Audio Signal Noise Attenuation Using Segmented Codebooks
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Solution Overview
Problem
Existing single-microphone noise attenuation algorithms are computationally resource-intensive and require large noise codebooks, making them impractical for low-complexity devices and prone to suboptimal noise attenuation in diverse noise environments.
Innovation Solution
A noise attenuation apparatus that uses a smaller noise codebook with generic noise signal contribution candidates, segmenting audio signals into time segments, and generating estimated signal candidates through a weighted combination of desired and noise signal candidates to minimize a cost function, reducing computational resources and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large noise codebook is used to cover diverse noise environments, then noise attenuation performance is improved, but computational resource requirements and memory requirements increase significantly
Solution Approach 1:
The noise codebook is segmented into multiple subsets, each covering a specific noise type or environmental condition. Instead of using one large codebook, the system divides the noise space into manageable segments, reducing the number of candidates that need to be evaluated during runtime while maintaining comprehensive noise coverage across different scenarios.
Solution Approach 2:
Different subsets of the noise codebook are optimized for specific local noise conditions (e.g., different noise types, reverberant vs. non-reverberant environments). Each subset contains noise candidates tailored to particular acoustic scenarios, allowing the system to select the appropriate subset based on current environmental conditions, thereby improving both performance and efficiency.
2Reliability
If a large noise codebook is used to cover diverse noise environments, then noise attenuation performance is improved, but memory requirements increase
Solution Approach 1:
The noise codebook is segmented into multiple subsets, each covering a specific noise type or environmental condition. Instead of using one large codebook, the system divides the noise space into manageable segments, reducing the number of candidates that need to be evaluated during runtime while maintaining comprehensive noise coverage across different scenarios.
Solution Approach 2:
The system loads only the relevant noise codebook subset into memory based on the current acoustic environment or processing stage, rather than keeping the entire large codebook in memory simultaneously. This allows the system to manage memory resources efficiently by discarding unused subsets and loading only those needed for the current operation.
3Measurement precision
If codebook-based algorithms search over all possible combinations of speech and noise codebook entries, then accurate signal estimation is achieved, but computational complexity increases
Solution Approach 1:
The noise codebook is segmented into multiple subsets, each covering a specific noise type or environmental condition. Instead of using one large codebook, the system divides the noise space into manageable segments, reducing the number of candidates that need to be evaluated during runtime while maintaining comprehensive noise coverage across different scenarios.
Solution Approach 2:
Instead of exhaustively searching all possible combinations of speech and noise codebook entries, the system performs a partial search by evaluating only the most promising candidates or using heuristic methods to identify likely matches. This approach achieves sufficient signal estimation accuracy without the computational burden of complete enumeration.
4Ease of manufacture
If a single-microphone approach is used for noise reduction, then device cost is reduced, but noise suppression capability deteriorates under non-stationary conditions
Solution Approach 1:
The system dynamically adapts parameters such as noise codebook subset selection, weighting factors, and processing characteristics based on the observed acoustic environment and noise conditions. This allows a single-microphone system to effectively handle non-stationary noise by adjusting its behavior to match current conditions, compensating for the lack of multiple microphones.
Solution Approach 2:
The noise reduction algorithm incorporates dynamic adaptation mechanisms that allow it to respond to changing noise conditions in real-time. The system can switch between different noise models, adjust processing parameters, and adapt to non-stationary environments, enabling a single-microphone device to achieve robust noise suppression performance despite hardware limitations.
Data Source
AI summary
A noise attenuation apparatus receives an audio signal comprising a desired and a noise signal component. Two codebooks (109, 111) comprise respectively desired signal candidates representing a possible desired signal component and noise signal contribution candidates representing possible noise contributions. A segmenter (103) segments the audio signal into time segments and for each time segment a noise attenuator (105) generates estimated signal candidates by for each of the desired signal candidates generating an estimated signal candidate as a combination of a scaled version of the desired signal candidate and a weighted combination of the noise signal contribution candidates. The noise attenuator (105) minimizes a cost function indicative of a difference between the estimated signal candidate and the audio signal in the time segment. A signal candidate is then determined for the time segment from the estimated signal candidates and the audio signal is noise compensated based on this signal candidate.