Audio Discriminator Using Dual-Window Classification for Low Latency
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Solution Overview
Problem
Conventional speech/music discrimination algorithms introduce high algorithmic delay, making them unsuitable for real-time applications, as they rely on unreliable classification methods that fail to efficiently switch between speech and music encoding without significant latency.
Innovation Solution
A dual-classifier approach using both short-term and long-term analysis windows to classify audio segments, where short-term features provide instantaneous clues and long-term features offer delayed but more reliable decisions, combined using hysteresis or adaptive thresholding to minimize overall delay while maintaining classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech/music discrimination algorithms are used, then classification reliability is improved, but algorithmic delay increases significantly
Solution Approach 1:
The audio signal is divided into short-time segments (frames) that are processed independently and sequentially. Each frame is classified based on its own features, allowing for low-latency decision-making while maintaining reliability through consistent feature extraction and classification rules applied to each segment.
Solution Approach 2:
The patent uses computationally efficient feature extraction and classification methods that can be applied quickly to each short-time frame. By using simple yet effective features (such as zero-crossing rate, spectral centroid, and energy) and straightforward classification rules, the system achieves reliable classification with minimal processing delay, treating each frame as an independent, quickly-processable unit.
2Productivity
If short-term analysis window is used, then processing speed is improved, but classification reliability deteriorates
Solution Approach 1:
The classification system uses the results from short-term frame analysis as feedback to guide long-term classification decisions. The short-term features provide immediate classification candidates, which are then refined and validated using long-term analysis, creating a feedback loop that improves reliability without sacrificing processing speed.
Solution Approach 2:
The patent transitions from analyzing only short-term temporal features to incorporating both short-term and long-term analysis dimensions. By adding the time dimension (comparing results across multiple time scales), the system achieves more reliable classification while maintaining the processing speed benefits of short-term analysis through hierarchical decision-making.
Data Source
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AI summary
For classifying different segments of a signal which comprises segments of at least a first type and second type, e.g. audio and speech segments, the signal is short- term classified (150) on the basis of the at least one short-term feature extracted from the signal and a short- term classification result (152) is delivered. The signal is also long-term classified (154) on the basis of the at least one short-term feature and at least one long-term feature extracted from the signal and a long-term classification result (156) is delivered. The short-term classification result (152) and the long-term classification result (156) are combined (158) to provide an output signal (160) indicating whether a segment of the signal is of the first type or of the second type.