166 - 172 . Turning to  FIG. 10 , a dual-device voice recording system  180  includes the computer or other electronic device  132  and the handheld device  34 . In some embodiments, the handheld device  34  may be joined to the computer or other electronic device  132  by way of a communication cable  134  or via wireless communication (e.g., an 802.11x Wi-Fi WLAN or a Bluetooth PAN). During the operation of the system  180 , the computer or other electronic device  132  may prompt the user to say a word or phrase while one or more of a variety of distractors  182  play in the background. Such distractors  182  may include, for example, sounds of crumpled paper  184 , babbling people  186 , white noise  188 , rock music  190 , and/or road noise  192 . The distractors  182  may additionally or alternatively include, for example, other noises commonly encountered in various contexts  56 , such as those discussed above with reference to  FIG. 3 . These distractors  182 , playing aloud from the computer or other electronic device  132 , may be picked up by the microphone  32  of the handheld device  34  at the same time the user provides a user voice sample  194 . In this manner, the handheld device  34  may obtain test audio signals that include both a distractor  182  and a user voice sample  194 . 
     In another embodiment, represented by a single-device voice recording system  200  of  FIG. 11 , the handheld device  34  may both output distractor(s)  182  and record a user voice sample  194  at the same time. As shown in  FIG. 11 , the handheld device  34  may prompt a user to say a word or phrase for the user voice sample  194 . At the same time, a speaker  48  of the handheld device  34  may output one or more distractors  182 . The microphone  32  of the handheld device  34  then may record a test audio signal that includes both a currently playing distractor  182  and a user voice sample  194  without the computer or other electronic device  132 . 
     Corresponding to blocks  166 - 170 ,  FIG. 12  illustrates an embodiment for determining user&#39;s noise suppression preferences based on a choice of noise suppression parameters applied to a test audio signal. In particular, the electronic device  10 , here represented as the handheld device  34 , may apply a first set of noise suppression parameters (“A”) to a test audio signal that includes both a user voice sample  194  and at least one distractor  182 . The handheld device  34  may output the noise-suppressed audio signal that results (numeral  212 ). The handheld device  34  also may apply a second set of noise suppression parameters (“B”) to the test audio signal before outputting the resulting noise-suppressed audio signal (numeral  214 ). 
     When the user has heard the result of applying the two sets of noise suppression parameters “A” and “B” to the test audio signal, the handheld device  34  may ask the user, for example, “Did you prefer A or B?” (numeral  216 ). The user then may indicate a noise suppression preference based on the output noise-suppressed signals. For example, the user may select either the first noise-suppressed audio signal (“A”) or the second noise-suppressed audio signal (“B”) via a screen  218  on the handheld device  34 . In some embodiments, the user may indicate a preference in other manners, such as by saying “A” or “B” aloud. 
     The electronic device  10  may determine the user preferences for specific noise suppression parameters in a variety of manners. A flowchart  220  of  FIG. 13  represents one embodiment of a method for performing blocks  166 - 172  of the flowchart  160  of  FIG. 9 . The flowchart  220  may begin when the electronic device  10  applies a set of noise suppression parameters that, for exemplary purposes, are labeled “A” and “B”. If the user prefers the noise suppression parameters “A” (decision block  224 ), the electronic device  10  may next apply new sets of noise suppression parameters that, for similarly descriptive purposes are labeled “C” and “D” (block  226 ). In certain embodiments, the noise suppression parameters “C” and “D” may be variations of the noise suppression parameters “A.” If a user prefers the noise suppression parameters “C” (decision block  228 ), the electronic device may set the noise suppression parameters to be a combination of “A” and “C” (block  230 ). If the user prefers the noise suppression parameters “D” (decision block  228 ), the electronic device may set the user-specific noise suppression parameters to be a combination of the noise suppression parameters “A” and “D” (block  232 ). 
