2D sound-source localization on the binaural manifold

Abstract

The problem of 2D sound-source localization based on a robotic binaural setup and audio-motor learning is addressed. We first introduce a methodology to experimentally verify the existence of a locally-linear bijective mapping between sound-source positions and high-dimensional interaural data, using manifold learning. Based on this local linearity assumption, we propose an novel method, namely probabilistic piecewise affine regression, that learns the localization-tointeraural mapping and its inverse. We show that our method outperforms two state-of-the art mapping methods, and allows to achieve accurate 2D localization of natural sounds from real world binaural recordings.

Variational EM for binaural sound-source separation and localization

“…This learning stage may be viewed as a system calibration task. Then, accurate 2D localization of a single sound source may be inferred from the inverse posterior distribution of the PPAM model [2]. This paper generalizes single-source localization [2] to SSL, e.g., the perceived binaural signals are generated from multiple sources with unknown azimuth and elevation.…”

“…Then, accurate 2D localization of a single sound source may be inferred from the inverse posterior distribution of the PPAM model [2]. This paper generalizes single-source localization [2] to SSL, e.g., the perceived binaural signals are generated from multiple sources with unknown azimuth and elevation. As in [2] the PPAM model is inferred from a training data set of input-output variable pairs, where the input is the known 2D location of a white-noise emitter and the output is the perceived ILD spectrogram.…”

“…Recently it was suggested that the ILD (interaural level difference) spectrogram carries information about the relationship between the binaural observation space and the two-dimensional (2D) localization space (azimuth and elevation) and that the latter can be retrieved via an unsupervised manifold learning method [1,2]. Within this framework, the general SSL problem is more challenging for several reasons.…”

“…Indeed, the computational experiments reported in [1,2] suggest the existence of a locally-linear bijection from the space of source locations to the space of binaural cues, and that the high-dimensional space spanned by the latter forms a low-dimensional manifold embedded in the former (source locations). In practice, the source-location-to-binaural-cue mapping can be approximated by a probabilistic piecewise affine mapping (PPAM) model whose parameters are learned via an EM procedure.…”

“…This paper generalizes single-source localization [2] to SSL, e.g., the perceived binaural signals are generated from multiple sources with unknown azimuth and elevation. As in [2] the PPAM model is inferred from a training data set of input-output variable pairs, where the input is the known 2D location of a white-noise emitter and the output is the perceived ILD spectrogram. The proposed runtime algorithm estimates separation and localization in the presence of multiple sparse-spectrum sounds.…”