Acoustic Context Recognition Using Local Binary Patterns
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Solution Overview
Problem
Existing audio processing technologies in computer electronics struggle to effectively recognize and contextualize audio scenes in real-time, especially in environments with multiple co-occurring contexts, which limits personalization and increases processing requirements, thereby affecting battery life and performance in mobile devices.
Innovation Solution
The use of local binary patterns (LBP) and audio spectrograms, combined with a codebook and machine learning models, to identify and classify audio patterns indicative of environmental contexts, allowing for real-time adaptation and reduced processing needs by clustering codebook histograms and utilizing a support vector machine for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio processing methods are used to recognize audio scenes, then the system can process audio data, but the recognition accuracy is insufficient especially in environments with multiple co-occurring contexts
Solution Approach 1:
The audio signal is segmented into multiple frequency sub-bands, and the spectrogram is divided into multiple blocks. Local binary patterns are constructed for each block independently, allowing parallel processing while capturing local temporal-frequency characteristics. This segmentation enables accurate recognition of multiple co-occurring contexts by analyzing different frequency regions separately.
Solution Approach 2:
The patent transforms the audio recognition problem from the time domain to the time-frequency domain by generating spectrograms. Local binary patterns are constructed by comparing pixel values in the spectrogram, adding a spatial dimension to the analysis. This dimensional transformation enables simultaneous capture of temporal evolution and spectral characteristics, improving recognition accuracy for complex audio scenes.
2Measurement precision
If complex audio processing algorithms are used to improve recognition accuracy, then the audio scene can be identified more accurately, but the processing requirements and power consumption increase
Solution Approach 1:
The patent extracts only the essential features from the audio signal by constructing local binary patterns from spectrogram blocks. Instead of processing the entire audio signal with complex algorithms, it extracts discriminative local patterns and represents them using histograms. This feature extraction approach maintains high recognition accuracy while significantly reducing computational complexity and power consumption for mobile devices.
Solution Approach 2:
The patent transforms the audio signal parameters by converting time-domain signals into frequency-domain spectrograms, then into local binary pattern histograms. This parameter transformation simplifies the data representation while preserving critical information for context identification, enabling accurate recognition with reduced processing requirements.
3Adaptability or versatility
If the device processes audio data in real-time to provide personalized context-aware information, then user personalization is improved, but the processing power requirements increase affecting overall device performance
Solution Approach 1:
The patent performs preliminary processing by pre-computing local binary patterns from spectrogram blocks and generating histograms before final classification. The codebook is pre-built with representative patterns, enabling efficient real-time matching. This preliminary action reduces the computational burden during real-time operation, allowing context-aware personalization without excessive processing requirements.
Data Source
AI summary
Various exemplary aspects are directed to acoustic context recognition apparatuses and methods involving isolating and identifying context(s) of an acoustic environment. In one exemplary embodiment, source audio is converted into audio spectrograms, each spectrogram indicative of a period of time. The series of spectrograms are analyzed to identify audio patterns, over a period of time, which are indicative of an environmental context of the source audio. In many embodiments of the present disclosure, acoustic context recognition also includes comparing the identified audio patterns to known environmental contexts.