Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models
Table 3: Metric values of LCC, SRCC, and F1 (larger is better) obtained on the test data corrupted by noise using the baseline method and two proposed approaches.
['[BOLD] Method', '[BOLD] LCC', '[BOLD] SRCC', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
The results show that the use of our approaches can yield better performance than the baseline method. Since various natural sounds from MUSAN [musan15] were used as noise to mix with clean signals, these noise signals might take place at different time and have various effects in frequency domain. To mitigate possible bias on LCC, SRCC, and F1 values, the experiments were repeated for eight times and the average values are used as the final results. To further compare the structure using an attention model and not using, the third approach L_1DCNN was also conducted. It has the same structure as L_ATT, but without the attention layer.