Dolby Laboratories Licensing Corporation

Dolby International Ab

Lake Technology Limited

Richard James Cartwright

Glenn N Dickins

David S McGrath

Mark R P Thomas

Andrew Peter Reilly

David Gunawan

Alan Jeffrey Seefeldt

Xuejing Sun

Joshua Brandon Lando

Hannes Muesch

Daniel Arteaga

Timothy Alan Port

Christopher Graham Hines

Stephen James Bennett

Adam Richard McKeag

Heiko Purnhagen

Benjamin Alexander Jancovich

Leonard Layton

Nigel Lloyd William Heyler

Geoffrey Alexander Drane

Glen Norman Dickins

Lars Villemoes

Kai Lin Li

Leif Jonas Samuelsson

Shen Huang

Toni Hirvonen

Kai Li

Janusz Klejsa

Michael Eckert

Michael P Hollier

Craig Johnston

Carlos Eduardo Medaglia Dyonisio

Jia Dai

Hadis Nosrati

Carlos Medaglia Dyonisio

Nigel Lloyd William Helyer

Roger David Butler

A method for selecting a device for audio processing may involve receiving a first wakeword confidence metric from a first device that includes at least a first microphone and receiving a second wakeword confidence metric from a second device that includes at least a second microphone. The first and second wakeword confidence metrics may correspond to a first local maximum of a first plurality of wakeword confidence values determined by the first device and a second local maximum of a second plurality of wakeword confidence values determined by the second device. The method may involve comparing the first wakeword confidence metric and the second wakeword confidence metric and selecting a device for subsequent audio processing based, at least in part, on a comparison of the first wakeword confidence metric and the second wakeword confidence metric.

Multi-device wakeword detection

An audio session management method for an audio environment having multiple audio devices may involve receiving, from a first device implementing a first application and by a device implementing an audio session manager, a first route initiation request to initiate a first route for a first audio session. The first route initiation request may indicate a first audio source and a first audio environment destination. The first audio environment destination may correspond with at least a first person in the audio environment, but in some instances will not indicate an audio device. The method may involve establishing a first route corresponding to the first route initiation request. Establishing the first route may involve determining a first location of at least the first person in the audio environment, determining at least one audio device for a first stage of the first audio session and initiating or scheduling the first audio session.

Coordination of audio devices

Some noise compensation methods involve receiving microphone signals corresponding to ambient noise from a noise source location in or near an audio environment, determining or estimating a listener position in the audio environment and estimating at least one critical distance, which is a distance from the noise source location at which directly propagated sound pressure is equal to diffuse field sound pressure. Some examples involve estimating whether the listener position is within the at least one critical distance and implementing a noise compensation method for the ambient noise based, at least in part, on an estimate of whether the listener position is within the critical distance.

Methods for reducing error in environmental noise compensation systems

A computer implemented system for rendering captured audio soundfields to a listener comprises apparatus to deliver the audio soundfields to the listener. The delivery apparatus delivers the audio soundfields to the listener with first and second audio elements perceived by the listener as emanating from first and second virtual source locations, respectively, and with the first audio element and/or the second audio element delivered to the listener from a third virtual source location. The first virtual source location and the second virtual source location are perceived by the listener as being located to the front of the listener, and the third virtual source location is located to the rear or the side of the listener.

Method of rendering one or more captured audio soundfields to a listener

A feature vector may be extracted from each frame of input digitized microphone audio data. The feature vector may include a power value for each frequency band of a plurality of frequency bands. A feature history data structure, including a plurality of feature vectors, may be formed. A normalized feature set that includes a normalized feature data structure may be produced by determining normalized power values for a plurality of frequency bands of each feature vector of the feature history data structure. A signal recognition or modification process may be based, at least in part, on the normalized feature data structure.

Method and apparatus for normalizing features extracted from audio data for signal recognition or modification

Noise compensation method comprising: (a) receiving a content stream including content audio data; (b) receiving first microphone signals from a first device; (c) detecting ambient noise from a noise source location in or near the audio environment; (d) causing a first wireless signal to be transmitted from the first device to a second device, the first wireless signal including instructions for the second device to record an audio segment (e) receiving a second wireless signal from the second device; (f) determining a content stream audio segment time interval for a content stream audio segment; (g) receiving a third wireless signal from the second device, including a recorded audio segment captured via a second device microphone; (h) determining a second device ambient noise signal at the second device location; and (i) implementing a noise compensation method for the content audio data based, at least in part, on the second device ambient noise signal.

