Audio Signal Isolation Using Dynamic Frequency Selection
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Solution Overview
Problem
Existing audio signal processing methods using microphone arrays with two microphones face challenges in improving voice quality due to sensitivity to microphone location errors and increased costs with more microphones, particularly with blind source separation technology.
Innovation Solution
A method that determines weighting coefficients based on both static and dynamic frequencies within a frequency band, using harmonic subsets and condition numbers of separation matrices to enhance signal isolation accuracy and reduce voice impairment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If blind source separation technology is used with two microphones to enhance voice, then product cost is reduced, but voice quality and signal separation accuracy deteriorate
Solution Approach 1:
The patent changes the parameter of frequency selection from static to dynamic by introducing a frequency selection mechanism that adaptively selects frequencies based on separation matrix condition numbers. This allows the system to dynamically adjust which frequencies are processed, improving separation accuracy without requiring more microphones or complex hardware
Solution Approach 2:
The patent introduces dynamic frequency selection where the set of frequencies to be processed is not fixed but adapts based on the condition number of the separation matrix at each time frame. This dynamic approach allows the system to focus computational resources on frequencies that need separation most, improving overall performance with limited microphones
2Reliability
If all frequencies in a frequency band are used for weighting coefficient determination, then signal processing completeness is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the necessary frequencies from the full frequency band for processing. By using frequency selection based on condition numbers, it takes out only those frequencies that require separation processing, excluding frequencies that don't need processing. This reduces computational load while maintaining processing completeness for relevant signals
Solution Approach 2:
The patent applies partial action by processing only a subset of frequencies rather than all frequencies in the band. The frequency selection mechanism determines which frequencies need processing based on separation difficulty, applying computational resources partially to only those frequencies that benefit from separation, thereby reducing overall processing time
Data Source
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AI summary
An original noisy signal of each of at least two microphones is acquired (S101) by acquiring, using the at least two microphones, an audio signal emitted by each sound source. For each frame in time domain, a frequency-domain estimated signal of each sound source is acquired (S102) according to the original noisy signal of each microphone. A frequency collection containing a plurality of predetermined static frequencies and dynamic frequencies is determined (SI03) in a predetermined frequency band range. A weighting coefficient of each frequency contained in the frequency collection is determined (S104) according to the frequency-domain estimated signal of the each frequency in the frequency collection. A separation matrix of the each frequency is determined (S105) according to the weighting coefficient. The audio signal emitted by each of the at least two sound sources is acquired (S106) based on the separation matrix and the original noisy signal.