Previous | 
Home
Section: New Results
Supervised Audio-Source Localization
We addressed the problem of localizing audio sources using binaural measurements. After proposing an unsupervised method [20] , we proposed a supervised formulation that simultaneously localizes multiple sources at different locations [22] . The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression [21] between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods. More recently the method has been extended to an arbitrary number of microphones [35] , [34] . Moreover, we have started to develop a method that extracts the direct path on an acoustic wave in order to enable robust audio-source localization in reverberant environments [40] .
Websites:
https://team.inria.fr/perception/research/acoustic-learning/