     If, after block  222 , the user prefers the noise suppression parameters “B” (decision block  224 ), the electronic device  10  may apply the new noise suppression parameters “C” and “D” (block  234 ). In certain embodiments, the new noise suppression parameters “C” and “D” may be variations of the noise suppression parameters “B”. If the user prefers the noise suppression parameters “C” (decision block  236 ), the electronic device  10  may set the user-specific noise suppression parameters to be a combination of “B” and “C” (block  238 ). Otherwise, if the user prefers the noise suppression parameters “D”(decision block  236 ), the electronic device  10  may set the user-specific noise suppression parameters to be a combination of “B” and “D” (block  240 ). As should be appreciated, the flowchart  220  is presented as only one manner of performing blocks  166 - 172  of the flowchart  160  of  FIG. 9 . Accordingly, it should be understood that many more noise suppression parameters may be tested, and such parameters may be tested specifically in conjunction with certain distractors (e.g., in certain embodiments, the flowchart  220  may be repeated for test audio signals that respectively include each of the distractors  182 ). 
     The voice training sequence  104  may be performed in other ways. For example, in one embodiment represented by a flowchart  250  of  FIG. 14 , a user voice sample  194  first may be obtained without any distractors  182  playing in the background (block  252 ). In general, such a user voice sample  194  may be obtained in a location with very little ambient sounds  60 , such as a quiet room, so that the user voice sample  194  has a relatively high signal-to-noise ratio (SNR). Thereafter, the electronic device  10  may mix the user voice sample  194  with the various distractors  182  electronically (block  254 ). Thus, the electronic device  10  may produce one or more test audio signals having a variety of distractors  182  using a single user voice sample  194 . 
     Thereafter, the electronic device  10  may determine which noise suppression parameters a user most prefers to determine the user-specific noise suppression parameters  102 . In a manner similar to blocks  166 - 170  of  FIG. 9 , the electronic device  10  may alternatingly apply certain test noise suppression parameters to the test audio signals obtained at block  254  to gauge user preferences (blocks  256 - 260 ). The electronic device  10  may repeat the actions of blocks  256 - 260  with various test noise suppression parameters and with various distractors, learning more about the user&#39;s noise suppression preferences each time until a suitable set of user noise suppression preference data has been obtained (decision block  262 ). Thus, the electronic device  10  may test the desirability of a variety of noise suppression parameters as applied to a test audio signal containing the user&#39;s voice as well as certain common ambient sounds. 
     Like block  174  of  FIG. 9 , the electronic device  10  may develop user-specific noise suppression parameters  102  (block  264 ). The user-specific noise suppression parameters  102  may be stored in the memory  14  or the nonvolatile storage  16  of the electronic device  10  (block  266 ) for noise suppression when the same user later uses a voice-related feature of the electronic device  10 . 
     As mentioned above, certain embodiments of the present disclosure may involve obtaining a user voice sample  194  without distractors  182  playing aloud in the background. In some embodiments, the electronic device  10  may obtain such a user voice sample  194  the first time that the user uses a voice-related feature of the electronic device  10  in a quiet setting without disrupting the user. As represented in a flowchart  270  of  FIG. 15 , in some embodiments, the electronic device  10  may obtain such a user voice sample  194  when the electronic device  10  first detects a sufficiently high signal-to-noise ratio (SNR) of audio containing the user&#39;s voice. 
     The flowchart  270  of  FIG. 15  may begin when a user is using a voice-related feature of the electronic device  10  (block  272 ). To ascertain an identity of the user, the electronic device  10  may detect a voice profile of the user based on an audio signal detected by the microphone  32  (block  274 ). If the voice profile detected in block  274  represents the voice profile of the voice of a known user of the electronic device (decision block  276 ), the electronic device  10  may apply the user-specific noise suppression parameters  102  associated with that user (block  278 ). If the user&#39;s identity is unknown (decision block  276 ), the electronic device  10  may initially apply default noise suppression parameters (block  280 ). 
     The electronic device  10  may assess the current signal-to-noise ration (SNR) of the audio signal received by the microphone  32  while the voice-related feature is being used (block  282 ). If the SNR is sufficiently high (e.g., above a preset threshold), the electronic device  10  may obtain a user voice sample  194  from the audio received by the microphone  32  (block  286 ). If the SNR is not sufficiently high (e.g., below the threshold) (decision block  284 ), the electronic device  10  may continue to apply the default noise suppression parameters (block  280 ), continuing to at least periodically reassess the SNR. A user voice sample  194  obtained in this manner may be later employed in the voice training sequence  104  as discussed above with reference to  FIG. 14 . In other embodiments, the electronic device  10  may employ such a user voice sample  194  to determine the user-specific noise suppression parameters  102  based on the user voice sample  194  itself. 
