Amazon Technologies, Inc.

Rawles LLC

Ramya Gopalan

Amit Singh Chhetri

Kavitha Velusamy

Philip Ryan Hilmes

Wai Chung Chu

William Folwell Barton

Shiva Kumar Sundaram

Kenneth Edgar Hild, Ii

Jeffrey C O'Neill

Carlo Murgia

Wei Ey Li

Yue Liu

Sridhar Pilli

Paraskevas Argyropoulos

Xianxian Zhang

Praveen Jayakumar

The systems, devices, and processes described herein may identify a beam of a voice-controlled device that is directed toward a reflective surface, such as a wall. The beams may be created by a beamformer. An acoustic echo canceller (AEC) may create filter coefficients for a reference sound. The filter coefficients may be analyzed to identify beams that include multiple peaks. The multiple peaks may indicate presence of one or more reflective surfaces. Using the amplitude and the time delay between the peaks, the device may determine that it is close to a reflective surface in a direction of the beam.

Beam rejection in multi-beam microphone systems

Techniques for improving acoustic echo cancellation are described. Energy levels of audio data received from a microphone and representing near-end audio and reference audio data representing far-end audio are determined. If near-end audio is detected but far-end audio is not detected, a controller turns of or bypasses an acoustic echo cancellation system until far-end audio is again detected, thereby decreasing or eliminating distortion of the near-end audio by the acoustic echo cancellation system.

Acoustic echo cancellation control

A wearable computer is configured to use beamforming techniques to isolate a user's speech from extraneous audio signals occurring within a physical environment. A microphone array of the wearable computer may generate audio signal data from an utterance from a user's mouth. A motion sensor(s) of the wearable computer may generate motion data from movement of the wearable computer. This motion data may be used to determine a direction vector pointing from the wearable computer to the user's mouth, and a beampattern may be defined that has a beampattern direction in substantial alignment with the determined direction vector to focus the microphone array on the user's mouth for speech isolation.

Beamforming for a wearable computer

Devices and techniques are generally described for control of a voice-controlled device using acoustic echo cancellation statistics. A reference signal representing the audio stream may be sent to an acoustic echo cancellation (AEC) unit. A microphone may receive an input audio signal and send the input audio signal to the AEC unit. The AEC unit may attenuate at least a part of the input audio signal. AEC statistics related to the attenuation of at least the part of the input audio signal may be determined over a first period of time. A wake-word in the input audio signal may be detected during the first period of time. A determination may be made that the wake-word is part of the playback of the audio stream based at least in part on the AEC statistics.

Voice-controlled device control using acoustic echo cancellation statistics

A system configured to improve speech quality by performing residual echo suppression (RES). The system may detect when double talk is present in a voice conversation and may use different attenuation parameters based on a frequency of audio data. The system may perform RES on the audio data using a low attenuation value for low frequencies and a high attenuation value for high frequencies and determine that double talk is present when a difference in energy level between the low frequencies and the high frequencies is below a threshold. If double talk is present, the RES may generate output audio data using a low attenuation value for low frequencies and a high attenuation value for high frequencies. If double talk is not present, the RES may generate output audio data using a high attenuation value for low frequencies and a high attenuation value for high frequencies.

Methods for detecting double talk

Techniques for enhancing an acoustic echo canceller based on visual cues are described herein. The techniques include changing adaptation of a filter of the acoustic echo canceller, calibrating the filter, or reducing background noise from an audio signal processed by the acoustic echo canceller. The changing, calibrating, and reducing are responsive to visual cues that describe acoustic characteristics of a location of a device that includes the acoustic echo canceller. Such visual cues may indicate that no human being is present at the location, that some subject(s) are engaged in speaking or sound generating activities, or that motion associated with an echo path change has occurred at the location.

Acoustic echo cancellation using visual cues

Architectures and techniques for selecting a voice-enabled device to handle audio input that is detected by multiple voice-enabled devices are described herein. In some instances, multiple voice-enabled devices may detect audio input from a user at substantially the same time, due to the voice-enabled devices being located within proximity to the user. The architectures and techniques may analyze a variety of audio signal metric values for the voice-enabled devices to designate a voice-enabled device to handle the audio input.

Arbitration between voice-enabled devices

Embodiments of systems and methods are described for determining which of a plurality of beamformed audio signals to select for signal processing. In some embodiments, a plurality of audio input signals are received from a microphone array comprising a plurality of microphones. A plurality of beamformed audio signals are determined based on the plurality of input audio signals, the beamformed audio signals comprising a direction. A plurality of signal features may be determined for each beamformed audio signal. Smoothed features may be determined for each beamformed audio signal based on at least a portion of the plurality of signal features. The beamformed audio signal corresponding to the maximum smoothed feature may be selected for further processing.

Method and system for beam selection in microphone array beamformers

Features are disclosed for improving the accuracy and stability of beamformed signal selection. The selection may consider processing feedback information to identify when the current beam selection may need to be re-evaluated. The feedback information may further be used to select a beamformed signal for processing. For example, beams which detect wake-words or yield high confidence speech recognition may be favored over beams which fail to detect or recognize at a lower confidence level.

Feedback based beamformed signal selection

In a speech-based system, a wake word or other trigger expression is used to preface user speech that is intended as a command. The system receives multiple directional audio signals, each of which emphasizes sound from a different direction. The signals are monitored and analyzed to detect the directions of interfering audio sources such as televisions or other types of electronic audio players. One of the directional signals having the strongest presence of speech is selected to be monitored for the trigger expression. If the directional signal corresponds to the direction of an interfering audio source, a more strict standard is used to detect the trigger expression. In addition, the directional audio signal having the second strongest presence of speech may also be monitored to detect the trigger expression.

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Cupertino, CA, United States of America

Ramya Gopalan