TY - GEN
T1 - A convolutional neural network approach for acoustic scene classification
AU - Valenti, Michele
AU - Squartini, Stefano
AU - Diment, Aleksandr
AU - Parascandolo, Giambattista
AU - Virtanen, Tuomas
N1 - jufoid=58177
PY - 2017/6/30
Y1 - 2017/6/30
N2 - This paper presents a novel application of convolutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the-art systems which competed in the 'Detection and Classification of Acoustic Scenes and Events' (DCASE) challenges held in 20161 and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner's score.
AB - This paper presents a novel application of convolutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the-art systems which competed in the 'Detection and Classification of Acoustic Scenes and Events' (DCASE) challenges held in 20161 and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner's score.
U2 - 10.1109/IJCNN.2017.7966035
DO - 10.1109/IJCNN.2017.7966035
M3 - Conference contribution
AN - SCOPUS:85031008536
SP - 1547
EP - 1554
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017
PB - IEEE
T2 - International Joint Conference on Neural Networks
Y2 - 1 January 1900
ER -