Audio Signal Sound Quality Correction via Feature Parameter Segmentation
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Solution Overview
Problem
Current sound quality correction processes for audio signals struggle to distinguish between speech and music signals, leading to inadequate sound quality enhancement due to the difficulty in separating these signals, especially when they are mixed.
Innovation Solution
A digital television broadcasting receiving apparatus calculates feature parameters for an input audio signal to differentiate between speech and music signals, and between music and background sounds, applying adaptive sound quality correction processes based on these distinctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sound quality correction process is applied to an audio signal, then sound quality is improved, but the process cannot distinguish between speech and music signals when they are mixed, leading to inadequate enhancement
Solution Approach 1:
The patent segments the audio signal analysis into multiple feature parameters: zero-crossing rate for speech detection, spectral flatness for music detection, and power ratio for overall signal characterization. By dividing the analysis into these distinct features, the system can accurately identify speech and music components even when mixed, resolving the contradiction between reliable sound quality enhancement and precise signal type discrimination.
2Measurement precision
If feature parameters are calculated to distinguish speech and music signals, then signal identification accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary classification mechanism that uses predetermined thresholds for each feature parameter (zero-crossing rate, spectral flatness, power ratio). These thresholds act as mediators between the raw audio signal and the final classification decision, enabling accurate speech/music identification through simple threshold comparisons rather than complex algorithms, thus improving identification accuracy while controlling system complexity.
Data Source
AI summary
According to one embodiment, various feature parameters are calculated for distinguishing between a speech and music and between music and background sound for an input audio signal. With the feature parameters, score determination is made as to whether the input audio signal is close to a speech signal or a music signal. If the input audio signal is determined to be close to music, the preceding score determination result is corrected considering the influence of background sound. Based on the corrected score value, a sound quality correction process for a speech or music is applied to the input audio signal.