Audio Codec Vector Quantization Correction for Outlier Indices
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Solution Overview
Problem
Neural-based audio encoders and vector quantizers often assign 'outlier' indices during post-training, leading to audio distortion and degraded output due to improbable assignments.
Innovation Solution
Implement a vector quantization correction (VQC) process that considers transition probabilities and cluster distances to correct these outlier indices, generating a preferred candidate sequence that reduces distortion and improves decoded audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If vector quantizer uses distance computation to assign indices to clusters, then quantization speed is improved, but outlier indices are assigned causing audio distortion
Solution Approach 1:
The system performs preliminary actions by computing transition probabilities between all pairs of indices during training and storing them in a transition probability matrix. This pre-computed information is then used during inference to evaluate and correct potential outlier indices without requiring re-computation of complex probability distributions, thus maintaining speed while improving reliability.
Solution Approach 2:
The system implements feedback by using the transition probability matrix to evaluate and correct indices assigned by the vector quantizer. The corrected indices are fed back into the decoding process, and the system continuously optimizes the transition probabilities based on training data to improve audio quality while maintaining quantization speed.
2Productivity
If vector quantizer assigns indices based on cluster distance, then compression efficiency is improved, but outlier indices cause degraded audio output
Solution Approach 1:
The system changes the parameter space from simple distance-based index selection to a multi-parameter evaluation that incorporates transition probabilities. By adjusting the decision criteria to consider both distance and transition likelihoods, the system maintains compression efficiency while reducing outlier assignments that degrade audio quality.
3Adaptability or versatility
If neural-based audio encoder and vector quantizer are trained data-driven, then adaptation to audio data is improved, but post-training outlier assignments occur
Solution Approach 1:
The system performs preliminary training to compute and store transition probabilities between indices in a matrix structure. This pre-computed information captures the statistical relationships learned during data-driven training and is then used during inference to correct outlier indices, maintaining adaptability while improving assignment accuracy.
Solution Approach 2:
The system creates a copy of the training insights in the form of a transition probability matrix that can be efficiently queried during inference. This copied information allows the system to leverage the adaptability gained from data-driven training without re-computing complex probability distributions, thus maintaining versatility while improving reliability.
Data Source
AI summary
A method comprises: vector quantizing input vectors representative of audio into an original sequence including indices of codewords of a codebook; generating candidate sequences including the indices of the codewords of the codebook by evaluating, for each candidate sequence, transition costs for transitions between the indices based on (i) transition probabilities of the transitions, and (ii) distances between the codewords represented by the indices and the input vectors that corresponds to the indices; determining a preferred candidate sequence of the candidate sequences to replace the original sequence based on the transition costs for each candidate sequence; and transmitting the preferred candidate sequence in place of the original sequence.