Xu, Y and Afshar, S and Wang, R and Cohen, G and Thakur, CS and Hamilton, TJ and Van Schaik, A (2021) A biologically inspired sound localisation system using a silicon cochlea pair. In: Applied Sciences (Switzerland), 11 (4). pp. 1-21.
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Abstract
We present a biologically inspired sound localisation system for reverberant environments using the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear model. The system exploits a CAR-FAC pair to pre-process binaural signals that travel through the inherent delay line of the cascade structures, as each filter acts as a delay unit. Following the filtering, each cochlear channel is cross-correlated with all the channels of the other cochlea using a quantised instantaneous correlation function to form a 2-D instantaneous correlation matrix (correlogram). The correlogram contains both interaural time difference and spectral information. The generated correlograms are analysed using a regression neural network for localisation. We investigate the effect of the CAR-FAC nonlinearity on the system performance by comparing it with a CAR only version. To verify that the CAR/CAR-FAC and the quantised instantaneous correlation provide a suitable basis with which to perform sound localisation tasks, a linear regression, an extreme learning machine, and a convolutional neural network are trained to learn the azimuthal angle of the sound source from the correlogram. The system is evaluated using speech data recorded in a reverberant environment. We compare the performance of the linear CAR and nonlinear CARFAC models with current sound localisation systems as well as with human performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
| Item Type: | Journal Article |
|---|---|
| Publication: | Applied Sciences (Switzerland) |
| Additional Information: | The copyright for this article belongs to Authors |
| Department/Centre: | Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) |
| Date Deposited: | 21 Dec 2021 05:25 |
| Last Modified: | 21 Dec 2021 05:25 |
| URI: | http://eprints.iisc.ac.in/id/eprint/68196 |
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