Cross-acoustic transfer learning for sound event classification

Abstract

A well-trained acoustic model that effectively captures the characteristics of sound events is a critical factor to develop more reliable system for sound event classification. Deep neural network (DNN) which has an ability to extract discriminative representation of features can be a good candidate for acoustic model of sound events. Compared to other data such as speech or image, the amount of sound database is often insufficient for learning the DNN properly, resulting in overfitting problems. In this paper, we propose a cross-acoustic transfer learning framework that can effectively train the DNN even with insufficient sound data by employing rich speech data. Three datasets are used to evaluate our proposed method; one sound dataset is from Real World Computing Partnership (RWCP) DB and two speech datasets are from Resource Management (RM) and Wall Street Journal (WSJ) DBs. A series of experimental results verify that cross-acoustic transfer learning performs significantly better than the baseline DNN which was trained only from sound data, achieving 26.24% relative classification error rate (CER) improvement over the DNN baseline system.