Sound Context Classification based on Joint Learning Model and Multi-Spectrogram Features (original) (raw)

International Journal of Computing

This article presents a deep learning framework applied for Acoustic Scene Classification (ASC), the task of classifying different environments from the sounds they produce. To successfully develop the framework, we firstly carry out a comprehensive analysis of spectrogram representation extracted from sound scene input, then propose the best multi-spectrogram combination for front-end feature extraction. In terms of back-end classification, we propose a novel joint learning model using a parallel architecture of Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (C-RNN), which is able to learn efficiently both spatial features and temporal sequences of a spectrogram input. The experimental results have proved our proposed framework general and robust for ASC tasks by three main contributions. Firstly, the most effective spectrogram combination is indicated for specific datasets that none of publication previously analyzed. Secondly, our joint learning arc...