Chop Detection in Audio Signals via Power Gradient Analysis
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Solution Overview
Problem
Existing technologies fail to effectively detect and quantify choppy speech, a type of audio degradation characterized by periodic gaps or delays in speech signals, which is particularly problematic in VoIP systems due to factors like CPU overload, low bandwidth, and codec mismatches.
Innovation Solution
A method and system for detecting chop in audio signals using a time-frequency representation, such as a short-term Fourier Transform (STFT) spectrogram, to calculate the gradient of mean power per frame, determine overlap offsets between positive and negative gradient signals, and estimate the amount of chop based on a comparison to a threshold, while being insensitive to other types of distortion and applicable to narrowband, wideband, and superwideband speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing non-reference quality metrics are used to predict general degradation scores, then a general quality prediction is obtained, but the model cannot specifically detect choppy speech and is sensitive to other types of distortion
Solution Approach 1:
The patent extracts and isolates the specific characteristics of choppy speech from general audio degradation. By focusing solely on the gradient patterns unique to chopping (periodic gaps and delays) rather than attempting to capture all types of distortion, the model achieves high specificity for chop detection while keeping the overall system relatively simple.
Solution Approach 2:
The patent segments the audio signal into individual frames and analyzes the gradient of mean power per frame separately. This segmentation allows the model to detect local chop characteristics without being overwhelmed by the complexity of analyzing the entire speech signal at once, improving detection precision while managing computational complexity.
2Reliability
If comprehensive quality metrics that can detect all types of distortion are used, then general speech quality assessment is improved, but computational complexity increases and sensitivity to non-chop distortions occurs
Solution Approach 1:
The patent extracts only the relevant gradient information needed for chop detection, filtering out unnecessary analysis of other distortion types. This selective extraction maintains high reliability for chop detection while significantly reducing computational complexity compared to comprehensive quality metrics that analyze all possible distortion types.
Solution Approach 2:
Instead of attempting to detect all types of distortion and then filtering for chop, the patent inverts the approach by directly analyzing only the gradient patterns that are characteristic of choppy speech. This inversion of the detection strategy achieves high accuracy for the target distortion while avoiding the computational burden of analyzing unrelated distortion types.
3Measurement precision
If a model sensitive to all types of distortion is used, then comprehensive quality assessment is achieved, but the model cannot reliably distinguish chop from natural speech features
Solution Approach 1:
The patent applies local quality analysis by examining the gradient of mean power at specific locations within the audio signal frames. By focusing on local gradient patterns rather than global signal characteristics, the model can identify the specific signature of choppy speech (periodic gaps and delays) while being less sensitive to natural variations in speech that occur at different locations.
Solution Approach 2:
The patent extracts and analyzes only the gradient information that is characteristic of choppy speech, separating this specific feature from other speech characteristics. This extraction allows the model to detect chop with high specificity while minimizing false detections caused by sensitivity to natural speech features or other types of distortion.
Data Source
AI summary
Methods and systems are provided for detecting chop in an audio signal. A time-frequency representation, such as a spectrogram, is created for an audio signal and used to calculate a gradient of mean power per frame of the audio signal. Positive and negative gradients are defined for the signal based on the gradient of mean power, and a maximum overlap offset between the positive and negative gradients is determined by calculating a value that maximizes the cross-correlation of the positive and negative gradients. The negative gradient values may be combined (e.g., summed) with the overlap offset, and the combined values then compared with a threshold to estimate the amount of chop present in the audio signal. The chop detection model provided is low-complexity and is applicable to narrowband, wideband, and superwideband speech.