Harmonic Source Enhancement Using Time-Frequency Masking
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Solution Overview
Problem
Current audio processing technologies face challenges in effectively separating and enhancing harmonic sources from audio signals, which is crucial for audio identification, authentication, and improving audio experiences, as they often struggle with background noise and inefficiencies in identifying media content.
Innovation Solution
The method involves an audio analyzer that processes media signals to extract and enhance harmonic sources using techniques like magnitude spectrogram analysis, Fourier transforms, and time-frequency masking, allowing for the separation of harmonic and percussive components, and a database-driven system for classification and identification of media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio signals are processed to separate harmonic and percussive components, then audio identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The audio signal is segmented into harmonic and percussive components using harmonic source enhancement techniques. The system separates the audio signal into these distinct components, allowing independent processing and identification of harmonic sources, which improves identification accuracy while managing complexity through structured decomposition
Solution Approach 2:
The harmonic source enhancement process extracts and isolates the harmonic components from the mixed audio signal. By taking out the harmonic sources and separating them from percussive elements and background noise, the system enables more accurate audio identification without the masking effects of other signal components
2Measurement precision
If harmonic sources are enhanced to improve identification, then audio fingerprinting capability is improved, but computational resources required increase
Solution Approach 1:
The harmonic source enhancement applies local processing quality differentially across the audio spectrum. By focusing computational resources on identifying and enhancing harmonic components at specific frequencies and time windows, rather than processing the entire audio signal uniformly, the system improves fingerprinting capability while optimizing computational resource usage
Solution Approach 2:
The system performs preliminary harmonic source enhancement and separation before conducting audio fingerprinting analysis. This preliminary action prepares the audio signal by isolating the most informative harmonic components, making subsequent fingerprinting more efficient and accurate while reducing the computational burden on later processing stages
3Reliability
If audio signals are separated into components to reduce noise, then audio quality is improved, but processing time increases
Solution Approach 1:
The harmonic source enhancement employs periodic processing actions organized in structured time-frequency analysis windows. By processing audio in periodic frames and using rhythmic processing patterns aligned with musical structures, the system achieves effective noise reduction and quality improvement while optimizing processing time through efficient temporal organization
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.