Sound Quality Identification via Spectral Analysis
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Solution Overview
Problem
Existing sound file processing technologies cannot accurately identify the sound quality of files, particularly distinguishing between true lossless and false lossless sound files in various audio formats, due to irreversible compression distortions.
Innovation Solution
A method involving converting sound files into a preset reference audio format, performing framing and Fourier transformation, followed by model matching and energy change point determination to classify sound files as lossless or lossy based on their spectral characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sound files are compressed into various audio formats for convenient distribution, then storage efficiency and transmission speed are improved, but sound quality information is lost due to irreversible compression distortions
Solution Approach 1:
The patent performs preliminary action by converting the sound file to a reference audio format and conducting spectral analysis before making the quality determination. This advance processing extracts spectral features and identifies energy change points, allowing the system to detect compression distortions before final quality assessment, thereby resolving the contradiction between efficient distribution and quality preservation.
2Speed
If existing sound processing technologies are used, then file processing speed is maintained, but accurate identification of sound quality cannot be achieved
Solution Approach 1:
The patent applies segmentation by dividing the sound file into multiple frames and performing Fourier transformation on each frame separately. This segmentation allows the system to analyze spectral characteristics at different time points, identify energy change points, and detect compression distortions more accurately while maintaining processing efficiency through parallel frame analysis.
Solution Approach 2:
The patent transitions from time-domain analysis to frequency-domain analysis by performing Fourier transformation. This dimensionality change enables the system to observe spectral characteristics and energy distribution patterns that are not visible in the time domain, significantly improving sound quality identification accuracy while maintaining computational feasibility.
3Measurement precision
If format conversion and spectral analysis are performed on sound files, then sound quality identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent reduces processing complexity by performing preliminary actions: converting the sound file to a reference audio format first, then systematically extracting spectral features through Fourier transformation. This structured preliminary processing organizes the data in a way that simplifies subsequent quality determination, making the overall complex process more manageable and efficient.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enables precise identification of true lossless sound files from lossless audio formats, achieving high accuracy in determining sound quality, with tests showing an identification accuracy of up to 99.07%, and allows users to quickly assess sound quality without listening to the files.
Implementation Method 1
performing Fourier transformation processing on the to-be-identified sound file in the reference audio format, to obtain a spectrum of each frame of the to-be-identified sound file
Data Source
AI summary
A sound file sound quality identification method is provided. The method includes converting a format of a to-be-identified sound file into a preset reference audio format; performing framing on the sound file to obtain a plurality of frames; and performing Fourier transformation processing on the to-be-identified sound file to obtain a spectrum of each frame. The method also includes performing model matching according to the spectrum of each frame of the to-be-identified sound file to obtain a preliminary classification result of the to-be-identified sound file; determining an energy change point of the to-be-identified sound file according to the spectrum of each frame; and determining a sound quality of the to-be-identified sound file according to the preliminary classification result of the to-be-identified sound file and the energy change point of the to-be-identified sound file.