Using multi-stream hierarchical deep neural network to extract deep audio feature for acoustic event detection

Extraction of effective audio features from acoustic events definitely influences the performance of Acoustic Event Detection (AED) system, especially in adverse audio situations. In this study, we propose a framework for extracting Deep Audio Feature (DAF) using multi-stream hierarchical Deep Neural Network (DNN). The DAF outputted from the proposed framework fuses the potential complementary information of multiple input feature streams and thus could be more discriminative than those input features for AED. We take two input feature streams and the hierarchical DNNs with two stages as an example for showing the extraction of DAF. The effectiveness of different audio features for AED is evaluated on two audio corpora, i.e. BBC (British Broadcasting Corporation) audio dataset and TV audio dataset with different signal-to-noise ratios. Experimental results show that DAF outperforms other features for AED under several experimental conditions.