Acoustic scene classification: from a hybrid classifier to deep learning

Abstract

This report describes our contribution to the 2017 Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. We investigated two approaches for the acoustic scene classification task. Firstly, we used a combination of features in the time and frequency domain and a hybrid Support Vector Machines - Hidden Markov Model (SVM-HMM) classifier to achieve an average accuracy over 4-folds of 80.9% on the development dataset and 61.0% on the evaluation dataset. Secondly, by exploiting dataaugmentation techniques and using the whole segment (as opposed to splitting into sub-sequences) as an input, the accuracy of our CNN system was boosted to 95.9%. However, due to the small number of kernels used for the CNN and a failure of capturing the global information of the audio signals, it achieved an accuracy of 49.5% on the evaluation dataset. Our two approaches outperformed the DCASE baseline method, which uses log-mel band energies for feature extraction and a Multi-Layer Perceptron (MLP) to achieve an average accuracy over 4-folds of 74.8%.

Description

Keywords

Acoustic scene classification, feature extraction, deep learning, spectral features, data augmentation

Citation

Vafeiadis, A. et al. (2017) Acoustic scene classification: from a hybrid classifier to deep learning. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Nov. 2017.

Rights

Research Institute

Cyber Technology Institute (CTI)
Institute of Artificial Intelligence (IAI)
Institute of Engineering Sciences (IES)