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Algorithmic evaluation demonstrates improved compression ratios and faster decoding in lossless audio, indicating significant efficiency gains from machine-learning-refined predictive coding.
This paper presents SRLA (Svr-fiR Lossless Audio), a practical lossless audio codec based on linear predictive coding (LPC). SRLA systematically integrates and evaluates three improvements for LPC-based lossless audio coding: (i) Support Vector Regression (SVR)-motivated refinement of LPC coefficients, (ii) adaptive audio-frame segmentation, and (iii) efficient use of higher LPC prediction orders. The study also analyzes a continuous relaxation of the recursive Golomb–Rice (RGR) codeword length under the adopted parameter setting and uses it to motivate an SVR-type coefficient refinement. In the maximum-compression setting, SRLA reduced the compressed-size ratio by 1.04 percentage points and achieved a 2.3-fold lower decoding-time ratio than MPEG-4 ALS, at the cost of a 7.3-fold higher encoding-time ratio. The ablation study further shows that the proposed components have different compression gains and encoder-side costs while having little effect on decoding time.
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Mineo et al. (2026) studied this question.
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