Late Fusion of Convolutional Neural Network with Wavelet-Based Ensemble Classifier for Acoustic Scene Classification

Abstract

Log-Mel spectrogram for the convolutional neural network (CNN) and wavelet time scattering for Ensemble of subspace discriminant classifiers is used for classifying acoustic scenes with human speech. The Tampere University of Technology (TUT) Acoustic Scenes dataset is used to demonstrate the feasibility of the proposed model. Comparisons are performed with the baseline model in the TUT 2017 dataset used for Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge-Task 1. The fused model shows good acoustic classification accuracy of 79.43%. The proposed late fusion of multi-model using CNN and ensemble classifiers exhibits 18.4% higher accuracy than the baseline model with just CNN.