Discriminative multiple sound source localization based on deep neural networks using independent location model
We propose a training method for multiple sound source localization (SSL) based on deep neural networks (DNNs) Such networks function as posterior probability estimator of sound location in terms of position labels and achieve high localization correctness. Since the previous DNNs' configuration for SSL handles one sound source cases, it should be extended to multiple sound source cases to apply it to real environments. However, a naive design causes 1) an increase in the number of labels and training data patterns and 2) a lack of label consistency across different numbers of sound sources, such as one and two or more sound cases. These two problems were solved using our proposed method, which involves an independent location model for the former and an block wise consistent labeling with ordering for the latter. Our experiments indicated that the SSL based on DNNs trained by our proposed training method out performed a conventional SSL method by a maximum of 18 points in terms of block level correctness.
2016 IEEE Spoken Language Technology Workshop (SLT)