Novel Phase Encoded Mel Filterbank Energies for Environmental Sound Classification

Abstract

In Environment Sound Classification (ESC) task, only the magnitude spectrum is processed and the phase spectrum is ignored, which leads to degradation in the performance. In this paper, we propose to use phase encoded filterbank energies (PEFBEs) for ESC task. In proposed feature set, we have used Mel-filterbank, since it represents characteristics of human auditory processing. Here, we have used Convolutional Neural Network (CNN) as a pattern classifier. The experiments were performed on ESC-50 database. We found that our proposed PEFBEs feature set gives better results compared to the state-of-the-art Filterbank Energies (FBEs). In addition, score-level fusion of FBEs and proposed PEFBEs have been carried out, which leads to further relatively better performance than the individual feature set. Hence, the proposed PEFBEs captures the complementary information than FBEs alone.