     Specifically, in addition to the voice training sequence  104 , the user-specified noise suppression parameters  102  may be determined based on certain characteristics associated with a user voice sample  194 . For example,  FIG. 16  represents a flowchart  290  for determining the user-specific noise suppression parameters  102  based on such user voice characteristics. The flowchart  290  may begin when the electronic device  10  obtains a user voice sample  194  (block  292 ). The user voice sample may be obtained, for example, according to the flowchart  270  of  FIG. 15  or may be obtained when the electronic device  10  prompts the user to say a specific word or phrase. The electronic device next may analyze certain characteristics associated with the user voice sample (block  294 ). 
     Based on the various characteristics associated with the user voice sample  194 , the electronic device  10  may determine the user-specific noise suppression parameters  102  (block  296 ). For example, as shown by a voice characteristic diagram  300  of  FIG. 17 , a user voice sample  194  may include a variety of voice sample characteristics  302 . Such characteristics  302  may include, among other things, an average frequency  304  of the user voice sample  194 , a variability of the frequency  306  of the user voice sample  194 , common speech sounds  308  associated with the user voice sample  194 , a frequency range  310  of the user voice sample  194 , formant locations  312  in the frequency of the user voice sample, and/or a dynamic range  314  of the user voice sample  194 . These characteristics may arise because different users may have different speech patterns. That is, the highness or deepness of a user&#39;s voice, a user&#39;s accent in speaking, and/or a lisp, and so forth, may be taken into consideration to the extent they change a measurable character of speech, such as the characteristics  302 . 
     As mentioned above, the user-specific noise suppression parameters  102  also may be determined by a direct selection of user settings  108 . One such example appears in  FIG. 18  as a user setting screen sequence  320  for a handheld device  32 . The screen sequence  320  may begin when the electronic device  10  displays a home screen  140  that includes a settings button  142 . Selecting the settings button  142  may cause the handheld device  34  to display a settings screen  144 . Selecting a user-selectable button  146  labeled “Phone” on the settings screen  144  may cause the handheld device  34  to display a phone settings screen  148 , which may include various user-selectable buttons, one of which may be a user-selectable button  322  labeled “Noise Suppression.” 
     When a user selects the user-selectable button  322 , the handheld device  34  may display a noise suppression selection screen  324 . Through the noise suppression selection screen  324 , a user may select a noise suppression strength. For example, the user may select whether the noise suppression should be high, medium, or low strength via a selection wheel  326 . Selecting a higher noise suppression strength may result in the user-specific noise suppression parameters  102  suppressing more ambient sounds  60 , but possibly also suppressing more of the voice of the user  58 , in a received audio signal. Selecting a lower noise suppression strength may result in the user-specific noise suppression parameters  102  permitting more ambient sounds  60 , but also permitting more of the voice of the user  58 , to remain in a received audio signal. 
     In other embodiments, the user may adjust the user-specific noise suppression parameters  102  in real time while using a voice-related feature of the electronic device  10 . By way of example, as seen in a call-in-progress screen  330  of  FIG. 19 , which may be displayed on the handheld device  34 , a user may provide a measure of voice phone call quality feedback  332 . In certain embodiments, the feedback may be represented by a number of selectable stars  334  to indicate the quality of the call. If the number of stars  334  selected by the user is high, it may be understood that the user is satisfied with the current user-specific noise suppression parameters  102 , and so the electronic device  10  may not change the noise suppression parameters. On the other hand, if the number of selected stars  334  is low, the electronic device  10  may vary the user-specific noise suppression parameters  102  until the number of stars  334  is increased, indicating user satisfaction. Additionally or alternatively, the call-in-progress screen  330  may include a real-time user-selectable noise suppression strength setting, such as that disclosed above with reference to  FIG. 18 . 
