Deep Neural Network Baseline for DCASE Challenge 2016
The DCASE Challenge 2016 contains tasks for Acoustic Scene Classification (ASC) Acoustic Event Detection (AED) and audio tagging. Since 2006, Deep Neural Networks (DNNs) have been widely applied to computer visions, speech recognition and natural language processing tasks. In this paper, we provide DNN baselines for the DCASE Challenge 2016. In Task 1 we obtained accuracy of 81.0% using Mel DNN against 77.2% by using Mel Frequency Cepstral Coefficients (MFCCs) Gaussian Mixture Model (GMM) In Task 2 we obtained F value of 12.6% using Mel DNN against 37.0% by using Constant Q Transform (CQT) Nonnegative Matrix Factorization (NMF) In Task 3 we obtained F value of 36.3% using Mel DNN against 23.7% by using MFCCs GMM. In Task 4 we obtained Equal Error Rate (ERR) of 18.9% using Mel DNN against 20.9% by using MFCCs GMM. Therefore the DNN improves the baseline in Task 1, 3, and 4, although it is worse than the baseline in Task 2. This indicates that DNNs can be successful in many of these tasks, but may not always perform better than the baselines.