Apple Inc.

Peter A Raffensperger

Jonathan D Sheaffer

Joshua D Atkins

Symeon Delikaris Manias

Juha O Merimaa

Mehrez Souden

Harvey D Thornburg

Hyung-Suk Kim

Dipanjan Sen

Martin E Johnson

Frank M Baumgarte

Eric A Allamanche

Ashrith Deshpande

Jason Wung

Gaetan R Lorho

Symeon Delikaris-Manias

A device may include a plurality of microphones, and a processor configured to extract a sound and a sound field from microphone signals of the plurality of microphones. The sound may be treated as near-field sound and the sound field may be treated as far-field sound. The device may adjust a strength of the sound based on a strength of the sound field and apply near-field head related transfer functions (HRTFs) to the sound. The device may apply far-field HRTFs to the sound field. The device may combine the near-field applied sound with the far-field applied sound field to generate spatial audio for playback through a plurality of speakers.

Spatial audio using near-field and far-field rendering

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.

Spatial audio file format for storing capture metadata

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.

Spatially biased sound pickup for binaural video recording

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.

Processing of audio signals for spatial audio

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.

Real-time feedback during audio recording, and related devices and systems

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.

Machine learning based sound field analysis

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.

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Cupertino, CA, United States of America

Peter A Raffensperger