Loss-Conditioned Audio Neural Network for Multi-Codec Enhancement
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Solution Overview
Problem
Existing deep learning models for audio enhancement are computationally expensive and inefficient when covering different signal categories such as speech, music, applause, and varying bitrates and codecs, requiring multiple separate neural networks.
Innovation Solution
A loss conditional training method that randomly samples a coefficient vector from a distribution to condition a neural network, allowing it to balance multiple loss functions and cover a wide range of conditions, using a single neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate neural networks are trained for different signal categories and conditions, then the performance for each specific task is optimized, but the device complexity and computational overhead increase significantly
Solution Approach 1:
The patent applies universality by designing a single neural network that can handle multiple signal categories (speech, music, applause) and different conditions (bitrates, codecs) through conditional input vectors. The network architecture includes a conditioning module that receives category indicators and adapts the network's behavior accordingly, allowing one network to perform functions that previously required multiple specialized networks.
Solution Approach 2:
The patent implements dynamics by making the neural network's parameters adaptive based on the input signal category. A conditioning vector is generated from the signal category information and used to dynamically adjust the network's internal representations and processing characteristics, enabling the same network structure to optimize its performance for different task conditions without requiring separate static models.
2Reliability
If deep convolutional models are used to reduce coding artifacts, then the audio quality is improved, but the computational load and memory usage increase
Solution Approach 1:
The patent applies parameter changes by modifying the neural network's operational parameters based on the signal category and coding conditions. The conditioning mechanism adjusts key parameters such as feature extraction depth, processing resolution, and loss function weights according to the input signal characteristics, allowing the model to reduce computational complexity for certain signal types while maintaining audio quality where full processing is necessary.
3Reliability
If separate models are trained for different codecs and bitrates, then the performance for each coding condition is optimized, but the adaptability of a single model decreases
Solution Approach 1:
The patent applies segmentation by dividing the conditioning information into distinct categories (signal type, codec type, bitrate range) that are processed separately through dedicated embedding layers. Each segment of the conditioning vector corresponds to a specific aspect of the audio signal, allowing the network to independently adapt to different coding conditions while maintaining a unified architecture that covers all scenarios.
Data Source
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.