Audio Sound Determination Using Feature Space Attention Based Convolution Recurrent Neural Network

The classification framework has been popularly adopted to perform sound event detection. However, the existing neural network based classification based approaches treat each feature dimension equally and the varying influence of feature dimensions has not been taken into consideration. To deal with this, we propose a feature space attention based convolution recurrent neural network approach utilizing the varying importance of each feature dimension to perform acoustic event detection. The convolution layers are used to extract the high level information from the audio signals. Then the feature space attention scheme is applied to the extracted features to automatically determine the importance of each feature dimension. Experimental results on the latest TUT Sound Event 2017 dataset demonstrate the improved performance of the proposed approach compared to the existing acoustic event detection systems.