Latent Feature Prediction for Low-Bitrate Speech Coding
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Solution Overview
Problem
Existing neural audio codecs fail to fully exploit temporal correlations in audio signals, leading to redundancy and inefficiency in data transmission, particularly in real-time communications where delays can hinder effective communication.
Innovation Solution
Incorporating contextual coding with temporal predictions into the VQ-VAE framework for neural audio coding, utilizing a learnable extractor and synthesizer to fuse latent features and quantized outputs, and employing end-to-end learnable amplitude compression and vector quantization techniques to reduce redundancy and improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If neural audio codecs are used without temporal prediction, then encoding simplicity is maintained, but temporal correlations are not fully exploited leading to data redundancy
Solution Approach 1:
The patent applies preliminary action by using reconstructed latent features from prior frames to predict current frame latent features before actual encoding occurs. The temporal prediction module generates predictions based on historical data, allowing the system to exploit temporal correlations and reduce redundancy in the encoded data.
Solution Approach 2:
The patent introduces an intermediary approach by inserting a temporal prediction module between the encoder and quantizer. This intermediary component processes latent features through a prediction mechanism that uses past frame information, thereby reducing redundancy without directly increasing the core encoding complexity.
2Manufacturing precision
If more data is transmitted to improve audio quality, then audio quality improves, but transmission time and bandwidth requirements increase
Solution Approach 1:
The patent extracts and transmits only the residual-like features that contain the most important information not captured by temporal predictions. By separating the prediction component (which can be reconstructed) from the residual component (which contains new information), the system reduces the amount of data that needs to be transmitted while maintaining audio quality.
Solution Approach 2:
The patent changes the parameter of data representation by transforming the encoding from direct audio waveforms to latent feature space. This parameter transformation allows for more efficient compression and reduces the bitrate required for transmission while maintaining perceptual audio quality.
3Productivity
If temporal prediction is added to exploit correlations, then data efficiency improves, but processing delays may increase
Solution Approach 1:
The patent segments the encoding process into distinct modules: temporal prediction module, extractor, and quantizer. This segmentation allows each component to process data independently and optimally, reducing overall processing delay while maintaining data efficiency. The prediction module operates on past frames while the current frame is being processed, enabling parallel operations.
Data Source
AI summary
Techniques and solutions are described for encoding and decoding signals, such as audio data. Disclosed innovations can find particular use in speech coding applications, such as for real time communications. Using a neural network, contextual coding can be used to encode latent features for a current frame using a prediction from reconstructed latent features of past frames as a context. An extractor learns a residual-like feature based on such prediction and latent features of the current frame obtained using an encoder. The residual-like feature is then quantized. At a decoder portion of a coding framework, the quantized feature is dequantized and then combined with a prediction from prior reconstructed latent features to provide reconstructed features of a current frame, which can then be processed by a decoder to provide a reconstructed signal.