Audio File Paragraph Division Using Relevance Characteristic Sequences
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Solution Overview
Problem
Current audio processing methods for splitting audio files, such as in karaoke and music listening systems, are inefficient and lack intelligence, requiring manual processing and failing to meet user demands for automated sectioning of audio files.
Innovation Solution
A method that constructs relevance characteristic sequences based on similarity, time intervals, or peak values within audio files to determine optimal section breaking points, allowing for automated division of audio files into sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing is used to split audio files, then the processing can be completed, but the efficiency is low and cannot meet user demands
Solution Approach 1:
The audio processing system performs automated sectioning by itself without human intervention. The computer executes algorithms to automatically identify section breaking points, extract features, and divide audio files into sections based on musical structure analysis, eliminating the need for manual processing
Solution Approach 2:
The patent replaces manual mechanical processing with computer-based automated processing. Instead of human operators manually identifying and marking section breaks, the system uses computational algorithms to analyze audio features, detect structural patterns, and automatically determine section dividing points
2Extent of automation
If manual processing is used for audio sectioning, then the task can be completed, but the intelligence of audio processing is low
Solution Approach 1:
The patent segments the audio processing task into distinct computational stages: feature extraction, section breaking point detection, and audio file division. By breaking down the complex automated processing into manageable modules, the system achieves high automation while keeping each component relatively simple and well-defined
Solution Approach 2:
The system analyzes multiple audio parameters including amplitude, frequency, and temporal features to identify section breaking points. By monitoring changes in these parameters and their relationships, the automated system can intelligently detect structural boundaries in the audio without requiring complex manual judgment
Data Source
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AI summary
An audio processing method and apparatus, and a terminal. The method comprises: acquiring file data of a target audio file; constructing a correlation characteristic sequence according to correlation characteristic data among component elements of the file data; optimizing the correlation characteristic sequence according to a preset total quantity of paragraphs; determining the paragraph change time according to a value of at least one characteristic element in the optimized correlation characteristic sequence; and dividing, according to the paragraph change time, the target audio file into paragraphs the paragraph quantity of which is the preset total quantity of paragraphs. In the method, a target audio file is divided into paragraphs according to the similarity between and time interval characteristics of character simple sentences in a subtitle file corresponding to an audio file as well as correlation characteristics of audio frames between audio paragraphs, thereby improving the efficiency of the paragraph division processing and the intelligence of audio processing.