An Audio Scene Classification Framework with Embedded Filters and a DCT-based Temporal Module
Hangting Chen, Pengyuan Zhang, Yonghong Yan · 2019
Deep convolutional neural network (DCNN) has recently improved the performance of acoustic scene classification. However, the input features of the network are usually based on predefined hand-tailored filters, which may not apply to the specific tasks. To overcome this, we propose a hybrid framework that jointly trains the front-end filters and the back-end DCNN. Also, a novel temporal module based on the discrete cosine transform (DCT) is inserted after the high-level feature map of the network, thus enabling us to utilize time information without a reduction of training samples. Our single system, composed of the fine-tuned wavelet front-end and the DCNN back-end, with the integrated DCT-based temporal module, has achieved an accuracy of 79.20% in the evaluation set in DCASE17, gaining around 3% and 8% accuracy improvement compared with scalogram-DCNN and FBank-DCNN systems, respectively.