B.S. Goryachkin1, R.V. Fonkants2, R.R. Safin3

1–3 Bauman Moscow State Technical University (Moscow, Russia)

Every day the volume of digital information is inexorably increasing, and this leads to the fact that to store this information, more and more large arrays of storage devices are needed, requiring serious financial investments. To reduce the amount of stored data, special algorithms for encoding / compressing information were created. One of these is the Huffman algorithm, which is used in many modern archivers. To obtain better data compression ratio, classical algorithms undergo various improvements. In this paper, we propose a modification of the Huffman algorithm using genetic algorithms for compression of WAV files.

Purpose. Analyzing and comparing Huffman coding and modern lossless compression audio codecs. Developing a modification of Huffman coding using genetic algorithms.

It is shown that the classical Huffman algorithm does not provide significant advantages over the proposed evaluation method compared to audio codecs for encoding WAV files. On this basis, a modification of the classical Huffman coding using genetic algorithms has been developed. Testing the developed modification of the algorithm showed that the developed modification of the Huffman algorithm is still inferior to popular audio codecs, although it is better than the classical one. In addition, in the course of the work, an analytical formula for estimating the encoding / decoding time for the considered lossless audio data compression algorithms was experimentally derived.

The developed modification of the Huffman algorithm makes it possible to compress WAV files more efficiently than using classical lossless data compression algorithms. The obtained formula for estimating the encoding/decoding time for the algorithms considered in the work makes it possible to conduct a qualitative comparative analysis.

Goryachkin B.S., Fonkants R.V., Safin R.R. Comparative analysis of lossless audio data compression formats. Dynamics of complex systems. 2023. V. 17. № 3. P. 64−71. DOI: 10.18127/j19997493-202303-09 (in Russian).

1. Klassifikaciya metodov szhatiya. URL: https://mf.grsu.by/UchProc/livak/po/comprsite/classification_intro.html (data obrashcheniya: 26.01.2023) (in Russian).
2. Mayer H.F. Principles of Pulse Code Modulation. Advances in Electronics and Electron Physics. Academic Press. 1951. V. 3. P. 221–260.
3. Huffman D.A. A Method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE. 1952. V. 40. № 9. P. 1098– 1101.
4. Vatolin D., Ratushnyak A., Smirnov M., Yukin V. Metody szhatiya dannyh. Ustrojstvo arhivatorov, szhatie izobrazhenij i video. M.: DIALOG-MIFI, 2003. 384 c. (in Russian).
5. Tropchenko A.Yu., Tropchenko A.A. Metody szhatiya izobrazhenij, audiosignalov i video: Ucheb. posobie. SPb.: SPbGU ITMO. 2009. 108 s. (in Russian).
6. FLAC. URL: https://xiph.org/flac/index.html (data obrashcheniya: 18.02.2023).
7. Monkey's Audio. URL: https://monkeysaudio.com (data obrashcheniya: 18.02.2023).
8. WavPack. URL: https://www.wavpack.com (data obrashcheniya: 18.02.2023).
9. Goryachkin B.S., Bagaviev B.I. Razrabotka metoda ocenki tochnosti raspoznavaniya audiosignala s pomoshch'yu nejroseti dlya bol'shih ob"emov dannyh. Dinamika slozhnyh sistem – XXI vek. 2020. T. 14. № 2. S. 63-70. DOI: 10.18127/j19997493-202002-07 (in Russian).
10. Gusev S.I., Spirkina O.V. Effektivnost' adaptivnogo algoritma podavleniya pomekh s ispol'zovaniem prostranstvennoj predprocessornoj obrabotki signalov. Radiotekhnika. 2016. T. 80. № 8. S. 86–90 (in Russian).
11. Burakov M.V. Geneticheskij algoritm: teoriya i praktika: ucheb. posobie. SPb.: GUAP, 2008. 164 s. (in Russian).
12. Holland J.H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor: University of Michigan Press. 1975. 183 p.
13. Panchenko T.V. Geneticheskie algoritmy: uchebno-metodicheskoe posobie. Astrahan': Izdatel'skij dom «Astrahanskij universitet». 2007. 87 s. (in Russian).