Transformer Audio Noise Reduction Recognition Network Based on Conv-TasNet
In order to reduce the impact of environmental noise on transformer voiceprint recognition,a transformer audio denoising recognition network based on convolutional time-domain audio separation network was proposed.Firstly,a convolutional time-domain audio separation network was used to remove environmental noise,and then a convolutional neural network was applied to achieve voiceprint recognition.A transformer audio dataset was obtained through fault simulation experiments and the denoising effect was then compared with other denoising methods.The experimental results show that the proposed method improves the scale invariant signal-to-noise ratio of the dataset audio by 9.84 dB and updates the recognition accuracy by 25.85％,both of which are superior to other denoising methods.In on-site application,the proposed denoising recognition network reduced the false alarm rate to 1.2％and successfully achieved transformer fault detection.
万方数据
维普
