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Section: New Results
Audio-Source Localization
In previous years we have developed several supervised sound-source localization algorithms. The general principle of these algorithms was based on the learning of a mapping (regression) between binaural feature vectors and source locations [5], [7]. 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 released 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. During the period 2015-2016 we extended this method to an arbitrary number of microphones based on the relative transfer function – RTF (between any channel and a reference channel). Then we extended this work and developed a novel transfer function that contains the direct path between the source and the microphone array, namely the direct-path relative transfer function [29], [36].
Websites:
https://team.inria.fr/perception/research/acoustic-learning/