Time-Frequency Scattergrams for Biomedical Audio Signal Representation and Classification

Speech, music, and environmental sounds are the main forms of audio signals that are widely studied. There is a certain amount of texture present in every sound, and our human auditory system is not efficient in recognizing and classifying these audio textures present within the sounds. E.g. we are not able to distinguish between two sounds of a fire crackling or two sounds of water-falling. Hence, there is a need for a representation that could model these audio textures. These textures are also present in the audio signals that changes if pathological and pathomorphological conditions are present. To capture and analyze these audio textures, the audio signal is generally transformed into an intermediate time-frequency (t-f) representation such as spectrograms, Mel-spectrograms, and more. But recent studies have shown that joint time-frequency scattering transform is more suitable for classification problems than the standard time-frequency representations because of its inherent property of invariance and invertibility. In this paper, we have investigated the capacity of joint time-frequency scattergrams to capture the audio textures by analyzing the audio structures of biomedical sounds such as COVID-19 cough and breath sounds, pathological speech in children and adults, and infant cry sounds. Accuracy rates up to 96.40% for COVID-19 sounds, 95.50% for pathological speech in children, 94.10% for pathological speech in adults, and 97.30% for infant cry sounds have been achieved with 10-fold cross-validation. The proposed model of using scattergram provides an alternate and an efficient way of representing biomedical audio signals for machine learning applications.