A Sensorimotor Approach to Sound Localization

Abstract

Sound localization is known to be a complex phenomenon, combining multisensory information processing, experience-dependent plasticity and movement. Here we present a sensorimotor model that addresses the question of how an organism could learn to localize sound sources without any a priori neural representation of its head related transfer function (HRTF) or prior experience with auditory spatial information. We demonstrate quantitatively that the experience of the sensory consequences of its voluntary motor actions allows an organism to learn the spatial location of any sound source. Using examples from humans and echolocating bats, our model shows that a naive organism can learn the auditory space based solely on acoustic inputs and their relation to motor states.

Relearning Auditory Spectral Cues for Locations Inside and Outside the Visual Field

“…6). From a computational perspective, some recent models have been developed whereby auditory spatial representations can be created by unsupervised sensorimotor learning where receiver motion is integrated with acoustic information (Aytekin et al 2008;Bernard et al 2012). Whether the motion signal is of motor (efference copy) or sensory origin (proprioception) is not known.…”

Variational EM for binaural sound-source separation and localization

“…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.…”

Sensorimotor learning of sound localization from an auditory evoked behavior

“…Thus, it has been showed that such a naive agent can recover the dimensionality of physical space without any a priori knowledge [12], [13], [14]. Once the space dimensionality is known, the sensorimotor approach can also be applied to the learning of sensory space parametrization from a set of sensorimotor experiences [12], [15], [16], giving rise to spatial perception. The basic assumption is that the sensory space of the agent lies on a low-dimensional manifold whose topology is homeomorphic to the topology of the embodying space.…”

“…Following this hypothesis, the learning of spatial perception becomes the learning of such a manifold. Methods based on manifold learning have been proposed for auditory localization using supervised linear regression [17], selforganized maps [18] and within the sensorimotor approach using local tangent space alignment [15], [16].…”