Audio Similarity Calculation Using Pitch Sequence Eigenvectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for calculating the similarity of audio files are either manual and inefficient or rely on attribute-based calculations that fail to consider audio content, resulting in lower accuracy.
Innovation Solution
A method and device that constitute pitch sequences for audio files, calculate eigenvectors based on these sequences, and determine similarity between audio files using these eigenvectors, thereby abstractly representing audio content for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation method is used, then accuracy of similarity determination is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated computer-based system that extracts audio features (pitch, energy, zero-crossing rate) and calculates similarity using mathematical algorithms. This substitution maintains accuracy through systematic feature comparison while dramatically improving productivity by eliminating manual labor.
2Productivity
If equipment calculation method based on attributes is used, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent extracts essential audio content features (pitch sequence, energy, zero-crossing rate) from the audio files, separating the meaningful content characteristics from irrelevant attributes like genre, album, and author. This extraction focuses the similarity calculation on actual audio content, improving measurement precision while maintaining computational efficiency.
Solution Approach 2:
The patent transforms audio content into quantifiable parameters (pitch values, energy levels, zero-crossing rates) that can be systematically compared. By changing the representation from qualitative attributes to quantitative acoustic parameters, the system achieves both high productivity through automated computation and high precision through content-based comparison.
3Device complexity
If attribute-based calculation is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the audio file into discrete analytical components: pitch sequence, energy distribution, and zero-crossing rate. By dividing the audio content analysis into these distinct feature extraction stages, the system manages complexity through modular processing while achieving precise similarity measurement through comprehensive feature comparison.
Data Source
AI summary
A method for calculating a similarity of audio files includes constituting a pitch sequence of a first audio file and a pitch sequence of a second audio file; calculating an eigenvector of the first audio file according to the pitch sequence of the first audio file, and calculating an eigenvector of the second audio file according to the pitch sequence of the second audio file; calculating a similarity between the first audio file and the second audio file according to the eigenvector of the first audio file and the eigenvector of the second audio file.