Contextual dynamic selection of machine learning models for optimization of audio processing inside environments
AssigneeQsc, LLC
InventorJainish Nileshkumar Chauhan
Life of this patent
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Abstract
Described herein are systems to monitor the context of an environment in real-time and select an appropriate machine learning model from a remote location (e.g., cloud platform) to be deployed on an audio, video and control (“AVC”) system inside the environment. The contextual data provides the system with situational awareness of the environment. The AVC system includes one or more core processors and peripheral equipment. Through deployment of the machine learning models, the operating system being executed by the AVC system performs all of audio, video, and control processing on the processing core.
Claims50
A computer-implemented method to select a machine learning model for deployment in an environment, comprising:
- capturing at least one of audio or video data using a microphone or camera, respectively, positioned within the environment;
- processing at least one of the audio or video data to generate contextual data regarding the environment;
- analyzing the contextual data;
- based upon the analysis of the contextual data, selecting one or more machine learning models stored in a location remote from the environment;
- deploying the selected one or more machine learning models from the remote location to a location within the environment;
- and
- applying the one or more machine learning models to optimize audio characteristics of the environment.
A system, comprising:
- at least one of a microphone or camera located in a environment;
- and
- a processing device communicably coupled to the microphone and camera, the processing device having an audio optimization and control (“AOC”) operating system executable thereon to manage and control functionality of the microphone and camera, the processing device being configured to perform operations comprising:
- capturing at least one of audio or video data using the microphone or camera, respectively;
- processing at least one of the audio or video data to generate contextual data regarding the environment;
- analyzing the contextual data;
- based upon the analysis of the contextual data, selecting one or more machine learning models stored in a location remote from the environment;
- deploying the selected one or more machine learning models from the remote location to a location within the environment;
- and
- applying the one or more machine learning models to optimize audio characteristics of the environment.
A non-transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
- capturing at least one of audio or video data using a microphone or camera, respectively, positioned within a environment;
- processing at least one of the audio or video data to generate contextual data regarding the environment;
- analyzing the contextual data;
- based upon the analysis of the contextual data, selecting one or more machine learning models stored in a location remote from the environment;
- deploying the selected one or more machine learning models from the remote location to a location within the environment;
- and
- applying the one or more machine learning models to optimize audio characteristics of the environment.
A computer-implemented method to select a machine learning model for deployment in a environment, comprising:
- generating a repository of contextual data for a environment;
- determining, using the repository of contextual data, which one or more machine learning models have most frequently been deployed inside the environment;
- and
- deploying, from a location remote from the environment, those frequent machine learning models inside the environment.
A system, comprising:
- at least one of a microphone or camera located in a environment;
- a first processing device communicably coupled to the microphone and camera, the first processing device having an audio optimization and control (“AOC”) operating system executable thereon to manage and control functionality of the microphone and camera;
- and a second processing device located at a location remote from the first processing device, wherein at least one of the first or second processing devices being configured to perform operations comprising:
- generating a repository of contextual data for the environment;
- determining, using the repository of contextual data, which one or more machine learning models have most frequently been deployed inside the environment;
- and
- deploying, from the remote location, those frequent machine learning models inside the environment.
A non-transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
- generating a repository of contextual data for a environment;
- determining, using the repository of contextual data, which one or more machine learning models have most frequently been deployed inside the environment;
- and
- deploying, from a location remote from the environment, those frequent machine learning models inside the environment.
A computer-implemented method to select a machine learning model for deployment in an environment, comprising:
- capturing at least one of audio or video data using a microphone or camera, respectively, positioned within the environment;
- determining, using a noise classifier, that a class of a noise signal present in the audio data is unknown;
- training one or more machine learning models for the unknown class of the noise signal;
- after the training, processing at least one of the audio or video data to generate contextual data regarding the environment;
- analyzing the contextual data;
- based upon the analysis of the contextual data, selecting the one or more machine learning models;
- and
- applying the selected one or more machine learning models to optimize audio characteristics of the environment.
The computer-implemented method as defined in claim 33 wherein the remote location is a cloud platform.
A system, comprising:
- at least one of a microphone or camera located in an environment;
- and
- a processing device communicably coupled to the microphone and camera, the processing device having an audio optimization and control (“AOC”) operating system executable thereon to manage and control functionality of the microphone and camera, the processing device being configured to perform operations comprising:
- capturing at least one of audio or video data using the microphone or camera;
- determining, using a noise classifier, that a class of a noise signal present in the audio data is unknown;
- training one or more machine learning models for the unknown class of the noise signal;
- after the training, processing at least one of the audio or video data to generate contextual data regarding the environment;
- analyzing the contextual data;
- based upon the analysis of the contextual data, selecting the one or more machine learning models;
- and
- applying the selected one or more machine learning models to optimize audio characteristics of the environment.
A non-transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
- capturing at least one of audio or video data using a microphone or camera, respectively, positioned within an environment;
- determining, using a noise classifier, that a class of a noise signal present in the audio data is unknown;
- training one or more machine learning models for the unknown class of the noise signal;
- after the training, processing at least one of the audio or video data to generate contextual data regarding the environment;
- analyzing the contextual data;
- based upon the analysis of the contextual data, selecting the one or more machine learning models;
- and
- applying the selected one or more machine learning models to optimize audio characteristics of the environment.
Dependencies are inferred from the claim text.