Fig. 1. Network structure of the proposed model.
Fig. 2. Confusion matrix for four-class classification of proposed model trained with clean data and tested with clean data (Table 3 clean train / clean test). Overall classification accuracy is 94.4 %.
Fig. 3. Confusion matrix for four-class classification of proposed model trained with noise data and tested with noise data with SNR 10 dB [Table 3 noise train / noise test (10 dB)]. Overall classification accuracy is 90.7 %.
Table 1. Data distribution of 4 classes. TS, CC, CH, and NS correspond to tire skidding, car crash, car horn, and normal sounds.
Table 2. Results of the two-class classification of the proposed model and baseline (the classification accuracy of normal sound is not reported in[2]).
Table 3. Classification results according to training and test data composition. TOT means overall classification accuracy.
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