Singing detection method based on multi-scale time-frequency graph parallel input convolutional neural network
A technology of convolutional neural network and time-frequency graph, which is applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems such as the effect needs to be improved, the computing resources are large, and the training time is long, so as to improve the detection accuracy , improve the overall performance, improve the effect of the overall performance
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[0069] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to the directions in the drawings, and the words "inner" and "outer" "Refer to a direction toward or away from the geometric center of a particular part, respectively.
[0070] As a specific embodiment of the present invention, the present invention provides a singing voice detection method based on multi-scale time-frequency graph parallel input convolutional neural network, the specific steps are as follows:
[0071] Step 1: Perform short-time Fourier transform on a single music file through different window lengths w i , i∈[1..n], get time-...
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