Auditory Event Boundary Detection via Subsampling and Adaptive Filtering
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Solution Overview
Problem
Current methods for detecting auditory event boundaries in digital audio signals require high processing complexity and memory, making them inefficient for real-time applications, and struggle to accurately identify changes in spectral balance and noise conditions.
Innovation Solution
The approach involves subsampling the digital audio signal to cause aliasing, allowing for reduced bandwidth processing without anti-aliasing filters, and using adaptive filters to detect changes in frequency content, which reduces memory and processing requirements while maintaining sensitivity to auditory events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to detect auditory event boundaries, then detection accuracy is maintained, but processing complexity and memory requirements increase significantly
Solution Approach 1:
The patent extracts and processes only the most essential spectral features (spectral centroid, spectral spread, spectral skewness) from the audio signal, discarding redundant information. This selective extraction maintains detection accuracy while significantly reducing processing complexity and memory requirements compared to analyzing the complete frequency spectrum.
Solution Approach 2:
The patent segments the spectral analysis into three distinct features (centroid, spread, skewness) that capture different aspects of spectral balance changes. This segmentation allows the system to monitor spectral changes efficiently through separate, simplified calculations rather than processing the entire spectrum continuously.
2Measurement precision
If traditional methods are used to detect auditory event boundaries, then spectral balance changes are detected, but memory requirements increase
Solution Approach 1:
The patent extracts only the essential spectral characteristics (centroid, spread, skewness) that are sufficient for detecting auditory event boundaries, eliminating the need to store and process complete spectral data. This extraction approach maintains spectral balance detection capability while dramatically reducing memory consumption.
3Device complexity
If subsampling with aliasing is used, then processing complexity is reduced, but spectral component ordering changes
Solution Approach 1:
The patent changes the sampling rate parameter to create controlled aliasing that folds high-frequency spectral components into the baseband. This parameter change reduces processing complexity by working with lower sample rates while the subsequent calculation of spectral features (centroid, spread, skewness) adapts to the aliased spectrum, treating the folded components as part of the new spectral distribution.
Data Source
AI summary
An auditory event boundary detector employs down-sampling of the input digital audio signal without an anti-aliasing filter, resulting in a narrower bandwidth intermediate signal with aliasing. Spectral changes of that intermediate signal, indicating event boundaries, may be detected using an adaptive filter to track a linear predictive model of the samples of the intermediate signal. Changes in the magnitude or power of the filter error correspond to changes in the spectrum of the input audio signal. The adaptive filter converges at a rate consistent with the duration of auditory events, so filter error magnitude or power changes indicate event boundaries. The detector is much less complex than methods employing time-to-frequency transforms for the full bandwidth of the audio signal.