Method of Semantic Coding of Speech Signals based on Empirical Wavelet Transform

The result of this work is to solve the current scientific and practical problem of developing and researching new effective methods of semantic coding of speech signals. A known method of semantic coding of speech signals based on mel-frequency cepstral coefficients, which does not comply with the condition of adaptability to the studied signal, which is a significant disadvantage of the existing method. A method of semantic coding of speech signals based on empirical wavelet transform is developed, which constructs sets of adaptive Meyer wavelet filters with subsequent application of Hilbert spectral analysis to find instantaneous amplitudes and frequencies of functions of internal empirical modes, which will determine semantic efficiency coding. It is proposed to use the method of adaptive empirical wavelet transform in problems of multiplescale analysis and semantic coding of speech signals, which will increase the efficiency of spectral analysis by decomposing high- frequency speech oscillations into its low-frequency components, namely internal empirical modes. An algorithm for semantic coding of speech signals based on empirical wavelet transform and its software implementation in the MATLAB R2020b programming language has been developed.