Introduction: New approaches to efficient compression and digital processing of audio signals are relevant today. There is a lot of interest to new pattern recognition methods which can improve the quality of acoustic anomaly detection. Purpose: Comparative analysis of methods for time-frequency transformation of audio signal patterns, including non-stationary quasiperiodic biomedical signals in the problem of acoustic anomaly detection. Results: The study compared different time-frequency transforms (such as windowed Fourier, Gabor, Wigner, pseudo Wigner, smoothed pseudo Wigner, Choi — Williams, Bertrand, pseudo Bertrand, smoothed pseudo Bertrand, and wavelet transforms) based on systematization of their functional characteristics (such as the existence and limitedness of basis functions, presence of zero moments and biorthogonal form, opportunity of two-dimensional representation and inverse transformation, real time processing, time-frequency transform quality, control of time-frequency definition, time and frequency interference suppression, relative computational complexity, fast algorithm implementation) for the problem of biomedial signal pattern recognition. A comparative table is presented with estimates of information capacity for the considered time-frequency transforms. Practical relevance: The proposed approach can solve some acoustic anomaly detection algorithm implementation problems common in non-stationary quasi-periodic processes, in order to study disruptive effects causing a change in the functional state of ergatic system operators.