Audio Source Identification via Frequency Spectrum Imaging
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Solution Overview
Problem
Current methods for identifying the source of audio signals are inefficient and inaccurate due to the lack of distinct differences in intrinsic background noise among various audio recording devices and the time-consuming nature of existing algorithms, such as wavelet technology.
Innovation Solution
A computer-implemented method that generates an image of the time-frequency spectrum of low and high frequency components of an audio signal using Fourier transformation, allowing for pattern recognition to identify the audio source based on distinctive frequency response characteristics in these frequency ranges, reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wavelet technology is used for audio source identification, then measurement precision is improved, but productivity deteriorates due to time-consuming analysis
Solution Approach 1:
The patent extracts and analyzes only specific frequency components (low frequency and high frequency) from the audio signal instead of performing complete wavelet analysis across all frequencies. This selective extraction maintains identification accuracy while significantly reducing computational time and improving processing productivity.
Solution Approach 2:
The audio frequency spectrum is segmented into specific ranges (low frequency and high frequency components) for separate analysis. By dividing the frequency spectrum and focusing only on relevant segments, the patent achieves accurate source identification without the computational burden of analyzing the entire frequency range using wavelet technology.
2Measurement precision
If complete frequency spectrum analysis is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary low frequency and high frequency components from the complete frequency spectrum, eliminating the need for complex analysis of mid-range frequencies. This extraction approach maintains sufficient identification precision while dramatically simplifying the computational device requirements.
Solution Approach 2:
Instead of performing excessive complete spectrum analysis, the patent applies partial action by analyzing only specific frequency components that are sufficient for accurate source identification. This partial analysis reduces computational complexity while maintaining measurement precision.
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 approach significantly enhances the efficiency and accuracy of audio source identification by focusing on the distinctive frequency response characteristics of low and high frequency components, eliminating the need to analyze background noise and reducing computational burden.
Implementation Method 1
generating, by the device, an image of time-frequency spectrum of low frequency component and high frequency component of the audio signal
Data Source
AI summary
Embodiments facilitating audio source identification are provided. A computer-implemented method comprises: receiving, by a device operatively coupled to one or more processors, an audio signal under inspection; generating, by the device, an image of time-frequency spectrum of low frequency component and high frequency component of the audio signal; and identifying, by the device, a source of the audio signal based on the generated image and one or more patterns of time-frequency spectrum, wherein each of the one or more patterns is corresponding to low frequency feature and high frequency feature of a specific audio source.