# A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair
## 3 Experiment and Evaluation
### Results and Comparisons
Figure 9 shows the performance of the tests, and the mean and the standard deviation of the results at the 13 locations of the quantised and non-quantised correlograms generated by the CAR and CAR-FAC models. For the ELM, the hidden layer size is set as ten times the input size or 11,760, which is typical for such networks, and the _tanh_ function is used as the nonlinear activation function of the hidden neurons. For the CNN, in the first convolutional layer, the filter number is set to 16, and the convolutional size is set to 19 \(\times\) 9. In the second convolutional layer, the filter number is set to 32, and the convolutional size is set to 5 \(\times\) 5. The all-to-all connection layer neuron number is set to 5120. The dropout is set as 0.5 to avoid overfitting. These CNN parameters are set empirically after investigating different configurations and selecting the parameters that result in the best regression performance. The linear regression shows a large variation of the test localisations around the true locations, the ELM shows improved performance over linear regression, and the CNN shows the closest match to the true locations. Table 2 lists the standard deviation at each location for each case. We can see an increased variance at locations of large azimuthal angles for all the results, especially for the CNN, the largest standard deviation occurs at locations \(-90^{\circ}\) and \(90^{\circ}\). As the change of the ITD at large azimuthal angles, e.g., from \(-90^{\circ}\) to \(-75^{\circ}\), is much smaller than the changes at small azimuthal angles around \(0^{\circ}\), the results thus tend to show larger errors in localising the sound source at large azimuthal angles. From the CNN results, we can see the only significant difference is at \(-90^{\circ}\). The non-quantised correlograms generated by both the CAR and CAR-FAC models show much smaller standard deviations at \(-90^{\circ}\) than the quantised correlograms, as shown in Table 2. This is likely due to the asymmetric noise and echo interference caused by the asymmetric office layout. Additionally, both the non-quantised and quantised correlograms generated by the CAR show slightly smaller averaged unsigned errors than the CAR-FAC, as shown in Table 2.  
Secondly, we test correlograms from different CF ranges using the regression CNN. In addition to the diagonal correlogram, a 2 \(\times\) 2 max-pooling is also used to down-sample the input full correlogram (70 \(\times\) 70) into size 35 \(\times\) 35. The RMS errors in the 0-45\({}^{\circ}\)/45-90\({}^{\circ}\) ranges of the quantised and non-quantised correlograms generated by both the CAR and CAR-FAC models are listed in Table 3. The diagonal approach tends to provide smaller 0-45\({}^{\circ}\)/45-90\({}^{\circ}\) RMS errors for the 45 Hz to 8 kHz frequency range when the non-quantised correlograms generated by both the CAR and CAR-FAC models are used. Both the max-pooling and diagonal approaches show smaller 45-90\({}^{\circ}\) RMS errors for the 500 Hz to 8 kHz frequency range than the 45 Hz to 8 kHz range when the quantised correlograms generated by either the CAR or CAR-FAC models are used. Although the 500 Hz to 8 kHz CF range correlograms show clearer patterns in Figure 8A,B than the 40 Hz to 8 kHz range, the results from Table 3 did not show significant differences in the RMS errors between them. The CNN is able to extract essential features from both of the correlograms for localising a sound source. Figure 9: Comparisons of the linear regression (**top**), ELM regression (**middle**), and CNN regression (**bottom**) on the experimental data. The true location is shown in blue square, and the testing data result is shown in red round. Figure 10 shows the CNN results of the quantised correlograms generated by the CAR model. With proper settings of cochlear CF ranges and pooling approaches, the quantised correlograms show reduced standard deviations at large azimuthal angles and excellent matches to the true sound source locations.