     In certain embodiments, subsets of the user-specific noise suppression parameters  102  may be determined as associated with certain distractors  182  and/or certain contexts  60 . As illustrated by a parameter diagram  340  of  FIG. 20 , the user-specific noise suppression parameters  102  may divided into subsets based on specific distractors  182 . For example, the user-specific noise suppression parameters  102  may include distractor-specific parameters  344 - 352 , which may represent noise suppression parameters chosen to filter certain ambient sounds  60  associated with a distractor  182  from an audio signal also including the voice of the user  58 . It should be understood that the user-specific noise suppression parameters  102  may include more or fewer distractor-specific parameters. For example, if different distractors  182  are tested during voice training  104 , the user-specific noise suppression parameters  102  may include different distractor-specific parameters. 
     The distractor-specific parameters  344 - 352  may be determined when the user-specific noise suppression parameters  102  are determined. For example, during voice training  104 , the electronic device  10  may test a number of noise suppression parameters using test audio signals including the various distractors  182 . Depending on a user&#39;s preferences relating to noise suppression for each distractor  182 , the electronic device may determine the distractor-specific parameters  344 - 352 . By way of example, the electronic device may determine the parameters for crumpled paper  344  based on a test audio signal that included the crumpled paper distractor  184 . As described below, the distractor-specific parameters of the parameter diagram  340  may later be recalled in specific instances, such as when the electronic device  10  is used in the presence of certain ambient sounds  60  and/or in certain contexts  56 . 
     Additionally or alternatively, subsets of the user-specific noise suppression parameters  102  may be defined relative to certain contexts  56  where a voice-related feature of the electronic device  10  may be used. For example, as represented by a parameter diagram  360  shown in  FIG. 21 , the user-specific noise suppression parameters  102  may be divided into subsets based on which context  56  the noise suppression parameters may best be used. For example, the user-specific noise suppression parameters  102  may include context-specific parameters  364 - 378 , representing noise suppression parameters chosen to filter certain ambient sounds  60  that may be associated with specific contexts  56 . It should be understood that the user-specific noise suppression parameters  102  may include more or fewer context-specific parameters. For example, as discussed below, the electronic device  10  may be capable of identifying a variety of contexts  56 , each of which may have specific expected ambient sounds  60 . The user-specific noise suppression parameters  102  therefore may include different context-specific parameters to suppress noise in each of the identifiable contexts  56 . 
     Like the distractor-specific parameters  344 - 352 , the context-specific parameters  364 - 378  may be determined when the user-specific noise suppression parameters  102  are determined. To provide one example, during voice training  104 , the electronic device  10  may test a number of noise suppression parameters using test audio signals including the various distractors  182 . Depending on a user&#39;s preferences relating to noise suppression for each distractor  182 , the electronic device  10  may determine the context-specific parameters  364 - 378 . 
     The electronic device  10  may determine the context-specific parameters  364 - 378  based on the relationship between the contexts  56  of each of the context-specific parameters  364 - 378  and one or more distractors  182 . Specifically, it should be noted that each of the contexts  56  identifiable to the electronic device  10  may be associated with one or more specific distractors  182 . For example, the context  56  of being in a car  70  may be associated primarily with one distractor  182 , namely, road noise  192 . Thus, the context-specific parameters  376  for being in a car may be based on user preferences related to test audio signals that included road noise  192 . Similarly, the context  56  of a sporting event  72  may be associated with several distractors  182 , such as babbling people  186 , white noise  188 , and rock music  190 . Thus, the context-specific parameters  368  for a sporting event may be based on a combination of user preferences related to test audio signals that included babbling people  186 , white noise  188 , and rock music  190 . This combination may be weighted to more heavily account for distractors  182  that are expected to more closely match the ambient sounds  60  of the context  56 . 
     As mentioned above, the user-specific noise suppression parameters  102  may be determined based on characteristics of the user voice sample  194  with or without the voice training  104  (e.g., as described above with reference to  FIGS. 16 and 17 ). Under such conditions, the electronic device  10  may additionally or alternatively determine the distractor-specific parameters  344 - 352  and/or the context-specific parameters  364 - 378  automatically (e.g., without user prompting). These noise suppression parameters  344 - 352  and/or  363 - 378  may be determined based on the expected performance of such noise suppression parameters when applied to the user voice sample  194  and certain distractors  182 . 
