Abstract: Existing abnormal sound detection methods often rely on strongly labeled data for training, but high-quality strongly labeled audio data is difficult to annotate and costly to collect. Addressing the issues of poor training results and low accuracy caused by interference from non-stationary and time-varying noise when using weakly labeled data in current abnormal audio detection methods, a weighted non-negative matrix factorization (WNMF) method based on audio spectrum is proposed. This method utilizes WNMF to label weakly labeled and unlabeled data, and separates target sound events from background noise. Under appropriate weight values, WNMF alters the importance of audio information in different frequency bands during labeling to suppress noise and improve separation quality, approaching the effect of fully supervised model training. Then, a convolutional neural network is used to generate frame-level predictions and audio label predictions. Simulation experiments show that this method improves the accuracy by 4.8% compared to traditional NMF methods for processing weakly labeled data.

Key words: abnormal sound detection, weakly labeled and unlabeled data, weighted non-negative matrix factorization, convolutional neural networks