Highlight Section Extraction from Audio Files
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Solution Overview
Problem
Existing music retrieval systems fail to efficiently extract highlight sections that accurately represent music, leading to slow search speeds and low accuracy, as they cannot convey the emotional essence of music and require professional skills or significant time to find preferred music.
Innovation Solution
An apparatus and method that analyze audio files by dividing them into frames, calculating average energy signals, and selecting highlight sections based on low-frequency signals, with secondary and tertiary candidate group selection to determine the most representative frames, allowing for rapid identification of preferred music.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If highlight sections are extracted using existing methods, then music search convenience is improved, but extraction speed is slow and accuracy is low
Solution Approach 1:
The audio signal is divided into multiple short-time frames, and each frame is analyzed independently to calculate energy characteristics. This segmentation allows parallel processing and reduces computational complexity, enabling faster extraction speed while maintaining accuracy in identifying highlight sections.
2Ease of operation
If highlight sections are extracted using existing methods, then music search convenience is improved, but extraction accuracy is low
Solution Approach 1:
Different weightings are assigned to different frequency bands when calculating energy characteristics. High-frequency components are emphasized for identifying rhythmic elements, while low-frequency components are weighted for identifying melodic and harmonic features. This local quality differentiation enables accurate identification of highlight sections that represent the emotional essence of music.
Solution Approach 2:
The system dynamically adjusts energy calculation parameters based on the musical context and detected features. By changing the weighting factors and threshold values adaptively, the system achieves high extraction accuracy while maintaining fast processing speed, resolving the contradiction between convenience and precision.
3Loss of information
If meta information is provided about music, then music information access is improved, but emotional feeling and climax cannot be conveyed
Solution Approach 1:
The system extracts and isolates the most representative highlight sections from the complete audio file, capturing the emotional essence and climax moments. These extracted segments are then presented to users, providing both rapid access to music information and authentic emotional experience, simultaneously addressing both requirements.
Data Source
AI summary
Disclosed are an apparatus and a method for extracting a highlight section of music. The apparatus for extracting a highlight section of music in accordance with the embodiment of the present invention includes a frame divider that divides an audio file into a plurality of frames having a predetermined sample length; an average energy signal calculator that calculates a signal representing the average magnitude of audio energy for a plurality of samples belonging to each frame for each frame of the plurality of frames; and a highlight section selector that extracts a low frequency signal from the signal representing the average audio energy magnitude for each frame and determines the highlight section from frame sections including maximum points of the low frequency signal.