Speech Enhancement Control for Neural Codec Compatibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
State-of-the-art communication pipelines using neural speech codecs face challenges in maintaining speech quality due to the need to consider background noise during training, which consumes model capacity and requires larger, more complex networks, and alignment issues between Speech Enhancement Modules (SEMs) and neural codecs reduce flexibility and compatibility.
Innovation Solution
An audio processor with a control unit that adjusts a speech enhancement module based on control signals derived from downstream task modules or user inputs, allowing the neural codec to operate effectively with noisy speech without retraining, thus simplifying training and enhancing compatibility with existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If background noise is considered during training of neural codec, then speech quality in noisy conditions is improved, but model capacity is consumed and network complexity increases
Solution Approach 1:
The system is divided into two separate modules: a speech enhancement module (SEM) that processes noisy speech before it enters the neural codec. This segmentation allows the neural codec to be trained on clean speech while the SEM handles noise reduction, thus maintaining speech quality without increasing codec complexity
Solution Approach 2:
The speech enhancement module acts as an intermediary between the noisy input and the neural codec. It preprocesses the noisy speech to produce enhanced speech that the neural codec can process effectively, eliminating the need for the codec to directly handle noisy conditions
2Reliability
If Speech Enhancement Module and neural codec are aligned with each other, then speech quality is maintained, but flexibility and compatibility with existing solutions are reduced
Solution Approach 1:
The speech enhancement module is designed as a universal preprocessing component that can work with any neural codec without requiring specific alignment or retraining. The SEM produces enhanced speech in a format that is compatible with existing codecs, maintaining both speech quality and system flexibility
Solution Approach 2:
The speech enhancement module performs preliminary processing of the noisy speech before it reaches the neural codec. By preprocessing the speech to remove noise and enhance quality beforehand, the codec receives clean input that it was originally designed to handle, eliminating alignment requirements
3Ease of manufacture
If Speech Enhancement Module and neural codec are aligned, then training of overall system is improved, but training difficulty increases due to reduced flexibility
Solution Approach 1:
The training process is segmented into two independent stages: training the speech enhancement module on noisy speech data, and training the neural codec on clean speech data. This segmentation allows each module to be trained separately with appropriate data, simplifying the overall training process while maintaining speech quality
Data Source
Figure 1~3
AI summary
Audio processor (10), comprising an audio enhancement module (12) configured to process an input audio signal to obtain an enhanced audio signal (ES) and a control unit (14) configured to generate a control signal (CS), wherein the audio enhancement module is adjusted dependent on the control signal (CS).