Microsoft Technology Licensing, LLC

Syavosh Zad Issa

Rod G Fleck

Youhong Lu

Hundraj S Gopal

Examples of the disclosure describe user environment aware single channel acoustic noise reduction. A noisy signal received by a computing device is transformed and feature vectors of the received noisy signal are determined. The computing device accesses classification data corresponding to a plurality of user environments. The classification data for each user environment has associated therewith a noise model. A comparison is performed between the determined feature vectors and the accessed classification data to identify a current user environment. A noise level, a speech level, and a speech presence probability from the transformed noisy signal are estimated and the noise signal is reduced based on the estimates. The resulting signal is outputted as an enhanced signal with a reduced or eliminated noise signal.

User environment aware acoustic noise reduction

Methods and systems for identifying sound from a source of interest are provided for herein. In some embodiments, a first audio feed is captured by a first microphone and a second audio feed is captured by a second microphone. The first microphone may be located closer in proximity to the source of interest than the second microphone. The first audio feed can be processed utilizing the second audio feed to produce a first processed audio feed that can enable identification of sound originating from the source of interest. In some embodiments, the second audio feed can be additionally processed utilizing the first audio feed to produce a second processed audio feed. In such embodiments, frequencies from the first processed audio feed can be compared against frequencies of the second processed audio feed to identify sound originating from the source of interest. Other embodiments may be described and/or claimed herein.

Identifying sound from a source of interest based on multiple audio feeds

Various technologies are applied to focus audio received from a plurality of microphones of a mobile device. A camera can be used to portray a scene, and a selection within the scene can focus audio to a desired audio focus region. Techniques can account for movement of the mobile device or an object being tracked. Pre-computed audio filters can be used to customize the audio focus process to account for a particular mobile device geometry.

Audio focusing via multiple microphones

Examples of the disclosure provide variable step size (VSS) adaptive echo cancellation in the presence of near-end noise such as dense double talk without using an explicit double talk detector and/or without using a dual-filter. During a conversation, the present value for an error signal is monitored. Based on the monitored present value for the error signal, a first function is determined. A second function is determined based on long-term statistics describing a reference signal, a near-end noise signal, and the error signal. An adaptation coefficient is calculated for the VSS adaptive filter based on the determined first function and the determined second function. The calculated adaptation coefficient is used in the VSS adaptive filter for echo cancellation against interference due to the near-end noise signal during the conversation.

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Kirkland, WA, United States of America

Syavosh Zad Issa