Audio Clipping Detection via Histogram Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital audio processing methods fail to effectively detect and differentiate between hard and soft clipping, particularly in scenarios where clipping occurs due to non-linear compression or subsequent processing, leading to degraded audio quality.
Innovation Solution
A method using histograms with sample intervals to detect audio clipping by calculating local maxima in the histogram's tails, comparing these with other histogram characteristics, and determining the presence and frequency of clipping based on predetermined threshold values, allowing for the determination of both hard and soft clipping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple detection algorithms are used for constant sequences of maximum and minimum sample values, then hard clipping detection is achieved, but soft clipping cannot be detected
Solution Approach 1:
The patent changes the detection parameter from simple sample value comparison to histogram-based distribution analysis. By examining the statistical distribution of audio samples and identifying abnormal peaks in the histogram that deviate from the expected sinusoidal pattern, the system can detect both hard and soft clipping. This parameter transformation enables versatile detection of different clipping types while maintaining algorithmic efficiency.
2Measurement precision
If advanced methods based on maximum and minimum value detection are used, then multiple clipping levels can be detected, but subsequent processing like filtering disperses clipped samples making detection difficult
Solution Approach 1:
The patent introduces a histogram as an intermediary representation between the raw audio signal and the detection logic. The histogram aggregates sample distribution information, creating a stable intermediate structure that reveals clipping characteristics even when individual samples are dispersed by subsequent processing. This intermediary transformation makes the detection robust against filtering and other signal processing operations.
3Reliability
If clipping detection is performed to ensure audio quality, then audio quality degradation is avoided, but detection of low percentages of clipping (0.01%) is challenging
Solution Approach 1:
The patent transitions from one-dimensional sample value analysis to two-dimensional histogram space analysis. By mapping sample values to histogram bins and examining the distribution pattern across multiple bins, the system amplifies the signal characteristics of clipped portions. This dimensional transformation enables the detection of very low percentage clipping (0.01%) that would be imperceptible in the time domain.
Data Source
AI summary
Methods and systems for detecting the presence and frequency of clipping in an audio signal are provided. A clipping detection algorithm detects the presence of hard and soft clipping using histograms with intervals of samples, rather than attempting to identify the clipping value. Therefore, it is not essential to the algorithm that there be a large number of bins. Furthermore, the bins may be non-uniformly distributed since the number of samples belonging to lower amplitudes is of little importance. The detection algorithm is also configured to determine the severity and/or perceptual effect of any clipping found to be present in the signal by calculating the ratio of clipped samples to non-clipped samples. Temporal information on the occurrence of clipping in the signal is also used to evaluate perceptual effect.