Audio Analytics Model for Abnormal Sound Detection
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Solution Overview
Problem
Current video surveillance systems relying on audio analytics struggle with accurately identifying abnormal events due to the need for multiple, environment-specific audio analytics modules and interference from background noises, which limits sound localization and reliability.
Innovation Solution
A method that uses a baseline normal audio stream to process incoming audio streams, identifies abnormal events, and determines their location, utilizing an audio classification model trained with reinforcement and transfer learning to differentiate between normal and abnormal sounds, and updates the model based on operator feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate audio analytics modules are used to detect different audio events, then the detection capability for various abnormal events is improved, but the system complexity and number of modules required increases significantly
Solution Approach 1:
The patent implements a single audio analytics module that can detect multiple types of abnormal audio events including gunshots, screams, glass breaking, and other anomalies. This universal module replaces the need for multiple separate specialized modules, achieving multi-functionality while reducing system complexity and the number of components required.
Solution Approach 2:
The patent combines multiple detection functions into one integrated audio analytics module that processes audio streams and identifies various abnormal events simultaneously. By merging the functionality of multiple separate modules into a single unified system, the patent reduces complexity while maintaining comprehensive detection capabilities across different event types.
2Loss of time
If off-the-shelf audio analytics modules are used, then deployment speed is improved, but reliability decreases due to background noise interference from the particular environment
Solution Approach 1:
The patent implements a training mode that runs before operational deployment, during which the audio analytics module learns and adapts to the specific background acoustic characteristics of the target environment. This preliminary adaptation phase allows the system to acclimate to environmental noises such as traffic, construction, or ambient sounds, thereby improving detection reliability when deployed without requiring environment-specific module customization.
Solution Approach 2:
The audio analytics module automatically adapts to its deployment environment through self-learning during the training mode, without requiring manual configuration or environment-specific customization. The system serves itself by autonomously learning the acoustic profile of its operating environment and adjusting its detection parameters accordingly, enabling reliable performance in diverse settings while maintaining rapid deployment capability.
3Measurement precision
If audio analytics modules are trained for specific environments, then detection accuracy is improved, but adaptability to different environments decreases
Solution Approach 1:
The patent implements a dynamic audio analytics module that can adapt its detection parameters and behavior based on the specific environment it operates in. Through the training mode, the module dynamically adjusts to local acoustic conditions while maintaining the flexibility to be deployed in various different environments. This dynamic adaptability allows the system to achieve high detection accuracy in each specific environment without sacrificing versatility across multiple deployment scenarios.
Data Source
AI summary
Methods and systems for identifying abnormal sounds in a particular environment. A normal audio stream obtained in the absence of abnormal sounds may be used as a baseline for subsequently processing an incoming audio stream with a processor to determine whether the incoming audio stream from the microphone in the particular environment includes an abnormal audio event for the particular environment. When it is determined that the incoming audio stream includes an abnormal audio event for the particular environment an electronic database may be accessed to determine a location of the abnormal audio event in the particular environment. A video camera with a field of view that includes the location of the abnormal audio event in the particular environment may be identified and the video stream from the identified video camera retrieved and displayed.