Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching

Abstract

As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition accuracy and recognition efficiency. There are few research studies on music recognition based on deep neural networks. This paper expounds on the basic principles of deep learning and the basic structure and training methods of neural networks. For two kinds of commonly used deep networks, convolutional neural network and recurrent neural network, their typical structures, training methods, advantages, and disadvantages are analyzed. At the same time, a variety of platforms and tools for training deep neural networks are introduced, and their advantages and disadvantages are compared. TensorFlow and Keras frameworks are selected from them, and the practice related to neural network research is carried out. Training lays the foundation. Results show that through the development and experimental demonstration of the prototype system, as well as the comparison with other researchers in the field of humming recognition, it is proved that the deep-learning method can be applied to the humming recognition problem, which can effectively improve the accuracy of humming recognition and improve the recognition time. A convolutional recurrent neural network is designed and implemented, combining the local feature extraction of the convolutional layer and the ability of the recurrent layer to summarize the sequence features, to learn the features of the humming signal, so as to obtain audio features with a higher degree of abstraction and complexity and improve the performance of the humming signal. The ability of neural networks to learn the features of audio signals lays the foundation for an efficient and accurate humming recognition process.