Neural Audio Processing Using Bit-Rate Reduced Intermediates
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Solution Overview
Problem
Existing neural network systems for audio signal processing become impractically complex due to the need for large amounts of training data and computational resources, and vector quantization methods introduce trade-offs between complexity reduction and quality, often removing relevant information.
Innovation Solution
A two-stage neural network architecture is employed, where a first network generates a bit-rate reduced representation associated with a pre-defined audio encoding process, followed by a second network that enhances this representation, allowing for separate training objectives and reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single neural network system is used for audio signal processing, then the system can be trained end-to-end, but the system becomes impractically complex requiring enormous training data and computational resources
Solution Approach 1:
The patent divides a single complex neural network into two separate networks: a first network that generates bit-rate reduced representations and a second network that enhances these representations. This segmentation reduces the complexity of each individual network while maintaining overall processing quality through coordinated operation of both networks
2Device complexity
If vector quantization is applied to reduce complexity, then computational resources are reduced, but relevant information is removed limiting achievable performance
Solution Approach 1:
The patent changes the parameter representation by using bit-rate reduced representations with formats associated with predefined audio encoding processes. This allows the system to operate with compressed representations that retain essential information while reducing computational complexity through optimized parameter encoding
3Device complexity
If vector quantization is applied directly to the signal, then complexity is reduced, but the trade-off between complexity reduction and attainable quality is difficult to optimize
Solution Approach 1:
The patent introduces bit-rate reduced representations as an intermediary between the input audio signal and the final processed output. This intermediary layer with predefined encoding formats enables optimized trade-offs between complexity and quality by serving as a structured bridge that preserves essential information while reducing computational demands
Data Source
AI summary
A method for processing an input audio signal, comprising conditioning a first neural network system with a representation of the input audio signal to predict a bit-rate reduced representation of a processed input audio signal, the first neural network system being trained to generate a bit-rate reduced representation of a processed version of a given audio signal, wherein the bit-rate reduced representation has a format associated with a pre-defined audio encoding process, conditioning a second neural network system with the bit-rate reduced representation to predict an enhanced representation of the processed audio signal, the second neural network system being trained to generate an enhanced representation of a given a bit-rate reduced audio representation, wherein the bit-rate reduced representation has a format associated with the pre-defined audio encoding process, and transforming the enhanced representation of the processed audio signal into an output audio signal.