A method for estimating a user's location in an environment may involve receiving output signals from each microphone of a plurality of microphones in the environment. At least two microphones of the plurality of microphones may be included in separate devices at separate locations in the environment and the output signals may correspond to a current utterance of a user. The method may involve determining multiple current acoustic features from the output signals of each microphone and applying a classifier to the multiple current acoustic features. Applying the classifier may involve applying a model trained on previously-determined acoustic features derived from a plurality of previous utterances made by the user in a plurality of user zones in the environment. The method may involve determining, based at least in part on output from the classifier, an estimate of the user zone in which the user is currently located.

Acoustic zoning with distributed microphones

An audio processing method may involve receiving output signals from each microphone of a plurality of microphones in an audio environment, the output signals corresponding to a current utterance of a person and determining, based on the output signals, one or more aspects of context information relating to the person, including an estimated current proximity of the person to one or more microphone locations. The method may involve selecting two or more loudspeaker-equipped audio devices based, at least in part, on the one or more aspects of the context information, determining one or more types of audio processing changes to apply to audio data being rendered to loudspeaker feed signals for the audio devices and causing one or more types of audio processing changes to be applied. In some examples, the audio processing changes have the effect of increasing a speech to echo ratio at one or more microphones.

Acoustic echo cancellation control for distributed audio devices

The present disclosure relates to the field of audio enhancement, and in particular to methods, devices and software for supervised training of a machine learning model, MLM, the MLM trained to enhance a degraded audio signal by calculating gains to be applied to frequency bands of the degraded audio signal. The present disclosure further relates to methods, devices and software for use of such a trained MLM.

System and method for enhancement of a degraded audio signal

An audio session management method may involve: determining, by an audio session manager, one or more first media engine capabilities of a first media engine of a first smart audio device, the first media engine being configured for managing one or more audio media streams received by the first smart audio device and for performing first smart audio device signal processing for the one or more audio media streams according to a first media engine sample clock; receiving, by the audio session manager and via a first application communication link, first application control signals from the first application; and controlling the first smart audio device according to the first media engine capabilities, by the audio session manager, via first audio session management control signals transmitted to the first smart audio device via a first smart audio device communication link and without reference to the first media engine sample clock.

An apparatus and method relating to use of a physical writing surface () during a videoconference or presentation. Snapshots of a whiteboard () are identified by applying a difference measure to the video data (e.g., as a way of comparing frames at different times). Audio captured by a microphone may be processed to generate textual data, wherein a portion of the textual data is associated with each snapshot. The writing surface may be identified (enrolled) using gestures. Image processing techniques may be used to transform views of a writing surface.

Sharing physical writing surfaces in videoconferencing

Teleconference audio data including a plurality of individual uplink data packet streams, may be received during a teleconference. Each uplink data packet stream may corresponding to a telephone endpoint used by one or more teleconference participants. The teleconference audio data may be analyzed to determine a plurality of suppressive gain coefficients, which may be applied to first instances of the teleconference audio data during the teleconference, to produce first gain-suppressed audio data provided to the telephone endpoints during the teleconference. Second instances of the teleconference audio data, as well as gain coefficient data corresponding to the plurality of suppressive gain coefficients, may be sent to a memory system as individual uplink data packet streams. The second instances of the teleconference audio data may be less gain-suppressed than the first gain-suppressed audio data.

Post-teleconference playback using non-destructive audio transport

Various disclosed implementations involve processing and/or playback of a recording of a conference involving a plurality of conference participants. Some implementations disclosed herein involve receiving audio data corresponding to a recording of at least one conference involving a plurality of conference participants. In some examples, only a portion of the received audio data will be selected as playback audio data. The selection process may involve a topic selection process, a talkspurt filtering process and/or an acoustic feature selection process. Some examples involve receiving an indication of a target playback time duration. Selecting the portion of audio data may involve making a time duration of the playback audio data within a threshold time difference of the target playback time duration.

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Killara, Australia

Richard James Cartwright