     When a voice-related feature of the electronic device  10  is in use, the electronic device  10  may tailor the noise suppression  20  both to the user and to the character of the ambient sounds  60  using the distractor-specific parameters  344 - 352  and/or the context-specific parameters  364 - 378 . Specifically,  FIG. 22  illustrates an embodiment of a method for selecting and applying the distractor-specific parameters  344 - 352  based on the assessed character of ambient sounds  60 .  FIG. 23  illustrates an embodiment of a method for selecting and applying the context-specific parameters  364 - 378  based on the identified context  56  where the electronic device  10  is used. 
     Turning to  FIG. 22 , a flowchart  380  for selecting and applying the distractor-specific parameters  344 - 352  may begin when a voice-related feature of the electronic device  10  is in use (block  382 ). Next, the electronic device  10  may determine the character of the ambient sounds  60  received by its microphone  32  (block  384 ). In some embodiments, the electronic device  10  may differentiate between the ambient sounds  60  and the user&#39;s voice  58 , for example, based on volume level (e.g., the user&#39;s voice  58  generally may be louder than the ambient sounds  60 ) and/or frequency (e.g., the ambient sounds  60  may occur outside of a frequency range associated with the user&#39;s voice  58 ). 
     The character of the ambient sounds  60  may be similar to one or more of the distractors  182 . Thus, in some embodiments, the electronic device  10  may apply the one of the distractor-specific parameters  344 - 352  that most closely match the ambient sounds  60  (block  386 ). For the context  56  of being at a restaurant  74 , for example, the ambient sounds  60  detected by the microphone  32  may most closely match babbling people  186 . The electronic device  10  thus may apply the distractor-specific parameter  346  when such ambient sounds  60  are detected. In other embodiments, the electronic device  10  may apply several of the distractor-specific parameters  344 - 352  that most closely match the ambient sounds  60 . These several distractor-specific parameters  344 - 352  may be weighted based on the similarity of the ambient sounds  60  to the corresponding distractors  182 . For example, the context  56  of a sporting event  72  may have ambient sounds  60  similar to several distractors  182 , such as babbling people  186 , white noise  188 , and rock music  190 . When such ambient sounds  60  are detected, the electronic device  10  may apply the several associated distractor-specific parameters  346 ,  348 , and/or  350  in proportion to the similarity of each to the ambient sounds  60 . 
     In a similar manner, the electronic device  10  may select and apply the context-specific parameters  364 - 378  based on an identified context  56  where the electronic device  10  is used. Turning to  FIG. 23 , a flowchart  390  for doing so may begin when a voice-related feature of the electronic device  10  is in use (block  392 ). Next, the electronic device  10  may determine the current context  56  in which the electronic device  10  is being used (block  394 ). Specifically, the electronic device  10  may consider a variety of device context factors (discussed in greater detail below with reference to  FIG. 24 ). Based on the context  56  in which the electronic device  10  is determined to be in use, the electronic device  10  may apply the associated one of the context-specific parameters  364 - 378  (block  396 ). 
     As shown by a device context factor diagram  400  of  FIG. 24 , the electronic device  10  may consider a variety of device context factors  402  to identify the current context  56  in which the electronic device  10  is being used. These device context factors  402  may be considered alone or in combination in various embodiments and, in some cases, the device context factors  402  may be weighted. That is, device context factors  402  more likely to correctly predict the current context  56  may be given more weight in determining the context  56 , while device context factors  402  less likely to correctly predict the current context  56  may be given less weight. 
     For example, a first factor  404  of the device context factors  402  may be the character of the ambient sounds  60  detected by the microphone  32  of the electronic device  10 . Since the character of the ambient sounds  60  may relate to the context  56 , the electronic device  10  may determine the context  56  based at least partly on such an analysis. 
