Polycom, Inc.

U.S. Robotics Access Corp.

Plantronics, Inc.

Hewlett-Packard Development Company, L.p.

Georgia Tech Research Corporation

Kwan Kin Truong

Peter L Chu

Michael Pocino

Eric David Elias

Steven L Potts

Craig H Richardson

David L Smith

John J Proctor

Chris J Hodges

Jesse Coleman

Yibo Liu

Jacob T Meyberg Guzman

David Verl Anderson

Zhemin Tu

Stephen McGrath

Cody Schnacker

Gregory Pelton

Jinwei Feng

Takahiro Unno

Erwin Goesnar

Philip A DesJardins

James Steven Joiner

Russell Manning Mersereau

Andrew Stewart Lochbaum

John Allen

Steve Potts

Steve Joiner

Thomas P Barnwell, Iii

James M Sharp

Mark M Baldassari

Andrew Lochbaum

Michael A Pocino

A method includes, during a teleconference between a first audio input/output device and a second audio input/output device, receiving, at an analysis and response device, a signal indicating a spoken command, the spoken command associated with a command mode. The method further includes, in response to receiving the signal, generating, at the device, a reply message based on the spoken command, the reply message to be output to one or more devices selected based on the command mode. The one or more devices includes the first audio input/output device, the second audio input/output device, or a combination thereof.

Responding to a spoken command in a communication session

A method includes obtaining, at a first conference endpoint device, spoken command data representing a spoken command detected by the first conference endpoint device during a teleconference between the first conference endpoint device and a second conference endpoint device. The method further includes generating modified spoken command data by inserting a spoken phrase into the spoken command. The method further includes transmitting the modified spoken command data to a natural language service.

Modifying spoken commands

Replying to a spoken command

A method for hybrid noise suppression includes receiving a processed audio signal from an audio device. The processed audio signal results from a partial audio processing performed on a noisy audio input signal. The method further includes predicting a noise suppression parameter using a neural network model operating on the processed audio signal and generating a noise-suppressed audio signal from the processed audio signal, using the noise suppression parameter. The method further includes generating a noise-suppressed audio output signal from the noise-suppressed audio signal using an additional audio processing and outputting the noise-suppressed audio output signal.

Hybrid noise suppression for communication systems

A method for hybrid noise suppression. The method involves obtaining a noisy audio input signal, generating a noise-suppressed audio output signal by performing a combination of a noise spectrum estimate-based noise suppression and a machine learning-based noise suppression, and outputting the noise-suppressed audio output signal.

Hybrid noise suppression

A method for hybrid noise suppression involves receiving a first processed audio signal and a second processed audio signal from an audio device. The first processed audio signal results from a comprehensive audio processing including a noise spectrum estimate-based noise suppression performed on a noisy audio input signal obtained by the audio device. The second processed signal results from a partial audio processing excluding the noise spectrum estimate-based noise suppression performed on the noisy audio input signal. The method further involves temporally aligning the second processed audio signal with the first processed audio signal, predicting a noise suppression parameter using a neural network model operating on the second processed audio signal after the temporal alignment, generating a noise-suppressed audio output signal from the first processed audio signal after the temporal alignment using the noise suppression parameter, and outputting the noise-suppressed audio output signal.

The amount of far-field noise transmitted by a primary communication device in an open-plan office environment is reduced by defining an acoustic perimeter of reference microphones around the primary device. Reference microphones generate a reference audio input including far-field noise in the proximity of the primary device. The primary device generates a main audio input including the voice of the primary speaker as well as background noise. Reference audio input is compared to main audio input to identify the background noise portion of the main audio signal. A noise reduction algorithm suppresses the identified background noise in the main audio signal. The one or more reference microphones defining the acoustic perimeter may be included in separate microphone devices placed in proximity to the main desktop phone, microphones within other nearby desktop telephone devices, or a combination of both types of devices.

Acoustic perimeter for reducing noise transmitted by a communication device in an open-plan environment

A device includes one or more processor devices, an audio sensor, a speaker device, and one or more memory devices. The audio sensor is configured to generate an audio stream. The one or more memory devices store data including data identifying a wake phrase and instructions. The instructions are executable by the one or more processor devices to initiate, as a result of detecting the wake phrase in the audio stream and determining that the wake phrase is invalid, presentation via the speaker device of audio output identifying a valid wake input. The instructions are further executable by the one or more processors to initiate, as a result of detecting the valid wake input, communication of a portion of the audio stream to a natural language processing service.

Identifying a valid wake input

Traditional audio feedback elimination systems may attempt to reduce the effect of the audio feedback by simply scaling down the audio volume of the signal frequencies that are prone to howling. Other traditional feedback elimination systems may also employ adaptive notch filtering to detect and 'notch' the so-called 'singing' or &#x201c;howling&#x201d; frequencies as they occur in real-time. Such devices may typically have several knobs and buttons needing tuning, for example: the number of adaptive parametric equalizers (PEQs) versus fixed PEQs; attack and decay timers; and/or PEQ bandwidth. Rather than removing the singing frequencies with PEQs, the devices described herein attempt to holistically model the feedback audio and then remove the entire feedback signal. Two advantages of the devices described herein are: 1.) the system can operate at a much larger loop-gain (and hence with a much higher loudspeaker volume); and 2) setup is greatly simplified (i.e., no tuning knobs or buttons).

Advanced audio feedback reduction utilizing adaptive filters and nonlinear processing

Audio feedback reduction utilizing adaptive filters and nonlinear processing

Systems and methods for holistically modelling audio feedback and removing the entire feedback signal corresponding thereto. The systems can operate at a much larger loop-gain (and hence with a much higher loudspeaker volume), than those conventional systems which seek to remove singing frequencies with PEQs. The systems are an improvement over traditional audio feedback elimination systems which attempt to reduce the effect of the audio feedback by simply scaling down the audio volume of the signal frequencies that are prone to howling, and those feedback elimination systems which simply employ adaptive notch filtering to detect and 'notch' the so-called 'singing' or &#x201c;howling&#x201d; frequencies as they occur in real-time. Such devices may typically have several knobs and buttons needing tuning, for example: the number of adaptive parametric equalizers (PEQs) versus fixed PEQs; attack and decay timers; and/or PEQ bandwidth. The systems set forth herein obviate the need for tuning knobs or buttons, making set up easy.

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Johns Creek, GA, United States of America

Kwan Kin Truong