Note Detection in Music Teaching Based on Intelligent Bidirectional Recurrent Neural Network

Abstract

Music education is an essential and significant link in a quality education, as it can assist pupils improve their integrity and nurture noble character. The evident distinction between music teaching and teaching in other disciplines is that music teaching can provide aesthetic education to students in order to improve students' self-cultivation and overall temperament and basically play a role in developing people in a holistic fashion. Note detection is an important content in music teaching. Instrument tuning, computerized score recognition, music database search, and electronic music synthesis all benefit greatly from note detection technologies. In note detection, there are problems such as difficult one-to-one correspondence between estimated pitches and standard frequencies, a narrow range of identifiable pitches, poor robustness of the recognition process, and low recognition rate. In this context, this work proposes an automatic note detection in music teaching based on deep learning. It uses a convolutional neural network (CNN) and a bidirectional long-short-term memory (BiLSTM) network to build a deep neural network model, called convolutional neural network Bidirectional Long Short-Term Memory (CNN-BiLSTM), using this network to conduct in-depth research on note detection. First, based on the current research status, a deep neural network model based on CNN and BiLSTM is proposed to detect musical notes. The network can independently mine and learn the deep-level features of music signals and has better feature extraction and generalization capabilities. Second, the experimental results are evaluated using different evaluation metrics. Experiments show the network model can significantly improve detection accuracy and can efficiently detect notes in music teaching.