Binaural sound localization for untrained directions based on a Gaussian mixture model

We propose and evaluate estimation methods for all sound source directions on the horizontal plane based on a Gaussian mixture model (GMM) using binaural signals. An estimation method based on GMM can estimate a sound source direction on which GMM has already been trained; however, it cannot estimate without a model that corresponds to a sound source direction. Three methods with interpolation techniques are investigated. Two generate GMMs for all directions by interpolating an acoustic transfer function or statistical values of GMM, and the other calculates the posterior probability for all directions with a limited number of GMMs. In our experiments, we investigated six interval conditions. From the results, the interpolation methods of an acoustic transfer function and the statistical values of GMM achieve better performance. Although there were 12 trained GMMs for the 30° intervals, the interpolation method of the statistical values of GMMestimated 62.5%accuracy (45/72) with 2.8° estimation error. These results indicate that the proposed method can estimate all sound source directions with a small amount of known information.