Homologous Audio Sound Quality Detection Model
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Solution Overview
Problem
The high cost and inefficiency in managing and storing homologous audio files due to uneven sound quality, leading to storage pressure and redundancy, as existing technologies lack effective methods for detecting and distinguishing sound quality.
Innovation Solution
A sound quality detection method and device that acquire audio features from homologous audio files, generate a correspondence list, and use a trained sound quality detection model to assign a sound quality score, allowing for the identification and management of audio files based on quality, thereby reducing storage and acquisition costs by deleting low-quality files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of homologous audio files are stored to ensure quality coverage, then sound quality options are improved, but storage costs and management complexity increase
Solution Approach 1:
The system performs preliminary sound quality detection and scoring on audio files during ingestion or periodic processing, assigning quality scores before files need to be managed or selected. This advance classification enables efficient retrieval and management without requiring complex real-time analysis, resolving the contradiction between having quality options and managing complexity.
2Adaptability or versatility
If all homologous audio files are retained to maintain quality diversity, then sound quality selection is improved, but storage costs increase
Solution Approach 1:
The system applies different retention strategies to different audio files based on their locally determined sound quality scores. High-quality files are retained while low-quality files are removed, creating a non-uniform but optimized storage portfolio that maintains quality diversity without storing all files equally, thus resolving the contradiction between quality selection and storage volume.
Solution Approach 2:
The system discards low-quality audio files identified through sound quality detection while preserving high-quality files. This selective discarding reduces storage requirements while maintaining the ability to provide quality diverse audio options, addressing the contradiction between storage volume and quality selection.
3Measurement precision
If sound quality detection is performed on all audio files, then quality assessment accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs sound quality detection on a representative subset of audio files or uses simplified detection methods that provide sufficient accuracy without analyzing every single file in exhaustive detail. This partial action approach maintains acceptable quality assessment accuracy while significantly reducing processing time and computational resource requirements.
4Measurement precision
If manual quality assessment is performed to ensure accurate sound quality evaluation, then quality detection precision is improved, but operational efficiency decreases
Solution Approach 1:
The system replaces manual quality assessment (mechanical human operation) with automated sound quality detection algorithms that analyze audio files programmatically. This substitution maintains or improves detection precision through consistent algorithmic evaluation while dramatically increasing operational efficiency by processing files automatically without human intervention, resolving the contradiction between precision and productivity.
Data Source
AI summary
Provided is a sound quality detection method, including: acquiring a plurality of audio files to be detected, wherein the plurality of audio files are homologous audio files; acquiring at least one audio feature of each of the plurality of audio files by performing feature extraction on the audio file, and generating a correspondence list between the at least one audio feature of each of the plurality of audio files and an audio file identifier; and determining, using a sound quality detection model, a sound quality score of each of the plurality of audio files based on the correspondence list between the at least one audio feature of each of the plurality of audio files and the audio file identifier, wherein the sound quality detection model is configured to detect sound quality of homologous audio files.