     A second factor  406  of the device context factors  402  may be the current date or time of day. In some embodiments, the electronic device  10  may compare the current date and/or time with a calendar feature of the electronic device  10  to determine the context. By way of example, if the calendar feature indicates that the user is expected to be at dinner, the second factor  406  may weigh in favor of determining the context  56  to be a restaurant  74 . In another example, since a user may be likely to commute in the morning or late afternoon, at such times the second factor  406  may weigh in favor of determining the context  56  to be a car  70 . 
     A third factor  408  of the device context factors  402  may be the current location of the electronic device  10 , which may be determined by the location-sensing circuitry  22 . Using the third factor  408 , the electronic device  10  may consider its current location in determining the context  56  by, for example, comparing the current location to a known location in a map feature of the electronic device  10  (e.g., a restaurant  74  or office  64 ) or to locations where the electronic device  10  is frequently located (which may indicate, for example, an office  64  or home  62 ). 
     A fourth factor  410  of the device context factors  402  may be the amount of ambient light detected around the electronic device  10  via, for example, the image capture circuitry  28  of the electronic device. By way of example, a high amount of ambient light may be associated with certain contexts  56  located outdoors (e.g., a busy street  68 ). Under such conditions, the factor  410  may weigh in favor of a context  56  located outdoors. A lower amount of ambient light, by contrast, may be associated with certain contexts  56  located indoors (e.g., home  62 ), in which case the factor  410  may weigh in favor of such an indoor context  56 . 
     A fifth factor  412  of the device context factors  402  may be detected motion of the electronic device  10 . Such motion may be detected based on the accelerometers and/or magnetometer  30  and/or based on changes in location over time as determined by the location-sensing circuitry  22 . Motion may suggest a given context  56  in a variety of ways. For example, when the electronic device  10  is detected to be moving very quickly (e.g., faster than 20 miles per hour), the factor  412  may weigh in favor of the electronic device  10  being in a car  70  or similar form of transportation. When the electronic device  10  is moving randomly, the factor  412  may weigh in favor of contexts in which a user of the electronic device  10  may be moving about (e.g., at a gym  66  or a party  76 ). When the electronic device  10  is mostly stationary, the factor  412  may weigh in favor of contexts  56  in which the user is seated at one location for a period of time (e.g., an office  64  or restaurant  74 ). 
     A sixth factor  414  of the device context factors  402  may be a connection to another device (e.g., a Bluetooth handset). For example, a Bluetooth connection to an automotive hands-free phone system may cause the sixth factor  414  to weigh in favor of determining the context  56  to be in a car  70 . 
     In some embodiments, the electronic device  10  may determine the user-specific noise suppression parameters  102  based on a user voice profile associated with a given user of the electronic device  10 . The resulting user-specific noise suppression parameters  102  may cause the noise suppression  20  to isolate ambient sounds  60  that do not appear associated with the user voice profile, and thus may be understood to likely be noise.  FIGS. 25-29  relate to such techniques. 
     As shown in  FIG. 25 , a flowchart  420  for obtaining a user voice profile may begin when the electronic device  10  obtains a voice sample (block  422 ). Such a voice sample may be obtained in any of the manners described above. The electronic device  10  may analyze certain of the characteristics of the voice sample, such as those discussed above with reference to FIG. (block  424 ). The specific characteristics may be quantified and stored as a voice profile of the user (block  426 ). The determined user voice profile may be employed to tailor the noise suppression  20  to the user&#39;s voice, as discussed below. In addition, the user voice profile may enable the electronic device  10  to identify when a particular user is using a voice-related feature of the electronic device  10 , such as discussed above with reference to  FIG. 15 . 
     With such a voice profile, the electronic device  10  may perform the noise suppression  20  in a manner best applicable to that user&#39;s voice. In one embodiment, as represented by a flowchart  430  of  FIG. 26 , the electronic device  10  may suppress frequencies of an audio signal that more likely correspond to ambient sounds  60  than a voice of a user  58 , while enhancing frequencies more likely to correspond to the voice signal  58 . The flowchart  430  may begin when a user is using a voice-related feature of the electronic device  10  (block  432 ). The electronic device  10  may compare an audio signal received that includes both a user voice signal  58  and ambient sounds  60  to a user voice profile associated with the user currently speaking into the electronic device  10  (block  434 ). To tailor the noise suppression  20  to the user&#39;s voice, the electronic device may perform noise suppression  20  in a manner that suppresses frequencies of the audio signal that are not associated with the user voice profile and by amplifying frequencies of the audio signal that are associated with the user voice profile (block  436 ). 
