Amazon Technologies, Inc.

Michael Mark Goodwin

John Joseph Dunne

Karim Helwani

Siddhartha Shankara Rao

Jean-Marc Valin

Paris Smaragdis

Arvindh Krishnaswamy

Erfan Soltanmohammadi

Michael Klingbeil

Mehmet Umut Isik

Richard Newman

Srikanth Venkata Tenneti

Curtis Gill Hartmann

Jan Buethe

Ahmed Mustafa

Masahito Togami

Thushara Paul

Amalavoyal Chari

Sid Shankara Rao

An audio assessment machine learning model, for example including one or more neural networks, may be trained for human audio quality assessment estimation, wherein the training comprises performing comparisons of a plurality of training machine learning audio quality assessments of training audio content to a plurality of human audio quality assessments of the training audio content and adjusting the audio assessment machine learning model based on the comparisons. After the training, a first audio analysis of first audio content may be performed by the audio assessment machine learning model. A first machine learning audio quality assessment of the first audio content may be provided, by the audio assessment machine learning model, based on the first audio analysis. The first machine learning audio quality assessment may include a quality score for the first audio content and a quality degradation reason for the first audio content.

Machine learning model that estimates human audio quality assessments

System and methods are provided for estimating the sound field from partial observations. Estimating an acoustic environment for virtual reality and augmented reality applications is a step in the creation of simulated acoustic sound scenes. In particular, the impulse responses of room can be estimated with a generative model. In a teleconferencing scenario with remote participants and a group of participants in a common physical space, giving the remote participants the impression that all other participants are sitting is in the same room acoustically requires filtering the speech of the remote participants with impulse responses estimated at the desired rendering position in the conference room.

Manifold learning for sound field estimation

A computing device may generate a first cryptographic key data for communicating with a second computing device. A computing device may receive, by a microphone of the computing device, a sound information, wherein the first sound information represents speech occurring during a multi-participant conversation. A computing device may generate a sound embedding using the sound information. A computing device may compare the sound embedding to each speaker embedding of a set of speaker embeddings stored in a memory of the computing device to generate a result. A computing device may identify, based on the result, a current active speaker. A computing device may generate a second cryptographic key data, based in part on a speaker embedding associated with the current active speaker.

Voice identification assisted end-to-end cryptography

Efficient voice synthesis using frame-based processing may be performed. An audio processing system converts an input speech waveform to an acoustic feature representation, which includes a sequence of frames at a lower resolution than the sampling resolution of the input waveform. The system propagates the acoustic feature representation through GRUs and fully-connected layers, while maintaining the lower resolution. At the end, the system performs a flattening operation on the frames of the final acoustic feature representation to generate an output waveform at a target sampling resolution.

Efficient voice synthesis using frame-based processing

A videoconference among a plurality of participants may be hosted, wherein the plurality of participants comprise Internet Protocol (IP)-connected participants and a Public Switched Telephone Network (PSTN)-connected participant. The IP-connected participants may send and receive audio content and video content of the videoconference via IP-based connections. The PSTN-connected participant may send and receive the audio content of the videoconference via a PSTN connection. Additional content from the videoconference may also be transmitted to the PSTN-connected participant, for example as text messages via the PSTN connection. The additional content may include, for example, images of a videoconference screen share, chat posts, polls, and the like. Images may be transmitted in the additional content based on video status change events, such as switching slides or pages in a screen share. In some examples, bidirectional messaging may allow contents of text messages from the PSTN-connected user to be displayed in the videoconference.

Bidirectional videoconference-related messaging for public switched telephone network participants

Real-time low-complexity stereo speech enhancement with spatial cue preservation may be performed. A stereo speech enhancement system receives a stereo input signal (e.g., a left and right input signal). The stereo speech enhancement system estimates spatial cues for a target speaker and downmixes the stereo input signal into a monaural signal. A low-complexity model may then process the monaural signal to generate an enhanced monaural signal. The stereo speech enhancement system upmixes the enhanced monaural signal based on the estimated spatial cues for the target speaker, to generate an enhanced stereo output signal.

Real-time low-complexity stereo speech enhancement with spatial cue preservation

A plurality of device-sharing participants may be detected that are participating in a videoconference via a shared computing device. The detecting of the plurality of device-sharing participants may be performed based, at least in part, on at least one of an audio analysis of captured audio from one or more microphones or a video analysis of captured video from one or more cameras. A plurality of participant connections corresponding to the plurality of device-sharing participants may be joined to the videoconference. Each of the plurality of participant connections may be identified within the videoconference using a respective name. A plurality of video streams and a plurality of audio streams corresponding to the plurality of participant connections may be transmitted, and the plurality of video streams and the plurality of audio streams may be presented to at least one other conference participant.

Separate representations of videoconference participants that use a shared device

Joint noise and echo suppression may be performed for enhancing two-way audio communications. Audio data is captured at a communication device and audio data transmitted to the communication device from another communication device are used as input features to a trained machine learning model that uses the transmitted audio data as a reference signal to eliminate residual echo in the captured audio data when also suppressing noise in the captured audio data.

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Scotts Valley, CA, United States of America

Michael Mark Goodwin