GRU-Based Neural Feature Prediction for Low-Bitrate Speech Coding
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Solution Overview
Problem
Existing speech coding technologies face challenges in achieving low bitrate, high quality, and low latency while maintaining low complexity, particularly in neural network-based systems.
Innovation Solution
A neural speech codec employs neural feature prediction to reduce temporal redundancy, using a recurrent neural network (RNN) with a gated recurrent unit (GRU) for encoding and decoding speech signals, and discriminative coding to optimize bit allocation based on residual signal energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If neural network-based speech coding is used to improve speech reconstruction quality, then coding trade-off is improved, but system complexity and computational load increase
Solution Approach 1:
The speech signal is divided into multiple frames, and the feature extraction and coding process is segmented into distinct stages: feature extraction from ground-truth, neural network-based prediction, residual calculation, and quantization. This segmentation allows each component to be optimized independently while maintaining overall system performance.
Solution Approach 2:
The system performs preliminary feature extraction to obtain ground-truth feature vectors before encoding. By pre-computing and storing these feature vectors, the system reduces the computational burden during the actual encoding process, as the neural network only needs to process the pre-extracted features rather than raw audio data.
2Manufacturing precision
If more bits are allocated to represent speech features for higher quality, then speech reconstruction quality is improved, but bitrate increases
Solution Approach 1:
The system introduces feature vectors as an intermediary representation between the raw speech signal and the final coded output. By working in the feature domain rather than the raw audio domain, the system can achieve better compression efficiency, as the feature vectors capture the essential speech characteristics with fewer bits compared to direct audio coding.
Solution Approach 2:
The system transforms the speech signal into a different parameter space (feature vectors) where the data has more favorable statistical properties for compression. The residual signals, which represent the difference between ground-truth and predicted features, typically have lower energy and more predictable patterns, enabling more efficient quantization and coding.
3Manufacturing precision
If complex quantization schemes are used to improve coding efficiency, then coding trade-off is improved, but computational complexity increases
Solution Approach 1:
The system applies quantization selectively based on the importance and energy of different residual components. Rather than uniformly quantizing all features with the same precision, the system adapts the quantization step size according to the local characteristics of the residual signal, applying finer quantization where needed and coarser quantization where acceptable, thus balancing quality and complexity.
4Speed
If real-time speech coding is implemented to reduce latency, then processing speed is improved, but accuracy of prediction decreases
Solution Approach 1:
The neural network prediction is applied locally to each frame or small group of frames rather than processing the entire speech signal at once. This local processing approach enables real-time operation with minimal latency, as each frame can be encoded independently or with minimal look-ahead, while still maintaining prediction accuracy through the use of previously decoded frames for context.
Data Source
AI summary
A method of encoding a speech signal includes predicting a feature vector of each of a plurality of frames included in the speech signal based on a ground-truth feature vector of a previous frame of each of the plurality of frames, calculating a residual signal corresponding to each of the plurality of frames based on a ground-truth feature vector of each of the plurality of frames and a predicted feature vector of each of the plurality of frames, and generating a bitstring corresponding to each of the plurality of frames by quantizing the residual signal.