Unsupervised adaptation of deep neural networks for sound source localization using entropy minimization

This paper describes an unsupervised method of adapting deep neural networks (DNNs) for sound source localization (SSL). DNNs-based SSL achieves high localization accuracy for sound data that are similar to training data. However, the accuracy deteriorates if a sound source is at an unknown position in unknown reverberant environments. We solve the problem by using unsupervised adaption of the DNNs' parameters to the observed sound signals. Entropy is used as the objective function and minimized to optimize the parameters on the basis of the gradient method. Adaptation without overfitting is achieved by using 1) a parameter adaptation layer, such as linear transform network, and 2) early stopping of the parameter updates. Experimental results indicated that our method improved localization accuracy by a maximum of 20 points for unknown positions and reverberant data.