Scalar Audio Quantization for Rate-Scalable Neural Coding
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Solution Overview
Problem
Conventional neural audio coders (NACs) face challenges such as high computational complexity, unintuitive discrete representations, difficulty in trading off data rate against quality without retraining, and instability during training, particularly due to non-differentiable vector quantizers (VQs).
Innovation Solution
Employing a scalar quantization method with a learnable encoder-decoder pair that includes a scalar quantization module and quantizers to convert latent scalar values into indexes, allowing for efficient and interpretable discrete representations, and enabling scalable data rate adjustments without retraining the NAC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vector quantizers (VQs) are used for discrete representation in NACs, then the audio quality can be maintained, but the computational complexity increases substantially
Solution Approach 1:
The patent segments the vector quantization process into multiple scalar quantization operations. Instead of performing one complex vector quantization, the latent representation is divided and quantized channel-wise using scalar quantizers, reducing computational complexity while maintaining reconstruction quality
Solution Approach 2:
The patent replaces the conventional vector quantization mechanism with a scalar quantization mechanism. This substitution changes the fundamental approach from vector-space operations to scalar operations, dramatically reducing computational complexity while preserving the essential function of discrete representation
2Measurement precision
If vector quantizers (VQs) are used for discrete representation, then audio quality is maintained, but the interpretability of the discrete representation decreases
Solution Approach 1:
By segmenting the latent representation into independent channels and applying scalar quantization to each channel separately, the patent creates interpretable discrete values that can be understood individually rather than as opaque vector operations
Solution Approach 2:
The patent changes the parameter space from high-dimensional vectors to scalar values. This transformation makes the discrete representation more interpretable by converting abstract vector operations into concrete scalar quantization levels that can be directly analyzed and understood
3Measurement precision
If conventional NACs are retrained to trade off data rate against quality, then the optimal performance can be achieved, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary action by designing a scalar quantization framework that inherently supports rate scalability without requiring retraining. The architecture is pre-configured to allow flexible adjustment of data rate through quantization level selection, eliminating the need for time-consuming retraining processes
Solution Approach 2:
The patent introduces dynamics by making the quantization process adaptive and configurable. The scalar quantizer can dynamically adjust its operation parameters (such as number of quantization levels) to achieve different data rates and quality trade-offs without retraining, enabling flexible real-time optimization
4Measurement precision
If vector quantizers (VQs) are used, then discrete representation is achieved, but training stability deteriorates due to non-differentiability
Solution Approach 1:
The patent introduces an intermediary approach by using scalar quantization as a bridge between continuous latent representations and discrete coded signals. This intermediary quantization process is designed to be more compatible with gradient-based optimization, improving training stability while still achieving the necessary discrete representation for compression
Data Source
AI summary
There are described techniques for encoding and decoding audio signals. A decoder, configured to generate an audio signal from a coded signal representing the audio signal, may include: a coded signal reader, configured to read the coded signal, thereby providing a plurality of indexes; a scalar dequantization module, including: a plurality of quantization index converters, each quantization index converter being configured to convert an index of the plurality of indexes onto a corresponding latent scalar value, so that a plurality of latent scalar values form a first latent audio signal representation of the audio signal; and a first learnable section to provide a second latent representation from the first latent audio signal representation; a second learnable section including at least one learnable layer and configured to generate the audio signal from the second latent audio signal representation.