Audio Clipping Detection Using Out-of-Band Spectral Magnitudes
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Solution Overview
Problem
Clipping events in audio signals, which occur when the amplitude exceeds the dynamic range of an audio recording device, degrade speech recognition performance by unintentionally appearing in high-frequency areas, and existing detection methods are inadequate as they rely on checking maximum or minimum values, which are not always the case.
Innovation Solution
A computer-implemented method that digitalizes audio signals at a sampling frequency greater than twice the maximum frequency component and detects clipping events based on the magnitudes of the spectrum in a bandwidth greater than or equal to the limited frequency band, using equations to calculate the sum or average of spectral magnitudes to determine if a frame is clipped.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If clipping events are detected by checking whether each sample reaches maximum or minimum value, then the detection process is simple, but the detection accuracy is insufficient because clipping events do not necessarily occur at maximum or minimum values
Solution Approach 1:
The patent changes the detection parameter from time-domain amplitude values to frequency-domain spectral magnitudes. By transforming the detection approach to analyze spectral content above the limited frequency band, the system achieves more accurate clipping detection without relying on maximum/minimum value thresholds, thereby resolving the contradiction between simple detection and accurate detection
Solution Approach 2:
The patent replaces the simple threshold-checking mechanism with a spectral analysis mechanism. Instead of mechanically comparing sample values against fixed thresholds, the system uses frequency domain transformation and spectral magnitude analysis, substituting a more sophisticated detection mechanism that achieves higher accuracy
2Use of energy by moving object
If clipping events are detected using maximum or minimum value checking, then the computational load is low, but speech recognition performance degrades due to false detection in high-frequency areas
Solution Approach 1:
The patent changes the detection parameter from time-domain amplitude to frequency-domain spectral magnitude. By analyzing spectral content specifically in the frequency range above the limited band, the system accurately identifies true clipping events while avoiding false detections, thereby protecting speech recognition performance without excessive computational overhead
Solution Approach 2:
The patent applies local quality analysis by focusing spectral analysis specifically on the frequency region above the limited frequency band. Instead of analyzing the entire spectrum or using global threshold checks, the system concentrates computational resources on the specific frequency region where clipping artifacts manifest, achieving reliable detection with targeted computational effort
Data Source
AI summary
A method, a system, and a computer program product detect a clipping event in audio signals. The method includes digitalizing audio signals having limited frequency bands, at a sampling frequency which is greater than two times as large as the maximum frequency component of the audio signal; and detecting a clipping event of the audio signals, based on magnitudes of spectrum in a bandwidth which is greater than or equal to the limited frequency band. The sampling frequency may be greater than or equal to three times as large as the maximum frequency component of the audio signal. The detection of a clipping event may include determining, for each frame, whether or not a sum or average of the magnitudes of spectrum at the bandwidth which is greater than or equal to the limited frequency band is larger than a predetermined threshold.