Audio Analyzer Harmonic Source Enhancement
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Solution Overview
Problem
Existing audio processing technologies struggle to effectively enhance harmonic sources in audio signals, which is crucial for applications like audio identification, authentication, and media classification.
Innovation Solution
The proposed solution involves an audio analyzer that processes media signals to enhance harmonic sources by decomposing the signal into harmonic and percussive components, using techniques such as magnitude spectrogram analysis and time-frequency masking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio signals are processed using conventional methods, then processing speed is maintained, but harmonic source enhancement accuracy is insufficient
Solution Approach 1:
The audio signal is segmented into harmonic and non-harmonic components using harmonic product spectrum analysis. By dividing the frequency spectrum into harmonic bins and non-harmonic bins, the system can process each component separately with optimized algorithms, improving accuracy while maintaining speed through specialized processing paths for each segment type.
Solution Approach 2:
A time-frequency mask is introduced as an intermediary to separate harmonic from non-harmonic content. This mask acts as a mediator that guides the enhancement process by selectively preserving harmonic components while attenuating non-harmonic ones, enabling accurate enhancement without requiring computationally intensive processing of the entire signal.
2Measurement precision
If audio signals are separated into harmonic and percussive components, then audio identification accuracy improves, but processing complexity increases
Solution Approach 1:
The system replaces complex mechanical signal processing with frequency-domain analysis using the harmonic product spectrum. By transforming the audio signal into the frequency domain and analyzing the product of spectrum components, the system achieves component separation through mathematical operations rather than complex time-domain processing, reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The system changes the parameter domain from time to frequency by computing the harmonic product spectrum. This parameter transformation simplifies the separation process by converting time-varying signal characteristics into frequency-domain representations that are easier to classify and process, reducing the complexity of harmonic versus non-harmonic separation.
3Measurement precision
If time-frequency masking is applied to enhance harmonic sources, then harmonic content is improved, but computational requirements increase
Solution Approach 1:
Instead of processing the entire audio signal uniformly, the system applies time-frequency masking only to the harmonic components identified through harmonic product spectrum analysis. By performing the computationally intensive masking operation only where needed (in harmonic regions rather than across the entire spectrum), the system reduces overall computational energy while maintaining high harmonic content quality.
Data Source
AI summary
Methods and apparatus for harmonic source enhancement are disclosed herein. An example apparatus includes an interface to receive a media signal. The example apparatus also includes a harmonic source enhancer to determine a magnitude spectrogram of audio corresponding to the media signal; generate a time-frequency mask based on the magnitude spectrogram; and apply the time-frequency mask to the magnitude spectrogram to enhance a harmonic source of the media signal.