# A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair
## 3 Experiment and Evaluation
### Results and Comparisons  
In the CAR-FAC implementation, the total number of channels and the CF range are reconfigurable. Machine hearing models typically use 60 to 100 channels in total [62]. Here, we keep the cochlear channels to 70 and investigate different CF frequency ranges. We  
Figure 7: (**A**) Experiment setup in a reverberant environment (an office); (**B**) Onset detection; the log-energy change \(\Delta E\) of the spoken digit ‘six’ (blue) is shown in red and green, and the circles mark the detected onset time for each input of the binaural signal. The first onset time \(t\) is selected to be the onset time. Adapted from Reference [33]. limit the upper CF to 8 kHz since the sampling frequency of the original Austalk database is 16 kHz. We first set the lowest CF to 45 Hz, which is close to the lower frequency limit of human hearing. Figure 8A shows generated correlograms at different azimuthal angles. Similar to Figure 6F at zero-delay, it shows strong diagonal correlation and off-diagonal correlation and anticorrelation patterns which correspond to formants of the input speech at 0\({}^{\circ}\). Note that the environmental noise and echoes have resulted in non-symmetric off-diagonal patterns in Figure 6A at zero-delay. When the input speech is played from different azimuthal angles, the generated correlogram shows different patterns that encode different ITD cues. Additionally, the low-frequency channels (bottom-right region) of all the correlograms show blurred off-diagonal patterns in Figure 8A. This is because the input speech does not contain such low-frequency components, so that there is no significant response in these low-frequency channels, and the correlogram in these regions does not encode much information. As we then increase the lowest CF to 500 Hz, to \"cut-off\" the very low CF channels, the correlograms in these regions show noticeable correlation and anticorrelation stripes in Figure 8B. Since there are more channels above 500 Hz range in the 500 Hz-8 kHz setup, the whole correlogram is clearer than the 45 Hz setup. Figure 8: Onset correlogram generated from speech “eight”. (**A**) CAR-FAC filter CF frequency range is from 45 Hz to 8 kHz; (**B**) CAR-FAC filter CF frequency range is from 500 Hz to 8 kHz. Five azimuthal angles (\(-90^{\circ}\), \(-45^{\circ}\), \(0^{\circ}\), \(75^{\circ}\), and \(90^{\circ}\)) are shown here, and correlogram generated from a binaural white noise with 1.17 ms ITD (Bottom left) and (Bottom right) used as the input of the CNN. 14 channels on each side of the diagonal of the correlogram are selected, and the top left and bottom right of the correlogram are discarded to form a rectangular input to the CNN. (**C**) Diagonal correlogram of speech “eight” and (**D**) “zero” at location \(-90^{\circ}\), \(-45^{\circ}\), \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\). The generated 2-D 70 \(\times\) 70 correlograms (\(\Delta t\) = 90 ms) are transformed to have zero mean and fed into different regression neural networks for localisation. The dataset is divided into a training, validation, and testing set. We use the samples from four random speakers as the training data (9324) and the samples from the fifth speaker as the validation (840) and testing data (1540). Firstly, we use the correlogram generated with the 45 Hz-8 kHz CF range as the input to the neural networks. Inspired by the white noise correlogram in Figure 6D, we select only the diagonal region to reduce the input dimension to the neural network. The diagonal region should encode sufficient ITD cues for the localisation. Furthermore, in this experimental setup, the maximum ITD is 1.17 ms, as explained in the Experiment Setup section. For a binaural white noise signal with a 1.17 ms ITD, the generated correlogram shows a clear pattern in around 14 channels on each side of the diagonal. We, therefore, use 14 channels on each side of the diagonal, which is 42 \(\times\) 28 diagonal correlogram as the input to the CNN, as shown in Figure 8C,D.