Neural Beamformer with Automatic Detection of Notable Sounds for Acoustic Scene Classification

Recently, acoustic scene classification using an acoustic beamformer that is applied to a multichannel input signal has been proposed. Generally, prior information such as the direction of arrival of a target sound is necessary to generate a spatial filter for beamforming. However, it is not clear which sound is notable (i.e., useful for classification) in each individual sound scene and thus in which direction the target sound is located. It is therefore difficult to simply apply a beamformer for preprocessing. To solve this problem, we propose a method using a neural beamformer composed of the neural networks of a spatial filter generator and a classifier, which are optimized in an end-to-end manner. The aim of the proposed method is to automatically find a notable sound in each individual sound scene and generate a spatial filter to emphasize that notable sound, without requiring any prior information such as the direction of arrival and the reference signal of the target sound in both training and testing. The loss functions used in the proposed method are of four types: one is for classification and the remaining loss functions are for beamforming that help in obtaining a clear directivity pattern. To evaluate the performance of the proposed method, we conducted an experiment on classifying two scenes: one is a scene where a male is speaking under noise and another is a scene where a female is speaking under noise. The experimental results showed that the segmental SNR averaged over all the test data was improved by 10.7 dB. This indicates that the proposed method could successfully find speech as a notable sound in this classification task and generate the spatial filter to emphasize it.