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.

公开(公告)号：US12010490B1

公开(公告)日：2024-06-11

申请号：US18149659

申请日：2023-01-03

Applicant: Apple Inc.

Inventor： Symeon Delikaris Manias ,  Mehrez Souden ,  Ante Jukic ,  Matthew S. Connolly ,  Sabine Webel ,  Ronald J. Guglielmone, Jr.

IPC: H04R3/00 ,  H04R5/04

CPC classification number: H04R3/005 ,  H04R5/04

Abstract: An audio renderer can have a machine learning model that jointly processes audio and visual information of an audiovisual recording. The audio renderer can generate output audio channels. Sounds captured in the audiovisual recording and present in the output audio channels are spatially mapped based on the joint processing of the audio and visual information by the machine learning model. Other aspects are described.

公开(公告)号：US11849291B2

公开(公告)日：2023-12-19

申请号：US17322539

申请日：2021-05-17

Applicant: Apple Inc.

Inventor： Mehrez Souden ,  Jason Wung ,  Ante Jukic ,  Ramin Pishehvar ,  Joshua D. Atkins

IPC: H04R3/04 ,  H04R3/00 ,  H04R5/04 ,  G10L25/78 ,  G10L21/0216 ,  G10L21/0208 ,  H04M9/08

CPC classification number: H04R3/04 ,  G10L21/0216 ,  G10L25/78 ,  H04R3/005 ,  H04R5/04 ,  G10L2021/02082 ,  G10L2021/02166 ,  H04M9/082

Abstract: A plurality of microphone signals can be captured with a plurality of microphones of the device. One or more echo dominant audio signals can be determined based on a pick-up beam directed towards one or more speakers of a playback device. Sound that is emitted from the one or more speakers and sensed by the plurality of microphones can be removed from plurality of microphone signals, by using the one or more echo dominant audio signals as a reference, resulting in clean audio.

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.

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.