Two-Stage Neural Network Speech Processing for Interference Suppression
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Solution Overview
Problem
Current speech enhancement systems using neural networks face challenges in effectively suppressing acoustic interference while maintaining high-quality speech, often resulting in audible distortions and temporal discontinuities due to their dependency on specific interference types and requiring multiple processing stages that are not compatible with low-delay applications.
Innovation Solution
A two-stage neural network-based speech processing system is proposed, with a first stage configured for acoustic interference suppression and a second stage for speech restoration, using distinct neural network topologies to achieve strong interference attenuation and natural speech quality, even in non-stationary conditions, by decoupling processing into separate tasks and employing joint training for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-stage neural network is used for speech enhancement, then the system complexity is reduced, but the speech quality and interference attenuation performance deteriorate
Solution Approach 1:
The speech enhancement system is divided into two distinct stages: a first neural network stage for acoustic interference suppression and a second neural network stage for speech restoration. This segmentation allows each stage to specialize in specific tasks, achieving better overall performance than a single-stage system while managing complexity through modular design.
2Object-affected harmful factors
If strong interference attenuation is achieved through aggressive processing, then the interference suppression performance is improved, but audible speech distortions and temporal discontinuities increase
Solution Approach 1:
The processing is segmented into two stages with different objectives: the first stage aggressively suppresses interference, while the second stage specifically targets speech restoration and distortion correction. This separation allows strong interference attenuation without sacrificing speech quality.
Solution Approach 2:
The first stage output serves as an intermediary that feeds into the second stage. This intermediate representation allows the system to progressively refine the signal, with the second stage acting as a mediator that corrects distortions introduced by aggressive interference suppression.
3Manufacturing precision
If multiple processing stages are used for speech enhancement, then the speech quality is improved, but the processing delay increases making it incompatible with low-delay applications
Solution Approach 1:
The two-stage architecture processes different aspects of speech enhancement in parallel-capable stages, where each stage can be optimized for computational efficiency. This segmentation enables quality improvement without excessive delay accumulation.
Solution Approach 2:
The first stage performs preliminary interference suppression to clean the input signal before it enters the second stage. This preliminary action reduces the processing burden on subsequent stages, enabling faster overall processing and lower delay.
4Measurement precision
If the neural network is trained for a specific interference type, then the performance on that interference type is improved, but the adaptability to different interference instances deteriorates
Solution Approach 1:
The two-stage neural network system is designed with universal applicability across different interference types. The first stage learns general interference suppression patterns, while the second stage restores speech characteristics regardless of the original interference type, making the system adaptable to diverse acoustic conditions.
Data Source
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AI summary
A speech processing system (100, 200) comprises an input that receives an input speech signal (101, 201) that comprises clean speech and acoustic interference; a first neural network circuit (102, 216) configured to suppress the acoustic interference in the input speech signal (101, 201) and output (120) a representation of an estimate of a clean speech signal (209); and a second neural network circuit (103, 217) configured to receive the representation of the estimated clean speech signal (209) and restore at least one speech component of the clean speech in the input speech signal (101, 201) and output a reconstructed speech signal (104, 215).