DNN Reverberation Modeling for Speech Signal Dereverberation
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Solution Overview
Problem
Existing methods for reverberation modeling and dereverberation in enclosed environments, such as those encountered in hands-free speech communication, face challenges in distinguishing direct-path signals from attenuated and delayed copies, especially in high reverberation conditions with non-stationary noises, leading to degraded speech quality that affects automatic speech recognition systems.
Innovation Solution
A method and system utilizing deep learning techniques, specifically convolutive prediction with deep neural networks (DNNs), to estimate and model the room impulse response (RIR) by leveraging spectral-temporal patterns and linear filter structures, allowing for the identification and removal of early reflections and late reverberation, thereby improving dereverberation and speaker separation tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dereverberation methods are used to remove reverberation effects, then speech quality may be improved in simple environments, but the ability to distinguish direct-path signals from attenuated and delayed copies deteriorates in high reverberation conditions with non-stationary noises
Solution Approach 1:
The patent segments the reverberant speech signal into direct-path signal and reverberation components by modeling the RIR as a sum of delta functions representing distinct reflection paths. This segmentation allows separate processing and identification of direct and reflected signals even in high reverberation conditions.
Solution Approach 2:
The patent performs preliminary estimation of the room impulse response and spectral-temporal patterns before attempting signal separation. By pre-characterizing the reverberation environment and signal properties, the system prepares differentiation capabilities that enable reliable direct-path signal identification under challenging conditions.
2Measurement precision
If deep learning techniques are used to model RIR and remove reverberation, then accuracy of speech recognition improves, but computational complexity and processing time increase
Solution Approach 1:
The patent transforms the RIR estimation problem from the time domain to the frequency domain using Fourier transforms. This parameter change enables efficient computation of spectral-temporal patterns and simplifies the mathematical operations required for accurate RIR modeling, reducing computational complexity while maintaining precision.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with deep learning-based spectral-temporal pattern recognition. The DNN automatically learns and extracts relevant features from the spectrogram, substituting complex manual feature engineering and mechanical filtering approaches with an intelligent system that achieves higher accuracy with optimized computational requirements.
3Reliability
If aggressive reverberation removal is applied to enhance speech quality, then direct-path signal clarity improves, but speech naturalness and temporal characteristics deteriorate
Solution Approach 1:
The patent applies partial dereverberation by controlling the amount of reverberation removal through the parameter alpha. Instead of completely eliminating all reverberation, the system removes a controlled portion that suffices to enhance direct-path signal clarity while preserving enough reverberation to maintain speech naturalness and temporal characteristics.
Solution Approach 2:
The patent applies different processing strengths to different frequency bands and time regions of the speech signal. By adapting the dereverberation intensity locally across the spectro-temporal domain, the system enhances direct-path signal clarity in regions where reverberation dominates while preserving naturalness in regions where the speech signal is already clear.
Data Source
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AI summary
A system and method for reverberation reduction is disclosed. A first Deep Neural Network (DNN) produces a first estimate of a target direct-path signal from a mixture of acoustic signals that include the target direct-path signal and a reverberation of the target direct-path signal. A filter modeling a room impulse response (RIR) for the first estimate is estimated. The filter when applied to the first estimate of the target direct-path signal generates a result closest to a residual between the mixture of the acoustic signals and the first estimate of the target direct-path signal according to a distance function. The estimated filter is used for modeling the RIR.