# A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair
## 1 Introduction
Another way of obtaining ITD cues was proposed by [PERSON] and [PERSON]. They implemented a neuromorphic sound localiser in Reference [22]. In the system, a delay between the positive zero-crossings of both ears was detected, and a pulse was generated with the width equal to the delay value. A voltage across a capacitor, proportional to the average pulse width, was obtained by integrating over a fixed number of pulses. Once a fixed number of pulses was counted, the capacitor was read and reset. To emulate the robustness of the human sound localisation performance in noisy environments, neural network algorithms are introduced to analyse the ITD cues from cochlear models. Implementations of such auditory \"where\" pathways have been proposed and developed increasingly [23, 24, 25, 26, 27]. For example, [PERSON] et al. [25] and [PERSON] et al. [26] used a competitive learning network with a pulsed neuron model (CONP) to learn the direction of a sound source. [PERSON] et al. [28] proposed to build a spike-based sound localisation system on Field Programmable Gate Array (FPGA). They used a Leaky Integrate-and-Fire (LIF) neuron model to generate spikes from a cochlear inner hair cell output, and a delay line to extract ITD cues from the spike streams. A Winner-take-all (WTA) network was then used to select the dominant sound source direction. [PERSON] et al. proposed a robotic sound localisation system using a WTA network to estimate the direction of a sound source through the ITD cues from a cochlea pair with an address event representation (AER) interface [27]. In recent years, deep neural networks (DNN) have provided more accurate estimations of sound source locations from binaural cues [29, 30, 31, 32]. For example, in [PERSON] et al. [32], simulated binaural signals were pre-processed with a Gammatone filter bank and used to train a DNN classifier for sound source localisation. Although some of those systems showed small RMS errors, they used either simulated signals or sinewaves, instead of natural sounds, such as speech signals, as the input signal. The robust performance of biologically inspired sound localisation systems for practical applications are yet to be tested and proved. In this work, we present a biologically inspired sound localisation system and evaluate its performance for a practical task: speech localisation, in a small office. We propose to use the CAR-FAC cochlear model to generate correlograms and a regression Extreme Learning Machine (ELM) to localise a sound source from the correlograms in Reference [33]. In the human binaural system, a mechanism called the precedence effect is thought to allow suppression of echoes to help localisation between a direct sound and a reflection. The precedence effect refers to the phenomena that we perceive the location of a sound source based on sound onset and ignore the localisation cues that follow from about 2 ms up to 40 ms [34; 35]. Inspired by this, we use an onset detection algorithm to generate the correlogram only during the signal onset to decrease the influence of echoes. We then proposed to use a regression convolutional neural network (CNN) for sound localisation in Reference [36]. In this work, we describe the system in detail and investigate the effect of the CAR-FAC nonlinearity on the system performance by comparing it with a CAR only version. The performance of the quantised correlograms is also compared with non-quantised correlograms. The implementation and evaluation of the system will be described in the next sections.