公开(公告)号：US09516413B1

公开(公告)日：2016-12-06

申请号：US14503175

申请日：2014-09-30

Applicant: Apple Inc.

Inventor： Vasu Iyengar ,  Aram M. Lindahl

IPC: H04R3/02 ,  H04W4/02

CPC classification number: H04W4/02 ,  H04R2227/007 ,  H04S7/301 ,  H04S7/305

Abstract: Systems and methods are described for storing and reusing previously generated/calculated acoustic environment data. By reusing acoustic environment data, the systems and methods described herein may avoid the increased overhead in generating/calculating acoustic environment data for a location when this data has already been generated and is likely accurate. In particular, the time and complexity involved in determining reverberation/echo levels, noise levels, and noise types may be avoided when this information is available in storage. This previously stored acoustic environment data may not be limited to data generated/calculated by the same audio device. Instead, in some embodiments an audio device may access a centralized repository to leverage acoustic environment data generated/calculated by other audio devices.

Abstract translation: 描述了用于存储和重新使用先前生成/计算的声学环境数据的系统和方法。 通过重新使用声学环境数据，本文所描述的系统和方法可以避免在该数据已经被生成并且可能是准确的情况下在产生/计算位置的声学环境数据时增加的开销。 特别地，当该信息在存储器中可用时，可以避免确定混响/回波电平，噪声电平和噪声类型所涉及的时间和复杂性。 该先前存储的声学环境数据可以不限于由相同的音频设备产生/计算的数据。 相反，在一些实施例中，音频设备可以访问中央存储库以利用由其他音频设备生成/计算的声学环境数据。

公开(公告)号：US20150221322A1

公开(公告)日：2015-08-06

申请号：US14170136

申请日：2014-01-31

Applicant: Apple Inc.

Inventor： Vasu Iyengar ,  Aram M. LindahI

IPC: G10L25/84 ,  G10L21/0208

CPC classification number: G10L25/84 ,  G10L2021/02165 ,  G10L2025/786

Abstract: A method for adapting a threshold used in multi-channel audio voice activity detection. Strengths of primary and secondary sound pick up channels are computed. A separation, being a measure of difference between the strengths of the primary and secondary channels, is also computed. An analysis of the peaks in separation is performed, e.g. using a leaky peak capture function that captures a peak in the separation and then decays over time, or using a sliding window min-max detector. A threshold that is to be used in a voice activity detection (VAD) process is adjusted, in accordance with the analysis of the peaks. Other embodiments are also described and claimed.

Abstract translation: 一种用于调整在多声道音频语音活动检测中使用的阈值的方法。 计算一次和二次声音拾取通道的强度。 还计算出分离，作为主要和次要信道强度差异的量度。 进行分离峰的分析，例如 使用泄漏峰值捕获功能，捕获分离中的峰值，然后随时间衰减，或使用滑动窗口最小 - 最大检测器。 根据峰值的分析，调整在语音活动检测（VAD）过程中使用的阈值。 还描述和要求保护其他实施例。

公开(公告)号：US10553235B2

公开(公告)日：2020-02-04

申请号：US16256587

申请日：2019-01-24

Applicant: Apple Inc.

Inventor： Nicholas J. Bryan ,  Vasu Iyengar ,  Aram M. Lindahl

IPC: G10L21/028 ,  G10L21/02 ,  G10L21/0232 ,  G10L17/00

Abstract: A method for controlling a speech enhancement process in a far-end device, while engaged in a voice or video telephony communication session over a communication link with a near-end device. A near-end user speech signal is produced, using a microphone to pick up speech of a near-end user, and is analyzed by an automatic speech recognizer (ASR) without being triggered by an ASR trigger phrase or button. The recognized words are compared to a library of phrases to select a matching phrase, where each phrase is associated with a message that represents an audio signal processing operation. The message associated with the matching phrase is sent to the far-end device, which is used to configure the far-end device to adjust the speech enhancement process that produces the far-end speech signal. Other embodiments are also described.

公开(公告)号：US10535362B2

公开(公告)日：2020-01-14

申请号：US15909513

申请日：2018-03-01

Applicant: Apple Inc.

Inventor： Nicholas J. Bryan ,  Vasu Iyengar

IPC: G10L21/0232 ,  G10L21/028 ,  H04R3/00 ,  G10L21/02 ,  G10L21/0216

Abstract: Signals are received from audio pickup channels that contain signals from multiple sound sources. The audio pickup channels may include one or more microphones and one or more accelerometers. Signals representative of multiple sound sources are generated using a blind source separation algorithm. It is then determined which of those signals is deemed to be a voice signal and which is deemed to be a noise signal. The output noise signal may be scaled to match a level of the output voice signal, and a clean speech signal is generated based on the output voice signal and the scaled noise signal. Other aspects are described.

公开(公告)号：US20190272842A1

公开(公告)日：2019-09-05

申请号：US15909513

申请日：2018-03-01

Applicant: Apple Inc.

Inventor： Nicholas J. Bryan ,  Vasu Iyengar

IPC: G10L21/0232 ,  G10L21/028 ,  G10L21/02 ,  H04R3/00

Abstract: Signals are received from audio pickup channels that contain signals from multiple sound sources. The audio pickup channels may include one or more microphones and one or more accelerometers. Signals representative of multiple sound sources are generated using a blind source separation algorithm. It is then determined which of those signals is deemed to be a voice signal and which is deemed to be a noise signal. The output noise signal may be scaled to match a level of the output voice signal, and a clean speech signal is generated based on the output voice signal and the scaled noise signal. Other aspects are described.

