Data Augmentation Based on Contrastive Learning in Audio Anomaly Detection

Based on the contrastive supervised learning algorithm, this paper studies the impact of the linear combination of data augmentation in audio anomaly detection. Previous studies have shown that the linear combination of data enhancement performs well in image detection. However, there are few studies on similar methods in sound anomaly detection, and this study makes up for this gap. We apply transformations to one branch of the algorithm with the audio augmentation module of the AugLy library, and the other branch is directly transformed into the Mel spectrogram. We find that more data augmentation patterns can help neural networks learn more high-quality representations, and a suitable linear data combination augmentation can improve the performance of model generalization at a low cost. The combination effect of Add Background Noise + Pitch Shift is the best. The AUC index of the fan dataset is increased by 10.06% on average, and other datasets are also improved to varying degrees.