## 3 Results
### Parameter Search for LSTM and TCN  
As shown in Figure 11, we tested the features of 80-bin 1D-CNN, 41-bin STFT, 4-bin envelopes, and the 45-bin combination of STFT and envelopes (STFT+envelopes) based on two databases. The cells of one layer shows the results obtained with different numbers of cells in one LSTM layer net and the layers with 200 cells/layer shows the results produced with different numbers of LSTM layers with 200 cells/layer. In order to balance the accuracy and the quantity of the calculations, we decided the parameters for the LSTM network as two layers with 200 cells/layer. In addition, we found that STFT and STFT+envelopes yielded almost the same performance, thereby indicating that envelopes had little effect on increasing the information to STFT. In addition, the hardware implementation scheme for the discrete Fourier transform was sufficiently mature [30], so we employed STFT for comparison in the subsequent experiments, which also enhanced the possibility of a hardware implementation of our algorithm. Moreover, the accuracy of one layer of 1D-CNN was intermediate between that of STFT and envelopes according to the experiments, and the performance of 1D-CNN was significantly weaker than that of STFT.  
As shown in Figure 12, we performed parameter search based on the filter numbers and dilation structures for the TCN network, where the kernel size was fixed at two and the stack number was fixed at one. We found that the performance of features was similar to LSTM, so STFT was also selected for  
Figure 11: Accuracy of long short-term memory (LSTM) classification results. (**a**) PN-training-a. (**b**) LH-training.  
comparing the subsequent result. The filter number was selected as 60 and the dilation structure as (1, 2, 4, 8, 16, 32) to ensure that the accuracy and the quantity of the calculations were balanced.  
In order to search for the kernel size and stack number, we first fixed the dilation structure as (1, 2, 4, 8, 16, 32) and the number of filters as 60. Figure 13a shows the results obtained with different kernel sizes based on the two databases when the stack number was one, and Figure 13b shows the results with different stack numbers based on the two databases with a kernel size of two. Hence, the other TCN parameters in the subsequent experiments were determined with a kernel size and stack number of two and one, which resulted in the minimum amount of calculations and high performance. Clearly, this type of parameter search approach is incomplete, so we also tried other parameter combinations but the results did not significantly exceed those with our selected values, so the choice of these parameters was sufficiently economic considering the low number of calculations and the requirement for hardware implementation.  
Figure 12: Visualization of the accuracy of TCN with different filter numbers and dilation structures. (**a**) PN-training-a. (**b**) LH-training. The ordinate denotes the different number of filters and abscissa represents the dilation structure. For example, abscissa number 16 indicates that the dilation structure is (1, 2, 4, 8, 16), and the meanings of other abscissa numbers are similar.  
Table 2 shows the classification accuracy, parameter numbers, and floating point of operations (FLOPs) for the two LSTM layers with 200 cells/layer and the TCN network with 60 filters, one stack, (1, 2, 4, 8, 16, 32) as the dilation structure and a kernel size of two based on the testing sets. The parameter numbers and FLOPs were obtained using the function provided with TensorFlow. When the performance of TCN was similar to that of LSTM based on LH-training, which is a commonly used data set, the number of parameters and multiply-add calculations were significantly lower with TCN than LSTM.