Neural Audio Codec Quantization via White Noise Modeling
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Solution Overview
Problem
Existing neural network-based audio codecs require a softmax quantizer, which necessitates a soft-to-hard annealing process and an additional quantization penalty loss, complicating the optimization of overall loss during training.
Innovation Solution
The method employs white noise modeling as an approximated quantization process, generating a quantized latent vector from an input signal, and computes a total loss to train the neural network-based audio codec without the need for a softmax quantizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a softmax quantizer is used to approximate non-differentiable quantization, then the codec can be trained using error back-propagation, but the training process requires soft-to-hard annealing and additional quantization penalty loss, complicating optimization
Solution Approach 1:
The patent extracts and removes the softmax quantizer from the system, replacing it with a simplified quantization approach that does not require soft-to-hard annealing or additional penalty loss terms. This extraction eliminates the complex training requirements while maintaining the essential quantization function needed for codec operation.
Solution Approach 2:
The patent changes the quantization parameter representation from softmax probabilities to a simpler form that can be directly optimized without annealing schedules. By modifying how quantization parameters are handled and represented, the system achieves trainability without the complex training procedures associated with softmax quantizers.
2Manufacturing precision
If additional quantization penalty loss and entropy-related loss terms are added to optimize the softmax quantizer, then the quantization accuracy improves, but the overall loss optimization becomes more complex and difficult to converge
Solution Approach 1:
The patent removes the quantization penalty loss term from the loss function, replacing it with a simpler approach that achieves quantization accuracy without requiring this additional loss component. By extracting this complex term, the system maintains quantization performance while simplifying the overall optimization landscape.
Solution Approach 2:
The patent uses a simplified loss function that copies only the essential elements needed for quantization training, omitting the complex penalty terms. This copied-down essential functionality achieves the same quantization accuracy with a much simpler loss structure that is easier to optimize.
Data Source
AI summary
Provided is a method and apparatus for designing and testing an audio codec using quantization based on white noise modeling. A neural network-based audio encoder design method includes generating a quantized latent vector and a reconstructed signal corresponding to an input signal by using a white noise modeling-based quantization process, computing a total loss for training a neural network-based audio codec, based on the input signal, the reconstruction signal, and the quantized latent vector, training the neural network-based audio codec by using the total loss, and validating the trained neural network-based audio codec to select the best neural network-based audio codec.