Deep neural network bottleneck features for acoustic event recognition

Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.