公开(公告)号：US10090001B2

公开(公告)日：2018-10-02

申请号：US15225595

申请日：2016-08-01

Applicant: Apple Inc.

Inventor： Lalin S. Theverapperuma ,  Vasu Iyengar ,  Sarmad Aziz Malik ,  Raghavendra Prabhu

IPC: G10L25/30 ,  G10L21/0232 ,  G10L25/72 ,  G10L25/84 ,  G10L21/028

Abstract: Method of speech enhancement using Neural Network-based combined signal starts with training neural network offline which includes: (i) exciting at least one accelerometer and at least one microphone using training accelerometer signal and training acoustic signal, respectively. The training accelerometer signal and the training acoustic signal are correlated during clean speech segments. Training neural network offline further includes(ii) selecting speech included in the training accelerometer signal and in the training acoustic signal, and (iii) spatially localizing the speech by setting a weight parameter in the neural network based on the selected speech included in the training accelerometer signal and in the training acoustic signal. The neural network that is trained offline is then used to generate a speech reference signal based on an accelerometer signal from the at least one accelerometer and an acoustic signal received from the at least one microphone. Other embodiments are described.

公开(公告)号：US20180033449A1

公开(公告)日：2018-02-01

申请号：US15225595

申请日：2016-08-01

Applicant: Apple Inc.

Inventor： Lalin S. Theverapperuma ,  Vasu Iyengar ,  Sarmad Aziz Malik ,  Raghavendra Prabhu

IPC: G10L25/30 ,  G10L25/72 ,  G10L21/0232 ,  G10L25/84 ,  G10L21/028

CPC classification number: G10L25/30 ,  G10L21/0232 ,  G10L21/028 ,  G10L25/72 ,  G10L25/84

Abstract: Method of speech enhancement using Neural Network-based combined signal starts with training neural network offline which includes: (i) exciting at least one accelerometer and at least one microphone using training accelerometer signal and training acoustic signal, respectively. The training accelerometer signal and the training acoustic signal are correlated during clean speech segments. Training neural network offline further includes (ii) selecting speech included in the training accelerometer signal and in the training acoustic signal, and (iii) spatially localizing the speech by setting a weight parameter in the neural network based on the selected speech included in the training accelerometer signal and in the training acoustic signal. The neural network that is trained offline is then used to generate a speech reference signal based on an accelerometer signal from the at least one accelerometer and an acoustic signal received from the at least one microphone. Other embodiments are described.

公开(公告)号：US20130332157A1

公开(公告)日：2013-12-12

申请号：US13911915

申请日：2013-06-06

Applicant: Apple Inc.

Inventor： Vasu Iyengar ,  Sorin V. Dusan

IPC: G10L15/20

CPC classification number: G10L15/20 ,  G10L21/0224 ,  G10L21/0232 ,  G10L2021/02165 ,  G10L2025/783

Abstract: Digital signal processing techniques for automatically reducing audible noise from a sound recording that contains speech. A noise suppression system uses two types of noise estimators, including a more aggressive one and less aggressive one. Decisions are made on how to select or combine their outputs into a usable noise estimate in a different speech and noise conditions. A 2-channel noise estimator is described. Other embodiments are also described and claimed.

Abstract translation: 用于自动降低包含语音的录音的可听噪声的数字信号处理技术。 噪声抑制系统使用两种类型的噪声估计器，包括更具侵略性的一个或更少的噪声估计器。 决定如何在不同的语音和噪声条件下将其输出选择或组合成可用的噪声估计。 描述2通道噪声估计器。 还描述和要求保护其他实施例。

公开(公告)号：US12293753B2

公开(公告)日：2025-05-06

申请号：US18409693

申请日：2024-01-10

Applicant: Apple Inc.

Inventor： Yang Lu ,  Andrew P. Bright ,  Fatos Myftari ,  Vasu Iyengar

IPC: G10K11/178

Abstract: Aspects of the subject technology provide for generation of a self-voice signal by an electronic device that is operating in an active noise cancellation mode. In this way, during a phone call, a video conference, or while listening to audio content, a user of the electronic device may benefit from active cancellation of ambient noise while still being able to hear their own voice when they speak. In various implementations described herein, the concurrent self-voice and automatic noise cancellation features are facilitated by accelerometer-based control of sidetone and/or active noise cancellation operations.

公开(公告)号：US11935512B2

公开(公告)日：2024-03-19

申请号：US17746930

申请日：2022-05-17

Applicant: Apple Inc.

Inventor： Yang Lu ,  Andrew P. Bright ,  Fatos Myftari ,  Vasu Iyengar

IPC: G10K11/178

CPC classification number: G10K11/17853

Abstract: Aspects of the subject technology provide for generation of a self-voice signal by an electronic device that is operating in an active noise cancellation mode. In this way, during a phone call, a video conference, or while listening to audio content, a user of the electronic device may benefit from active cancellation of ambient noise while still being able to hear their own voice when they speak. In various implementations described herein, the concurrent self-voice and automatic noise cancellation features are facilitated by accelerometer-based control of sidetone and/or active noise cancellation operations.