     One manner of doing so is shown through  FIGS. 27-29 , which represent plots modeling an audio signal, a user voice profile, and an outgoing noise-suppressed signal. Turning to  FIG. 27 , a plot  440  represents an audio signal that has been received into the microphone  32  of the electronic device  10  while a voice-related feature is in use and transformed into the frequency domain. An ordinate  442  represents a magnitude of the frequencies of the audio signal and an abscissa  444  represents various discrete frequency components of the audio signal. It should be understood that any suitable transform, such as a fast Fourier transform (FFT), may be employed to transform the audio signal into the frequency domain. Similarly, the audio signal may be divided into any suitable number of discrete frequency components (e.g., 40, 128, 256, etc.). 
     By contrast, a plot  450  of  FIG. 28  is a plot modeling frequencies associated with a user voice profile. An ordinate  452  represents a magnitude of the frequencies of the user voice profile and an abscissa  454  represents discrete frequency components of the user voice profile. Comparing the audio signal plot  440  of  FIG. 27  to the user voice profile plot  450  of  FIG. 28 , it may be seen that the modeled audio signal includes range of frequencies not typically associated with the user voice profile. That is, the modeled audio signal may be likely to include other ambient sounds  60  in addition to the user&#39;s voice. 
     From such a comparison, when the electronic device  10  carries out noise suppression  20 , it may determine or select the user-specific noise suppression parameters  102  such that the frequencies of the audio signal of the plot  440  that correspond to the frequencies of the user voice profile of the plot  450  are generally amplified, while the other frequencies are generally suppressed. Such a resulting noise-suppressed audio signal is modeled by a plot  460  of  FIG. 29 . An ordinate  462  of the plot  460  represents a magnitude of the frequencies of the noise-suppressed audio signal and an abscissa  464  represents discrete frequency components of the noise-suppressed signal. An amplified portion  466  of the plot  460  generally corresponds to the frequencies found in the user voice profile. By contrast, a suppressed portion  468  of the plot  460  corresponds to frequencies of the noise-suppressed signal that are not associated with the user profile of plot  450 . In some embodiments, a greater amount of noise suppression may be applied to frequencies not associated with the user voice profile of plot  450 , while a lesser amount of noise suppression may be applied to the portion  466 , which may or may not be amplified. 
     The above discussion generally focused on determining the user-specific noise suppression parameters  102  for performing the TX NS  84  of the noise suppression  20  on an outgoing audio signal, as shown in  FIG. 4 . However, as mentioned above, the user-specific noise suppression parameters  102  also may be used for performing the RX NS  92  on an incoming audio signal from another device. Since such an incoming audio signal from another device will not include the user&#39;s own voice, in certain embodiments, the user-specific noise suppression parameters  102  may be determined based on voice training  104  that involves several test voices in addition to several distractors  182 . 
     For example, as presented by a flowchart  470  of  FIG. 30 , the electronic device  10  may determine the user-specific noise suppression parameters  102  via voice training  104  involving pre-recorded or simulated voices and simulated distractors  182 . Such an embodiment of the voice training  104  may involve test audio signals that include a variety of difference voices and distractors  182 . The flowchart  470  may begin when a user initiates voice training  104  (block  472 ). Rather than perform the voice training  104  based solely on the user&#39;s own voice, the electronic device  10  may apply various noise suppression parameters to various test audio signals containing various voices, one of which may be the user&#39;s voice in certain embodiments (block  474 ). Thereafter, the electronic device  10  may ascertain the user&#39;s preferences for different noise suppression parameters tested on the various test audio signals. As should be appreciated, block  474  may be carried out in a manner similar to blocks  166 - 170  of  FIG. 9 . 
