公开(公告)号：US20160071526A1

公开(公告)日：2016-03-10

申请号：US14847818

申请日：2015-09-08

Applicant: ANALOG DEVICES, INC.

Inventor： DAVID WINGATE ,  NOAH DANIEL STEIN ,  BENJAMIN VIGODA ,  PATRICK OHIOMOBA ,  BRIAN DONNELLY

IPC: G10L21/028 ,  G10L25/84 ,  G10L19/022 ,  G01S3/802 ,  G10L21/0232 ,  G10L21/0264

CPC classification number: G10L21/028 ,  G01S3/802 ,  G01S3/807

Abstract: The present disclosure relates generally to improving acoustic source tracking and selection and, more particularly, to techniques for acoustic source tracking and selection using motion or position information. Embodiments of the present disclosure include systems designed to select and track acoustic sources. In one embodiment, the system may be realized as an integrated circuit including a microphone array, motion sensing circuitry, position sensing circuitry, analog-to-digital converter (ADC) circuitry configured to convert analog audio signals from the microphone array into digital audio signals for further processing, and a digital signal processor (DSP) or other circuitry for processing the digital audio signals based on motion data and other sensor data. Sensor data may be correlated to the analog or digital audio signals to improve source separation or other audio processing.

Abstract translation: 本公开一般涉及改进声源跟踪和选择，更具体地，涉及使用运动或位置信息进行声源跟踪和选择的技术。 本公开的实施例包括被设计为选择和跟踪声源的系统。 在一个实施例中，该系统可被实现为包括麦克风阵列，运动感测电路，位置感测电路，被配置为将来自麦克风阵列的模拟音频信号转换成数字音频信号的模数转换器（ADC）电路的集成电路 用于进一步处理，以及用于基于运动数据和其他传感器数据处理数字音频信号的数字信号处理器（DSP）或其它电路。 传感器数据可以与模拟或数字音频信号相关联，以改善源分离或其他音频处理。

公开(公告)号：US20170178664A1

公开(公告)日：2017-06-22

申请号：US15129802

申请日：2015-03-26

Applicant: ANALOG DEVICES, INC.

Inventor： DAVID WINGATE ,  BENJAMIN VIGODA ,  PATRICK OHIOMOBA ,  BRIAN DONNELLY ,  NOAH DANIEL STEIN

IPC: G10L21/0308 ,  G10L25/30 ,  G10L21/0232

CPC classification number: G10L21/0308 ,  G10L15/16 ,  G10L21/0232 ,  G10L21/028 ,  G10L25/30 ,  G10L2021/02166

Abstract: Use of spoken input for user devices, e.g. smartphones, can be challenging due to presence of other sound sources. Blind source separation (BSS) techniques aim to separate a sound generated by a particular source of interest from a mixture of different sounds. Various BSS techniques disclosed herein are based on recognition that providing additional information that is considered within iterations of a nonnegative tensor factorization (NTF) model improves accuracy and efficiency of source separation. Examples of such information include direction estimates or neural network models trained to recognize a particular sound of interest. Furthermore, identifying and processing incremental changes to an NTF model, rather than re-processing the entire model each time data changes, provides an efficient and fast manner for performing source separation on large sets of quickly changing data. Carrying out at least parts of BSS techniques in a cloud allows flexible utilization of local and remote sources.