Motorola Solutions, Inc.

Shure Acquisition Holdings, Inc.

Daniel Aaron Law

Brent Robert Shumard

Bradley Michael Hiben

Francesca Schuler

Thomas S Messerges

Jehan Wickramasuriya

Michael Ryan Lester

Eric Johnson

David Grant Cason

Philip E May

Avinash K Vaidya

Christopher M Meyer

Kang Bum Lee

Shwetal S Parikh

Steven Norman Leib

Mark Swiderski

Randy L Ekl

Wei Lin

John Born

David Grundtvig

Gavin Baker

Iris Lorente

Kevin Othel Foy

Chad Wolf Reid

William Oakley

Stephen Varner

Christian Yost

Techniques for isolating audio signals related to audio sources within an audio environment are discussed herein. Examples may include receiving a plurality of audio data objects. Each audio data object includes digitized audio signals captured by a capture device positioned within an audio environment. Examples may also include inputting the audio data objects to a source localizer model that is configured to generate, based on the audio data objects, one or more audio source position estimate objects. Examples may also include inputting the audio data objects and each audio source position estimate object to a source generator model of one or more source generator models. The source generator model is configured to generate, based on the audio source position estimate object, a source isolated audio output component. The source isolated audio output component may include isolated audio signals associated with an audio source within the audio environment.

Audio signal isolation related to audio sources within an audio environment

An example immersive audio signal processing system and a computer-implemented method for generating a target arena environment audio stream are provided. The example immersive audio signal processing system includes a plurality of multi-lobe digital sound wave capture devices positioned within the arena environment. The plurality of multi-lobe digital sound wave capture devices is configured to direct first beamformed lobes to a playing region of the arena environment, second beamformed lobes to a spectator region of the arena environment, and third beamformed lobes to a noise source region of the arena environment. A digital signal processor is configured to isolate noise audio components originating from at least the spectator region or the noise source region from the audio signal stream and generate a target arena environment audio stream.

Multi-lobe digital microphone enabled audio capture and spatialization for generating an immersive arena based audio experience

Embodiments include an audio system comprising an audio device, a speaker, and a processor. The audio system is configured to receive data from one or more sensors corresponding to persons in a room and/or characteristics of a room, and responsively take action to modify one or more characteristics of the audio system, share the information with other systems or devices, and track data over time to determine patterns and trends in the data.

Intelligent audio system using multiple sensor modalities

Conferencing system and methods for facilitating conferencing sessions in an environment using virtual assistant systems and artificial intelligence algorithms are disclosed. Virtual assistant systems may be integrated into devices of the conferencing system in order to enable higher quality experiences with the virtual assistant by providing improved audio and visual feedback. The conferencing system can provide improved control, configuration, and interaction with the devices of the conferencing system through use of artificial intelligence algorithm that processes voice commands and characteristics of the environment and conferencing sessions, which can result in better system performance and increased user satisfaction.

Conferencing session facilitation systems and methods using virtual assistant systems and artificial intelligence algorithms

A computer apparatus, such as a validator node, for validating incident-related data records in a distributed electronic ledger is configured to receive a request from an agent to add a proposed incident-related data record associated with an incident to the distributed electronic ledger. In response to the proposed incident-related data record, the computer apparatus determines, from the distributed electronic ledger, one or more attributes of the agent or incident. The computer apparatus determines whether the proposed incident-related data record satisfies validation criteria based at least partially on the one or more attributes of the agent or incident. Once validated, the computer apparatus may then append the proposed incident-related data record to the distributed electronic ledger. Alternatively, the proposed incident-related data record may be appended regardless of validity, but an indication of validity may be appended if the proposed incident-related data record is validated.

System for validating and appending incident-related data records in a distributed electronic ledger

Receive first context information (FCI) including entered in-field incident timeline information values from an in-field incident timeline application and a time associated with an entry of the FCI values. Access a mapping that maps in-field incident timeline information values to events having a pre-determined threshold confidence of occurring and identify an event associated with the received FCI. Determine a location associated with the entry of the FCI and a time period associated with the entry of the FCI. Access a camera location database and identify cameras that have a field of view including the location during the time period. Retrieve audio and/or video streams captured by the cameras during the time period. And provide the audio and/or video streams to machine learning training modules corresponding to machine learning models for detecting the event in and/or video streams for further training of the machine learning models.

Method, device, and system for adaptive training of machine learning models via detected in-field contextual incident timeline entry and associated located and retrieved digital audio and/or video imaging

Methods and systems for differentiating an object in a video. One system includes an electronic computing device including an electronic processor configured to determine context information associated with a video. The electronic processor is further configured to determine an object included in the video to be differentiated from other objects included in the video. The electronic processor is further configured to determine a level of differentiation for the object based on the context information. The electronic processor is further configured to alter the video to create an altered video that shows the object differentiated from the other objects in accordance with the level of differentiation. The electronic processor is further configured to provide the altered video to a display.

Methods and systems for differentiating one or more objects in a video

Methods and systems for correlating cross-feed of audio and video. One system includes a display and an electronic processor connected to the display. The electronic processor is configured to receive a plurality of videos and a plurality of audio feeds, present an initial arrangement including one or more of the plurality of videos on the display, and generate a soundscape based on the initial arrangement, the soundscape including one or more of the plurality of audio feeds. The electronic processor is further configured to determine which of the videos of the plurality of videos contains a predetermined item of interest, determine which of the audio feeds of the plurality of audio feeds contains audio associated with the predetermined item of interest, and adjust the initial arrangement and the soundscape to highlight the predetermined item of interest.

Correlated cross-feed of audio and video

A device and method for generating text representative of lip movement is provided. One or more portions of video data are determined that include: audio with an intelligibility rating below a threshold intelligibility rating; and lips of a human face. A lip-reading algorithm is applied to the one or more portions of the video data to determine text representative of detected lip movement in the one or more portions of the video data. The text representative of the detected lip movement is stored in a memory. A transcript that includes the text representative of the detected lip movement may be generated. Captioned video data may be generated from the video data and the text representative of detected lip movement.

Device and method for generating text representative of lip movement

Receive first context information including sensor information values from in-field sensors and a time associated with a capture of the first context information. Access a context to detectable event mapping that maps sets of sensor information values to events and identify a particular event associated with the received first context information. Determine a geographic location associated with the in-field sensors and access an imaging camera location database and identify particular imaging cameras that have a field of view including the determined geographic location during the time associated with the capture of the first context information. Retrieve audio and/or video streams captured by the particular imaging cameras, identify machine learning training modules corresponding to machine learning models for detecting the particular event in audio and/or video streams, and provide the audio and/or video streams to the machine learning training modules for further training of the corresponding machine learning models.

Method, device, and system for adaptive training of machine learning models via detected in-field contextual sensor events and associated located and retrieved digital audio and/or video imaging

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Glencoe, IL, United States of America

Daniel Aaron Law