DCASE 2016 ACOUSTIC SCENE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS
This workshop paper presents our contribution for the acoustic scene classification (ASC) task proposed for the "detection and classification of acoustic scenes and events" (DCASE) 2016 challenge. We propose the use of a convolutional neural network trained to classify short sequences of audio, represented by their log mel spectrogram. We also propose a training method that can be used when the system validation performance saturates as the training proceeds. The system is evaluated on the public ASC development dataset provided for the DCASE 2016 challenge. The best accuracy score obtained by our system on a four fold cross validation setup is 79.0% which constitutes a 8.8% relative improvement with respect to the baseline system.