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    Audio source separation for audio devices
    1.
    发明授权
    Audio source separation for audio devices 有权

    公开(公告)号：US12272370B2

    公开(公告)日：2025-04-08

    申请号：US18376438

    申请日：2023-10-03

    Applicant: Apple Inc.

    Inventor： Carlos M. Avendano ,  John Woodruff ,  Jonathan Huang ,  Mehrez Souden ,  Andreas Koutrouvelis

    IPC: H04R29/00 ,  G06N20/00 ,  G10L21/0232 ,  G10L21/028 ,  H04R1/10

    Abstract: Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.

    Time domain neural networks for spatial audio reproduction
    2.
    发明授权
    Time domain neural networks for spatial audio reproduction 有权

    公开(公告)号：US11490218B1

    公开(公告)日：2022-11-01

    申请号：US17134097

    申请日：2020-12-24

    Applicant: Apple Inc.

    Inventor： Symeon Delikaris Manias ,  Mehrez Souden

    IPC: H04R5/02 ,  H04S7/00 ,  G06N20/00 ,  G06N3/04

    Abstract: A device for reproducing spatial audio using a machine learning model may include at least one processor configured to receive multiple audio signals corresponding to a sound scene captured by respective microphones of a device. The at least one processor may be further configured to provide the multiple audio signals to a machine learning model, the machine learning model having been trained based at least in part on a target rendering configuration. The at least one processor may be further configured to provide, responsive to providing the multiple audio signals to the machine learning model, multichannel audio signals that comprise a spatial reproduction of the sound scene in accordance with the target rendering configuration.

    Machine learning and user driven selective hearing
    3.
    发明授权
    Machine learning and user driven selective hearing 有权

    公开(公告)号：US12141347B1

    公开(公告)日：2024-11-12

    申请号：US18055600

    申请日：2022-11-15

    Applicant: Apple Inc.

    Inventor： Mehrez Souden ,  Symeon Delikaris Manias ,  Ante Jukic ,  John Woodruff ,  Joshua D. Atkins

    IPC: G06F3/01 ,  G10L15/16 ,  G10L15/24

    Abstract: An audio processing device may generate a plurality of microphone signals from a plurality of microphones of the audio processing device. The audio processing device may determine a gaze of a user who is wearing a playback device that is separate from the audio processing device, the gaze of the user being determined relative to the audio processing device. The audio processing device may extract speech that correlates to the gaze of the user, from the plurality of microphone signals of the audio processing device by applying the plurality of microphone signals of the audio processing device and the gaze of the user to a machine learning model. The extracted speech may be played to the user through the playback device.

    Detecting a trigger of a digital assistant
    6.
    发明授权
    Detecting a trigger of a digital assistant 有权

    公开(公告)号：US11532306B2

    公开(公告)日：2022-12-20

    申请号：US17111132

    申请日：2020-12-03

    Applicant: Apple Inc.

    Inventor： Yoon Kim ,  John Bridle ,  Joshua D. Atkins ,  Feipeng Li ,  Mehrez Souden

    IPC: G10L15/22 ,  H04R1/40 ,  G10L15/08 ,  G10L15/04 ,  H04R3/00 ,  G10L15/30 ,  G10L15/18 ,  G10L15/28 ,  G10L21/0216 ,  G10L25/51 ,  H04R27/00

    Abstract: Systems and processes for operating an intelligent automated assistant are provided. In accordance with one example, a method includes, at an electronic device with one or more processors, memory, and a plurality of microphones, sampling, at each of the plurality of microphones of the electronic device, an audio signal to obtain a plurality of audio signals; processing the plurality of audio signals to obtain a plurality of audio streams; and determining, based on the plurality of audio streams, whether any of the plurality of audio signals corresponds to a spoken trigger. The method further includes, in accordance with a determination that the plurality of audio signals corresponds to the spoken trigger, initiating a session of the digital assistant; and in accordance with a determination that the plurality of audio signals does not correspond to the spoken trigger, foregoing initiating a session of the digital assistant.

    Hybrid learning-based and statistical processing techniques for voice activity detection
    7.
    发明授权
    Hybrid learning-based and statistical processing techniques for voice activity detection 有权

    公开(公告)号：US11341988B1

    公开(公告)日：2022-05-24

    申请号：US16578802

    申请日：2019-09-23

    Applicant: Apple Inc.

    Inventor： Ramin Pishehvar ,  Feiping Li ,  Ante Jukic ,  Mehrez Souden ,  Joshua D. Atkins

    IPC: G10L15/22 ,  G10L25/84 ,  G10L15/05

    Abstract: A hybrid machine learning-based and DSP statistical post-processing technique is disclosed for voice activity detection. The hybrid technique may use a DNN model with a small context window to estimate the probability of speech by frames. The DSP statistical post-processing stage operates on the frame-based speech probabilities from the DNN model to smooth the probabilities and to reduce transitions between speech and non-speech states. The hybrid technique may estimate the soft decision on detected speech in each frame based on the smoothed probabilities, generate a hard decision using a threshold, detect a complete utterance that may include brief pauses, and estimate the end point of the utterance. The hybrid voice activity detection technique may incorporate a target directional probability estimator to estimate the direction of the speech source. The DSP statistical post-processing module may use the direction of the speech source to inform the estimates of the voice activity.

    Learning-Based Distance Estimation
    8.
    发明申请
    Learning-Based Distance Estimation 有权

    公开(公告)号：US20210020189A1

    公开(公告)日：2021-01-21

    申请号：US16516780

    申请日：2019-07-19

    Applicant: Apple Inc.

    Inventor： Ante Jukic ,  Mehrez Souden ,  Joshua D. Atkins

    IPC: G10L25/51 ,  G10L15/22 ,  G10L15/16 ,  G10L15/06 ,  G10L25/78

    Abstract: A learning based system such as a deep neural network (DNN) is disclosed to estimate a distance from a device to a speech source. The deep learning system may estimate the distance of the speech source at each time frame based on speech signals received by a compact microphone array. Supervised deep learning may be used to learn the effect of the acoustic environment on the non-linear mapping between the speech signals and the distance using multi-channel training data. The deep learning system may estimate the direct speech component that contains information about the direct signal propagation from the speech source to the microphone array and the reverberant speech signal that contains the reverberation effect and noise. The deep learning system may extract signal characteristics of the direct signal component and the reverberant signal component and estimate the distance based on the extracted signal characteristics using the learned mapping.

    Spatial Audio Upscaling Using Machine Learning
    9.
    发明公开
    Spatial Audio Upscaling Using Machine Learning 审中-公开

    公开(公告)号：US20240312468A1

    公开(公告)日：2024-09-19

    申请号：US18605688

    申请日：2024-03-14

    Applicant: Apple Inc.

    Inventor： Ismael H. Nawfal ,  Symeon Delikaris Manias ,  Mehrez Souden ,  Joshua D. Atkins

    IPC: G10L19/008 ,  H04S7/00

    CPC classification number: G10L19/008 ,  H04S7/30 ,  H04S2420/11

    Abstract: A sound scene is represented as first order Ambisonics (FOA) audio. A processor formats each signal of the FOA audio to a stream of audio frames, provides the formatted FOA audio to a machine learning model that reformats the formatted FOA audio in a target or desired higher order Ambisonics (HOA) format, and obtains output audio of the sound scene in the desired HOA format from the machine learning model. The output audio in the desired HOA format may then be rendered according to a playback audio format of choice. Other aspects are also described and claimed.

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