Acoustic Scene Classification on Large Dataset Using Sparse Auto-encoder Based Deep Neural Network

Abstract

Abstract-In this paper we study the acoustic scene classification using a large dataset. The spectrogram of the large acoustic samples are extracted and applied with texture feature classification method. First, the acoustic scene database is built including various acoustic events. Second the image texture features on spectrogram are used to represent the acoustic samples. Third the auto-encoder is adopted to build a deep neural network classifier. Finally, we verified the proposed system on a large number of dataset and compared our results with traditional Gaussian mixture model and three-layer neural network. The experimental results show that the proposed method is effective and promising in big acoustic data classification.