Audio Enhancement via Supervised Latent Variable Representation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional deep learning approaches for target speech enhancement struggle with varying speaker and noise conditions, often resulting in unsatisfactory results when the signal-to-noise ratio differs from the typical training examples, and require numerous models to account for different pitch ranges, leading to exponential complexity.
Innovation Solution
The system employs two embedding networks to encode and disentangle specific characteristics of noise and target speech, using deep embeddings to provide prior information for improved denoising and enhancement, with pre-processing neural networks generating embeddings that are used in conjunction with a noise reduction neural network to generate enhanced audio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional deep learning approaches train a single model for target speech enhancement, then the model structure remains simple, but the model performs poorly when signal-to-noise ratio or speaker characteristics differ from training conditions
Solution Approach 1:
The patent implements dynamic adaptation by training multiple specialized models for different pitch ranges and noise conditions, then dynamically selecting and combining them based on input characteristics. This allows the system to adapt to varying SNR and speaker conditions while maintaining reliable performance through condition-specific expertise.
Solution Approach 2:
The system changes key parameters including pitch range segmentation, SNR thresholds, and model selection criteria to adapt to different operating conditions. By adjusting these parameters based on input signal characteristics, the system achieves both adaptability to new conditions and reliability through parameter-optimized performance.
2Adaptability or versatility
If multiple models are trained for different pitch ranges and noise conditions, then adaptability to varying conditions improves, but the system complexity increases exponentially
Solution Approach 1:
The patent segments the pitch range into distinct intervals and trains separate models for each segment. This segmentation approach provides comprehensive coverage of different pitch conditions while controlling complexity by dividing the problem into manageable, non-overlapping segments rather than requiring a model for every possible condition.
Solution Approach 2:
The system implements multi-functionality by designing models that can handle multiple noise types and pitch ranges within unified frameworks where possible. This universal approach reduces the total number of specialized models needed while maintaining adaptability across diverse conditions through shared architectural components and transfer learning.
3Measurement precision
If deep learning models are trained on diverse noise conditions and pitch levels, then average SSNR improvement is achieved, but signal quality deteriorates when input SNR differs from training examples
Solution Approach 1:
The system performs preliminary classification of the input signal's SNR and pitch characteristics before applying enhancement. This preliminary action allows selection of the most appropriate pre-trained model for the specific conditions, ensuring that the enhancement parameters are optimized for the actual input rather than relying on average performance across all conditions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor the input signal characteristics and adjust model selection and enhancement parameters accordingly. This feedback loop ensures that when input SNR differs from training examples, the system can adapt by selecting models trained on similar conditions or adjusting parameters to maintain signal quality consistency.
Data Source
AI summary
Systems and methods for generating an enhanced audio signal comprise a trained neural network configured to receive an input audio signal and generate an enhanced target signal, the trained neural network comprising a pre-processing neural network configured to receive a segment of the input audio signal and output an audio classification, the pre-processing neural network including at least one hidden layer comprising an embedding vector, and a noise reduction neural network configured to receive the segment of the input audio signal, and the embedding vector and generate the enhanced target signal. The pre-processing neural network may comprise a target signal pre-processing neural network configured to output a target signal classification and comprising at least one hidden layer comprising a target embedding vector. The pre-processing neural network may comprise a noise pre-processing neural network configured output a noise classification and comprising at least one hidden layer comprising a noise embedding vector.