Amazon Technologies, Inc.

Savaskan Bulek

Trausti Thor Kristjansson

Tarun Pruthi

Ali Abdollahzadeh Milani

Steven Sensarn

Pradeep Kumar Govindaraju

Yuzhou Liu

A system configured to detect a tap event on a surface of a device only using microphone audio data. For example, instead of using a physical sensor to detect the tap event, the device may detect a tap event in proximity to a microphone based on a power level difference between two or more microphones. When a power ratio exceeds a threshold, the device may detect a tap event and perform an action. For example, the device may output an alarm and use a detected tap event as an input to delay or end the alarm. In some examples, the device may detect a tap event using a plurality of microphones. Additionally or alternatively, the device may distinguish between multiple tap events based on a location of the tap event, enabling the device to perform two separate actions depending on the location.

Inter-channel level difference based acoustic tap detection

A computing device may receive audio data from a microphone representing audio in an environment of the device, which may correspond to an utterance and noise. A model may be trained to process the audio data to reduce noise from the audio data. The model may include an encoder that includes one or more dense layers, one or more recurrent layers, and a decoder that includes one or more dense layers.

Systems and methods for noise reduction

A system configured to detect a tap event on a surface of a device using microphone audio data and motion data. Instead of using a physical sensor to detect the tap event, the device detects a tap event based on a power level difference between two or more microphones. When a power ratio exceeds a threshold, the device may detect a tap event and perform an action, such as delaying or ending an alarm. To reduce false positives caused by wind or loud noises close to the microphones, the device may confirm a tap event using motion data that indicates actual movement of the device. In some examples, the device may detect the tap event using a neural network processing the microphone data and the motion data. In addition, the device may embed the motion data within the microphone data using unused bits of the microphone data.

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Sunnyvale, CA, United States of America

Savaskan Bulek