Signal Feature Extraction of Music Melody Based on Deep Learning

Abstract

In music composition, besides intended original orchestration, the main melody generally has a higher reproduction frequency. To better understand the content and idea of music works, this paper researches a novel method for extracting the features of music melody signals based on deep learning. At first, a supervised classification model is employed to select better features extracted from the raw data of music melody signals and create an optimal melody feature subset; then, the Temporal Convolution Network (TCN) is introduced to propose a new algorithm for detecting feature points of melody signals, and the detection principles are introduced in detail; after that, this paper elaborates on the melody signal feature point detection model built based on multi-branch and multi-task TCN, and gives the structures and work principles of the encoding module, decoding module, and mask estimation module of the TCN. At last, experimental results verify the validity of the proposed model.