Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization

Biomimetic Sound-Source Localization

Sound-source localization systems typically comprise free-field microphone arrays. In nature, directional acoustic sensing evolved to rely on diffraction about the head with only two ears. For localization, the brain uses the resultant frequency-dependent acoustic phase and intensity differences between the two ears. We conceive a biomimetic artificial head with microphones placed on its surfac...

A Binaural Sound Source Localization System for a Mobile Robot

Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation

This paper presents a spiking neural network (SNN) for binaural sound source localisation (SSL). The cues used for SSL were the interaural time (ITD) and level (ILD) differences. ITDs and ILDs were extracted with models of the medial superior olive (MSO) and the lateral superior olive (LSO). The MSO and LSO outputs were integrated in a model of the inferior colliculus (IC). The connection weigh...

Subband Selection for Binaural Speech Source Localization

We consider the task of speech source localization using binaural cues, namely interaural time and level difference (ITD & ILD). A typical approach is to process binaural speech using gammatone filters and calculate frame-level ITD and ILD in each subband. The ITD, ILD and their combination (ITLD) in each subband are statistically modelled using Gaussian mixture models for every direction durin...

Discriminative Binaural Sound Localization

Time difference of arrival (TDOA) is commonly used to estimate the azimuth of a source in a microphone array. The most common methods to estimate TDOA are based on finding extrema in generalized crosscorrelation waveforms. In this paper we apply microphone array techniques to a manikin head. By considering the entire cross-correlation waveform we achieve azimuth prediction accuracy that exceeds...