基于频谱感知音频去噪的无监督机器异常声音检测

In industrial automation production,it is a common and effective method to judge whether the machine is operating normally through sound monitoring.Aiming at the misjudgement caused by the normal changes of the machine's operating status and the problem of a large number of background noise interference to monitoring in the actual production environment,an unsupervised abnormal sound detection method based on spectrum sensing audio denoising is proposed.First,the open source audio set is used to train a denoising system based on spectrum sensing.After the noise-containing audio is transformed into the frequency domain,the statistical characteristics of the noise spectrum are sensed in the frequency domain and the spectrum is modified to form an enhanced spectrum,then converted back to the time domain output denoised audio.The Log-Mel spectrum is selected as the audio feature,then the provided development set information is used to train a classifier based on the depth separable convolution and inverse residual structure,and the classification prediction value is calculated for each frame of the audio,then the average negative logarithm of its audio anomaly score to determine anomaly threshold,and the anomaly detection is performed by comparing with the anomaly threshold.The experimental results on the DCASE2021 Challenge Task2 data set show that the detection performance of the anomaly detection system that performs denoising preprocessing is improved compared with the baseline system.