     Based on the feedback from the user at block  474 , the electronic device  10  may develop user-specific noise suppression parameters  102  (block  476 ). The user-specific parameters  102  developed based on the flowchart  470  of  FIG. 30  may be well suited for application to a received audio signal (e.g., used to form the RX NS parameters  94 , as shown in  FIG. 4 ). In particular, a received audio signal will includes different voices when the electronic device  10  is used as a telephone by a “near-end” user to speak with “far-end” users. Thus, as shown by a flowchart  480  of  FIG. 31 , the user-specific noise suppression parameters  102 , determined using a technique such as that discussed with reference to  FIG. 30 , may be applied to the received audio signal from a far-end user depending on the character of the far-end user&#39;s voice in the received audio signal. 
     The flowchart  480  may begin when a voice-related feature of the electronic device  10 , such as a telephone or chat feature, is in use and is receiving an audio signal from another electronic device  10  that includes a far-end user&#39;s voice (block  482 ). Subsequently, the electronic device  10  may determine the character of the far-end user&#39;s voice in the audio signal (block  484 ). Doing so may entail, for example, comparing the far-end user&#39;s voice in the received audio signal with certain other voices that were tested during the voice training  104  (when carried out as discussed above with reference to  FIG. 30 ). The electronic device  10  next may apply the user-specific noise suppression parameters  102  that correspond to one of the other voices that is most similar to the end-user&#39;s voice (block  486 ). 
     In general, when a first electronic device  10  receives an audio signal containing a far-end user&#39;s voice from a second electronic device  10  during two-way communication, such an audio signal already may have been processed for noise suppression in the second electronic device  10 . According to certain embodiments, such noise suppression in the second electronic device  10  may be tailored to the near-end user of the first electronic  10 , as described by a flowchart  490  of  FIG. 32 . The flowchart  490  may begin when the first electronic device  10  (e.g., handheld device  34 A of  FIG. 33 ) is or is about to begin receiving an audio signal of the far-end user&#39;s voice from the second electronic device  10  (e.g., handheld device  34 B) (block  492 ). The first electronic device  10  may transmit the user-specific noise suppression parameters  102 , previously determined by the near-end user, to the second electronic device  10  (block  494 ). Thereafter, the second electronic device  10  may apply those user-specific noise suppression parameters  102  toward the noise suppression of the far-end user&#39;s voice in the outgoing audio signal (block  496 ). Thus, the audio signal including the far-end user&#39;s voice that is transmitted from the second electronic device  10  to the first electronic device  10  may have the noise-suppression characteristics preferred by the near-end user of the first electronic device  10 . 
     The above-discussed technique of  FIG. 32  may be employed systematically using two electronic devices  10 , illustrated as a system  500  of  FIG. 33  including handheld devices  34 A and  34 B with similar noise suppression capabilities. When the handheld devices  34 A and  34 B are used for intercommunication by a near-end user and a far-end user respectively over a network (e.g., using a telephone or chat feature), the handheld devices  34 A and  34 B may exchange the user-specific noise suppression parameters  102  associated with their respective users (blocks  504  and  506 ). That is, the handheld device  34 B may receive the user-specific noise suppression parameters  102  associated with the near-end user of the handheld device  34 A. Likewise, the handheld device  34 A may receive the user-specific noise suppression parameters  102  associated with the far-end user of the handheld device  34 B. Thereafter, the handheld device  34 A may perform noise suppression  20  on the near-end user&#39;s audio signal based on the far-end user&#39;s user-specific noise suppression parameters  102 . Likewise, the handheld device  34 B may perform noise suppression  20  on the far-end user&#39;s audio signal based on the near-end user&#39;s user-specific noise suppression parameters  102 . In this way, the respective users of the handheld devices  34 A and  34 B may hear audio signals from the other whose noise suppression matches their respective preferences. 
     The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.

Metadata:
Filing Date: 20140127
Publication Date: 20191015
Grant Date: 20191015
Priority Date: 20100604
Inventors: LINDAHL, ARAM
PAQUIER, BAPTISTE PIERRE
Assignee: APPLE INC
CPC Classifications: [{"code": "G10L21/0208", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L21/02", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L21/0208", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L21/0208", "inventive": true, "first": true, "tree": "[]"}]
Family ID: 44276060