Amazon Technologies, Inc.

Zhouhui Miao

Carlo Murgia

Kuan-Chieh Yen

Aditya Sharadchandra Joshi

Ian Ernan Liu

Madhuri Saraf

Huiqun Han

Kunal Atitkar

This disclosure describes, in part, techniques to process audio signals to lessen the impact that wind and/or other environmental noise has upon the resulting quality of audio signals output on speaker(s) of wireless earbuds or other headphones. For example, the techniques may determine a level of wind and/or other noise in an environment and may determine how best to process the signals to lessen the impact of the noise, such that one or more users that hear audio based on output of the signals hear higher-quality audio. In some instances, the techniques are able to detect levels of wind in a relatively low-power manner and as part of regular operation of the wireless earbuds or other headphones.

Mitigating effects of wind in audio data

A system configured to perform audio processing with adaptive multi-stage output gains. For example, an Audio Front End (AFE) component may generate a first output using a fixed gain value in order to improve device arbitration and a second output using an adaptive gain value in order to improve wakeword detection. A wakeword engine may process the second output to determine that a wakeword is present along with start/end times of the wakeword. The AFE component can use the start/end times to determine an amount of wakeword energy represented in the first output, which is sent to a remote device for device arbitration. The AFE component can also use the start/end times to determine an amount of wakeword energy represented in the second output, which can be used to determine the adaptive gain value that is unique to the device.

Adaptive multi-stage output gain

A system configured to perform microphone occlusion event detection. When a device detects a microphone occlusion event, the device will modify audio processing performed prior to speech processing, such as by disabling spatial processing and only processing audio data from a single microphone. The device detects the microphone occlusion event by determining inter-level difference (ILD) values between two microphone signals and using the ILD values as input features to a classifier. For example, when a far-end reference signal is inactive, the classifier may process a first ILD value within a high frequency band. However, when the far-end reference signal is active, the classifier may process the first ILD value and a second ILD value within a low frequency band.

Microphone occlusion detection

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Saratoga, CA, United States of America

Zhouhui Miao