Audio Signal Processing Framework for Enclosure Inference
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Solution Overview
Problem
Existing audio signal processing systems face challenges in accurately and efficiently inferring characteristics of physical enclosures and objects within them, leading to suboptimal denoising, echo cancellation, source classification, and source localization.
Innovation Solution
The implementation of a feature extraction framework and audio event framework applied to a plurality of audio signals to infer characteristics, using machine learning models and digital signal processing techniques for improved audio signal processing, including source localization and classification, and adjustment of capture device parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing audio signal processing systems are used to infer characteristics of physical enclosures, then the processing can be performed with current computational resources, but the accuracy and efficiency of denoising, echo cancellation, source classification, and source localization are suboptimal
Solution Approach 1:
The system segments the audio signal processing task into distinct functional modules: a feature extraction framework that processes audio signals to extract relevant characteristics, and an audio event framework that uses these features to infer enclosure properties. This segmentation allows each module to be optimized independently, improving both accuracy and processing efficiency.
Solution Approach 2:
The feature extraction framework performs preliminary processing of audio signals before they are used by the audio event framework. By pre-extracting relevant features such as acoustic signatures and spatial characteristics, the system reduces the computational burden on subsequent processing stages while maintaining high accuracy in enclosure characteristic inference.
2Measurement precision
If advanced machine learning models are applied to improve audio signal processing accuracy, then processing quality improves, but computational resource requirements increase
Solution Approach 1:
The system extracts and focuses only on the most relevant features from audio signals using the feature extraction framework. By taking out only the essential acoustic characteristics needed for enclosure inference, source classification, and localization, the system achieves high accuracy while minimizing computational resource consumption compared to processing entire raw audio streams.
Solution Approach 2:
The system transforms audio signals into different parameter spaces through feature extraction, converting raw audio data into compact representations such as spectral features, temporal features, and spatial features. This parameter transformation enables more efficient processing by machine learning models while maintaining or improving classification and localization accuracy.
3Measurement precision
If real-time audio signal processing is performed with high accuracy, then processing quality is maintained, but processing speed decreases
Solution Approach 1:
The feature extraction framework performs preliminary processing of audio signals in real-time, extracting essential features before they are passed to the audio event framework. This preliminary action ensures that only the most relevant information is processed further, maintaining high accuracy while enabling real-time processing speeds by reducing computational complexity at each stage.
4Adaptability or versatility
If resource-constrained environments are targeted for deployment, then accessibility improves, but processing capabilities are limited
Solution Approach 1:
The system extracts only the essential features needed for audio event detection and enclosure characteristic inference, discarding redundant information. This extraction approach significantly reduces computational requirements, enabling deployment in resource-constrained environments such as mobile devices and embedded systems while maintaining practical processing efficiency.
Solution Approach 2:
The feature extraction framework transforms audio signals into compact parameter representations that are more efficient to process on resource-constrained hardware. By changing from raw audio processing to feature-space processing, the system achieves comparable accuracy with significantly reduced computational demands, improving deployability across diverse platforms.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatus, systems, devices, and/or the like for inferring characteristics of a physical enclosure using a plurality of audio signals. The plurality of audio signals may be processed using a feature extraction framework to generate structured audio event data sets, which may be processed using an audio event framework to determine the characteristics of the physical enclosure.