Parameter Tuning for Wavelet-Based Sound Event Detection Using Neural Networks

Wavelet-based audio processing is used for sound event detection. The low-level audio features (timbral or temporal features) are found to be effective to differentiate between different sound events and that is why frequency processing algorithms have become popular in recent times. Wavelet based sound event detection is found effective to detect sudden onsets in audio signals because it offers unique advantages compared to traditional frequency-based sound event detection using machine learning approaches. In this work, wavelet transform is applied to the audio to extract audio features which can predict the occurrence of a sound event using a classical feedforward neural network. Additionally, this work attempts to identify the optimal wavelet parameters to enhance classification performance. 3 window sizes, 6 wavelet families, 4 wavelet levels, 3 decomposition levels and 2 classifier models are used for experimental analysis. The UrbanSound8k data is used and a classification accuracy up to 97% is obtained. Some major observations with regard to parameter-estimation are as follows: wavelet level and wavelet decomposition level should be low; it is desirable to have a large window; however, the window size is limited by the duration of the sound event. A window size greater than the duration of the sound event will decrease classification performance. Most of the wavelet families can classify the sound events; however, using Symlet, Daubechies, Reverse biorthogonal and Biorthogonal families will save computational resources (lesser epochs) because they yield better accuracy compared to Fejér-Korovkin and Coiflets. This work conveys that wavelet-based sound event detection seems promising, and can be extended to detect most of the common sounds and sudden events occurring at various environments.