Vocal Music Recognition Based on Deep Convolution Neural Network

In order to achieve fast and accurate music technique recognition and enhancement for vocal music teaching, the paper proposed a music recognition method based on a combination of migration learning and CNN (convolutional neural network). Firstly, the most standard timbre vocal music is preprocessed by panning, flipping, rotating, and scaling and then manually classified by vocal technique features such as breathing method, articulation method, pronunciation method, and pitch region training. Then, based on the migration learning method, the weight parameters obtained from the convolutional model trained on the sound dataset CNN are migrated to the sound recognition, and the convolutional and pooling layers of the convolutional model are used as feature extraction layers, while the top layer is redesigned as a global average pooling layer and a Softmax output layer, and some of the convolutional layers are frozen during training. The experimental results show that the average test accuracy of the model is 86%, the training time is about 1/2 of the original model, and the model size is only 74.2 M. The F1 values of the model are 0.88, 0.80, 0.83, and 0.85 in four aspects, such as breathing method, exhaling method, articulation method, and phonetic region training, etc. The experimental results show that the method is efficient for voice and vocal music teaching recognition. The experimental results show that the method is efficient, effective, and transferable for voice and vocal music teaching research.