SUBBAND DEPENDENCY MODELING FOR SOUND EVENT DETECTION

In the domain of sound event detection (SED), Convolutional Recurrent Neural Network (CRNN) has become the most successful architecture, which adopts Recurrent Neural Network (RNN) to model temporal dependencies from the output of Convolutional Neural Network (CNN). However, CRNN does not fully use the subband dependencies that have been proved critical for human perception of sound events. In this paper, we propose a subband dependency model (SDM) to enhance the capability of CRNN in modeling subband dependencies from the input spectrogram. To select prominent subband dependencies, we propose a novel SoftSparsemax transformation. It can select the salient parts by comparing all dependencies and further strengthen them by projecting them onto a probability simplex. Furthermore, since subband dependencies of different sound events may be prominent in different timescales, multi-timescale subband dependency is considered. The experiment results demonstrate the effectiveness of our method.