Acoustic Scene Classification: an Evaluation of an Extremely Compact Feature Representation

This paper investigates several approaches to address the acoustic scene classification (ASC) task. We start from low-level feature representation for segmented audio frames and investigate different time granularity for feature aggregation. We study the use of support vector machine (SVM), as a well-known classifier, together with two popular neural network (NN) architectures, namely multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluate the performance of these approaches on benchmark datasets provided from the 2013 and 2016 Detection and Classification of Acoustic Scenes and Events (DCASE) challenges. We observe that a simple approach exploiting averaged Mel-logspectrograms and SVM can obtain even better results than NNbased approaches and comparable performance with the best systems in the DCASE 2013 challenge.