Abstract: A device with microphones can generate microphone signals during an audio recording. The device can store, in an electronic audio data file, the microphone signals, and metadata that includes impulse responses of the microphones. Other aspects are described and claimed.

Abstract: A method for producing a target directivity function that includes a set of spatially biased HRTFs. A set of left ear and right ear head related transfer functions (HRTFs) are selected. The left ear and right ear head HRTFs are multiplied with an on-camera emphasis function (OCE), to produce the spatially biased HRTFs. The OCE may be designed to shape the sound profile of the HRTFs to provide emphasis in a desired location or direction that is a function of the specific orientation of the device as it is being used to make a video recording. Other aspects are also described and claimed.

Abstract: A device with microphones can generate microphone signals during an audio recording. The device can store, in an electronic audio data file, the microphone signals, and metadata that includes impulse responses of the microphones. Other aspects are described and claimed.

Abstract: Processing input audio channels for generating spatial audio can include receiving a plurality of microphone signals that capture a sound field. Each microphone signal can be transformed into a frequency domain signal. From each frequency domain signal, a direct component and a diffuse component can be extracted. The direct component can be processed with a parametric renderer. The diffuse component can be processed with a linear renderer. The components can be combined, resulting in a spatial audio output. The levels of the components can be adjusted to match a direct to diffuse ratio (DDR) of the output with the DDR of the captured sound field. Other aspects are also described and claimed.

Abstract: An audio appliance can include a microphone transducer configured to receive sound from an environment and to convert the received sound into an audio signal and a display. The audio appliance can include an audio analytics module configured to detect an audio-input impairment by analyzing the audio signal and output a detection signal identifying the audio-input impairment in real-time. The audio-input impairment can include, for example, a poor-intelligibility impairment, a microphone-occlusion impairment, a handling-noise impairment, a wind-noise impairment, or a distortion impairment. The audio appliance can also include an impairment module configured to identify and emit a user-perceptible alert corresponding to the identified audio-input impairment in real-time; and an interactive guidance module configured to present a suggested action to address the audio-input impairment in real-time. Related aspects also are described.

Abstract: A method for producing a target directivity function that includes a set of spatially biased HRTFs. A set of left ear and right ear head related transfer functions (HRTFs) are selected. The left ear and right ear head HRTFs are multiplied with an on-camera emphasis function (OCE), to produce the spatially biased HRTFs. The OCE may be designed to shape the sound profile of the HRTFs to provide emphasis in a desired location or direction that is a function of the specific orientation of the device as it is being used to make a video recording. Other aspects are also described and claimed.

Abstract: An audio appliance can include a microphone transducer configured to receive sound from an environment and to convert the received sound into an audio signal and a display. The audio appliance can include an audio analytics module configured to detect an audio-input impairment by analyzing the audio signal and output a detection signal identifying the audio-input impairment in real-time. The audio-input impairment can include, for example, a poor-intelligibility impairment, a microphone-occlusion impairment, a handling-noise impairment, a wind-noise impairment, or a distortion impairment. The audio appliance can also include an impairment module configured to identify and emit a user-perceptible alert corresponding to the identified audio-input impairment in real-time; and an interactive guidance module configured to present a suggested action to address the audio-input impairment in real-time. Related aspects also are described.

Abstract: Impulse responses of a device are measured. A database of sound files is generated by convolving source signals with the impulse responses of the device. The sound files from the database are transformed into time-frequency domain. One or more sub-band directional features is estimated at each sub-band of the time-frequency domain. A deep neural network (DNN) is trained for each sub-band based on the estimated one or more sub-band directional features and a target directional feature.

Abstract: Impulse responses of a device are measured. A database of sound files is generated by convolving source signals with the impulse responses of the device. The sound files from the database are transformed into time-frequency domain. One or more sub-band directional features is estimated at each sub-band of the time-frequency domain. A deep neural network (DNN) is trained for each sub-band based on the estimated one or more sub-band directional features and a target directional feature.

Abstract: Systems and techniques for removing non-stationary and/or colored noise can include one or more of the three following innovative aspects: (1) detection of an unwanted target signal, or component thereof, within an observed signal; (2) removal of the target (component) from the observed signal; and (3) filling of a gap in the observed signal generated by removal of the unwanted target (component). Removal regions, frequency bands, and/or regions of the observed signal used to train the gap filler can be adapted in correspondence with local characteristics of the observed signal and/or the target signal (component). Related aspects also are described. For example, disclosed noise detection and/or removal methods can include converting an incoming acoustic signal to a corresponding machine-readable form. And, a corrected signal in machine-readable form can be converted to a human-perceivable form, and/or to a modulated signal form conveyed over a communication connection.

Abstract: Systems and techniques for removing non-stationary and/or colored noise can include one or more of the three following innovative aspects: (1) detection of an unwanted target signal, or component thereof, within an observed signal; (2) removal of the target (component) from the observed signal; and (3) filling of a gap in the observed signal generated by removal of the unwanted target (component). Removal regions, frequency bands, and/or regions of the observed signal used to train the gap filler can be adapted in correspondence with local characteristics of the observed signal and/or the target signal (component). Related aspects also are described. For example, disclosed noise detection and/or removal methods can include converting an incoming acoustic signal to a corresponding machine-readable form. And, a corrected signal in machine-readable form can be converted to a human-perceivable form, and/or to a modulated signal form conveyed over a communication connection.

Abstract: Systems and techniques for removing non-stationary and/or colored noise can include one or more of the three following innovative aspects: (1) detection of an unwanted target signal, or component thereof, within an observed signal; (2) removal of the target (component) from the observed signal; and (3) filling of a gap in the observed signal generated by removal of the unwanted target (component). Removal regions, frequency bands, and/or regions of the observed signal used to train the gap filler can be adapted in correspondence with local characteristics of the observed signal and/or the target signal (component). Related aspects also are described. For example, disclosed noise detection and/or removal methods can include converting an incoming acoustic signal to a corresponding machine-readable form. And, a corrected signal in machine-readable form can be converted to a human-perceivable form, and/or to a modulated signal form conveyed over a communication connection.

Abstract: Systems and techniques for removing non-stationary and/or colored noise can include one or more of the three following innovative aspects: (1) detection of an unwanted target signal, or component thereof, within an observed signal; (2) removal of the target (component) from the observed signal; and (3) filling of a gap in the observed signal generated by removal of the unwanted target (component). Removal regions, frequency bands, and/or regions of the observed signal used to train the gap filler can be adapted in correspondence with local characteristics of the observed signal and/or the target signal (component). Related aspects also are described. For example, disclosed noise detection and/or removal methods can include converting an incoming acoustic signal to a corresponding machine-readable form. And, a corrected signal in machine-readable form can be converted to a human-perceivable form, and/or to a modulated signal form conveyed over a communication connection.