Unsupervised Adaptation of Neural Networks for Discriminative Sound Source Localization with Eliminative Constraint
Ryu Takeda, Yoshiki Kudo, Kazuki Takashima, Yoshifumi Kitamura, Kazunori Komatani · 2018
This paper describes an unsupervised adaptation method of deep neural networks (DNNs) regarding discriminative sound source localization (SSL). DNNs-based SSL and its unsupervised adaptation fail under different conditions from those during training. The estimations sometimes include incoherent unpredictable errors due to the NN's non-linearity. We propose an eliminative posterior probability constraint using a model-based SSL for unsupervised DNNs adaptation. This constraint forces the probability of “less possible candidates” to become zero to eliminate incoherent errors. The candidates are indicated by a model-based SSL method because it can estimate the azimuth of the sound source with moderate accuracy and explicit reasoning. As a result, the localization performance of adapted DNNs improved more than that of model-based SSL. Experimental results showed that our method improved localization correctness of 1D azimuth and 3D regions by a maximum of 13.3 and 5.9 points compared with the model-based SSL.