Automatic Synthesis Technology of Music Teaching Melodies Based on Recurrent Neural Network

Yingxue Zhang,Zhe Li,Ahmed Farouk

doi:10.1155/2021/1704995

Abstract

Computer music creation boasts broad application prospects. It generally relies on artificial intelligence (AI) and machine learning (ML) to generate the music score that matches the original mono-symbol score model or memorize/recognize the rhythms and beats of the music. However, there are very few music melody synthesis models based on artificial neural networks (ANNs). Some ANN-based models cannot adapt to the transposition invariance of original rhythm training set. To overcome the defect, this paper tries to develop an automatic synthesis technology of music teaching melodies based on recurrent neural network (RNN). Firstly, a strategy was proposed to extract the acoustic features from music melody. Next, the sequence-sequence model was adopted to synthetize general music melodies. After that, an RNN was established to synthetize music melody with singing melody, such as to find the suitable singing segments for the music melody in teaching scenario. The RNN can synthetize music melody with a short delay solely based on static acoustic features, eliminating the need for dynamic features. The proposed model was proved valid through experiments.

Highlights

With the rapid development of modern computer science, many researchers have shifted their focus to computer-based algorithm composition or automatic music melody generation system. e research results on music melody synthesis and music modeling methods are being applied to various fields. e research of computer music creation aims to quantify and combine the emotional tendencies of music, with the aid of computer and mathematical algorithms. e specific tasks include aided composition, sound simulation and storage, and music analysis and creation [1, 2]
Computer music creation generally relies on artificial intelligence (AI) and machine learning (ML) to generate the music score that matches the original mono-symbol score model or memorize/recognize the rhythms and beats of the music
There are very few music melody synthesis models based on artificial neural networks (ANNs)

Summary

Introduction

With the rapid development of modern computer science, many researchers have shifted their focus to computer-based algorithm composition or automatic music melody generation system. e research results on music melody synthesis and music modeling methods are being applied to various fields. e research of computer music creation aims to quantify and combine the emotional tendencies of music, with the aid of computer and mathematical algorithms. e specific tasks include aided composition, sound simulation and storage, and music analysis and creation [1, 2]. With the rapid development of modern computer science, many researchers have shifted their focus to computer-based algorithm composition or automatic music melody generation system. There are very few music melody synthesis models based on artificial neural networks (ANNs). This paper attempts to develop an automatic synthesis technology of music teaching melodies based on recurrent neural network (RNN). 2. Acoustic Feature Extraction e automatic synthesis of music melody aims to obtain a melody that is beautiful and pleasant to human ears. Mel scale was adopted to extract acoustic features in our music melody synthesis system. It is difficult to extract the features from time-domain music melody signal. En, the fast Fourier transform of the M points of the windowed framed time-domain music melody signal CH∗(m) can be expressed as CH(l). L μmin e mel spectrum can be extracted by taking the logarithm of on

Sequence-Sequence Model-Based Music Melody Synthesis

RNN-Based Melody Synthesis

Experiments and Result Analysis

Conclusions