Feature selection based on Multiple Kernel Learning for single-channel sound source localization using the acoustic transfer function

This paper presents a sound source (talker) localization method using only a single microphone. In our previous work [1], we discussed the single-channel sound source localization method, where the acoustic transfer function from a user´s position is estimated by using a Hidden Markov Model (HMM) of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly selected in order to select the cepstral dimensions having information that is useful for classifying the user´s position. Then, we propose a feature selection method for the cepstral parameter using Multiple Kernel Learning (MKL) to define the base kernels for each cepstral dimension (scalar) of the acoustic transfer function. The user´s position is trained and classified by Support Vector Machine (SVM). The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in a room environment.