Abstract
The paper considers the use of such linear interpolation algorithms as LPC, FLPC, and Wise-LPC in the lossless audio data compression. In addition to the interpolation methods, the problems of best coding and optimal sampling window selection are investigated. The Wise-LPC algorithm is shown to allow a 1–5% improvement of audio signal compression against conventional LPC and FLPC approaches. The prediction error has a Laplace distribution, its variance decreasing smoothly and reaching “saturation” with the growing window width.
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Acknowledgements
The research was supported by the State Program SRISA RAS No. 0065-2019-0003 (AAA-A19-119011590090-2).
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Telyatnikov, L.S., Karandashev, I.M. (2020). Linear Prediction Algorithms for Lossless Audio Data Compression. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_42
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DOI: https://doi.org/10.1007/978-3-030-30425-6_42
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