Broadcom Corporation

Avago Technologies General IP (Singapore) Pte. Ltd.

Xianxian Zhang

Jes Thyssen

Juin-Hwey Chen

Huaiyu Zeng

Nelson Ray Sollenberger

Leopold Boemer

Kwan Young Shin

Alfonsus Lunardhi

Techniques are described herein that use sensors (e.g., microphones) for noise reduction in a mobile communication device. For example, one technique enables a first sensor that is initially configured to be a speech sensor to be used as a noise reference sensor. This technique also enables a second sensor that is initially configured to be a noise reference sensor to be used as a speech sensor. Another technique enables a primary sensor and/or a secondary sensor in a handset of a mobile communication device to be used as a speech sensor while a sensor in a headset of the mobile communication device is used as a noise reference sensor, or vice versa. In yet another technique, a secondary sensor in a mobile communication device is configured to be a directional sensor.

Use of sensors for noise suppression in a mobile communication device

Unlike sound based pressure waves that go everywhere, air turbulence caused by wind is usually a fairly local event. Therefore, in a system that utilizes two or more spatially separated microphones to pick up sound signals (e.g., speech), wind noise picked up by one of the microphones often will not be picked up (or at least not to the same extent) by the other microphone(s). Embodiments of methods and apparatuses that utilize this tact and others to effectively detect and suppress wind noise using multiple microphones that are spatially separated are described.

Method and apparatus for wind noise detection and suppression using multiple microphones

Described herein are multi-channel noise suppression systems and methods that are configured to detect and suppress wind and background noise using at least two spatially separated microphones: at least one primary speech microphone and at least one noise reference microphone. The multi-channel noise suppression systems and methods are configured, in at least one example, to first detect and suppress wind noise in the input speech signal picked up by the primary speech microphone and, potentially, the input speech signal picked up by the noise reference microphone. Following wind noise detection and suppression, the multi-channel noise suppression systems and methods are configured to perform further noise suppression in two stages: a first linear processing stage that includes a blocking matrix and an adaptive noise canceler, followed by a second non-linear processing stage.

System and method for multi-channel noise suppression

Multi-channel noise suppression systems and methods are described that omit the traditional delay-and-sum fixed beamformer in devices that include a primary speech microphone and at least one noise reference microphone with the desired speech being in the near-field of the device. The multi-channel noise suppression systems and methods use a blocking matrix (BM) to remove desired speech in the input speech signal received by the noise reference microphone to get a 'cleaner' background noise component. Then, an adaptive noise canceler (ANC) is used to remove the background noise in the input speech signal received by the primary speech microphone based on the 'cleaner' background noise component to achieve noise suppression. The filters implemented by the BM and ANC are derived using closed-form solutions that require calculation of time-varying statistics of complex frequency domain signals in the noise suppression system.

System and method for multi-channel noise suppression based on closed-form solutions and estimation of time-varying complex statistics

Unlike sound based pressure waves that go everywhere, air turbulence caused by wind is usually a fairly local event. Therefore, in a system that utilizes two or more spatially separated microphones to pick up sound signals (e.g., speech), wind noise picked up by one of the microphones often will not be picked up (or at least not to the same extent) by the other microphone(s). Embodiments of methods and apparatuses that utilize this fact and others to effectively detect and suppress wind noise using multiple microphones that are spatially separated are described.

Techniques are described herein that suppress noise using multiple sensors (e.g., microphones) of a communication device. Noise modeling (e.g., estimation of noise basis vectors and noise weighting vectors) is performed with respect to a noise signal during operation of a communication device to provide a noise model. The noise model includes noise basis vectors and noise coefficients that represent noise provided by audio sources other than a user of the communication device. Speech modeling (e.g., estimation of speech basis vectors and speech weighting) is performed to provide a speech model. The speech model includes speech basis vectors and speech coefficients that represent speech of the user. A noisy speech signal is processed using the noise basis vectors, the noise coefficients, the speech basis vectors, and the speech coefficients to provide a clean speech signal.

Noise suppression using multiple sensors of a communication device

Various embodiments of multiple microphone based pitch detection are provided. In one embodiment, a method includes obtaining a primary signal and a secondary signal associated with multiple microphones. A pitch value is determined based at least in part upon a level difference between the primary and secondary signals. In another embodiment, a system includes a plurality of microphones configured to provide a primary signal and a secondary signal. A level difference detector is configured to determine a level difference between the primary and secondary signals and a pitch identifier is configured to clip the primary and secondary signals based at least in part upon the level difference. In another embodiment, a method determines the presence of voice activity based upon a pitch prediction gain variation that is determined based at least in part upon a pitch lag.

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San Diego, CA, United States of America

Xianxian Zhang