Conditioned Audio Enhancement Neural Network for Multi-Loss Training
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Solution Overview
Problem
Existing deep learning models for audio enhancement are computationally expensive and inefficient when covering multiple signal categories and conditions, requiring separate neural networks for each category, bitrate, and codec, leading to high computational load and memory usage.
Innovation Solution
A loss conditional training method for a neural network, where a coefficient vector is randomly sampled from a distribution to condition the network, allowing a single neural network to cover a wide range of coefficients and conditions, using Feature-wise Linear Modulation (FiLM) and multi-resolution STFT loss functions to balance loss terms, enabling efficient training and inference across different signal categories and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional audio processing methods are used, then computational resources are consumed, but processing speed and real-time performance deteriorate
Solution Approach 1:
The patent transforms the audio processing approach by changing the fundamental parameters from traditional spectral analysis to neural network-based feature extraction. This parameter change enables real-time processing by optimizing the computational characteristics of the system, achieving both speed improvement and controlled resource consumption.
Solution Approach 2:
The patent replaces traditional mechanical/audio signal processing methods with a neural network system. This substitution introduces a learning-based approach that can process audio data more efficiently, achieving real-time performance while managing computational resources through optimized network architecture and training techniques.
2Measurement precision
If complex audio processing models are used, then processing accuracy is improved, but model complexity and training difficulty increase
Solution Approach 1:
The patent segments the audio processing task into distinct components handled by different neural network modules. This segmentation allows each module to specialize in specific aspects of audio processing, improving overall accuracy while managing complexity through modular architecture that can be trained and deployed independently.
Solution Approach 2:
The patent implements dynamic elements in the neural network architecture, allowing the model to adapt its processing characteristics based on the input audio characteristics. This dynamic approach enables the system to maintain high accuracy across diverse audio inputs while managing complexity through conditional processing paths rather than uniformly complex architecture.
Data Source
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AI summary
A computer-implemented method of loss conditional training of a neural network for outputting an enhanced audio signal, the method including: randomly sampling a coefficient vector from a distribution of coefficients, wherein elements of the coefficient vector are indicative of weight coefficients corresponding to loss terms of a loss function; conditioning the neural network based on the coefficient vector; and training the conditioned neural network based on an audio training signal, wherein the training involves calculating the loss function for the audio training signal after processing by the conditioned neural network, using the weight coefficients indicated by the coefficient vector.