Support Vector Machine-based Automatic Music Transcription for Transcribing Polyphonic Music into MusicXML

被引：0

作者：

Fathurahman, Krisna ^{[1
]}

Lestari, Dessi Puji ^{[1
]}

机构：

[1] Inst Teknol Bandung, Sch Elect Engn & Informat, Informat Comp Sci, Bandung, Indonesia

来源：

5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS 2015 | 2015年

关键词：

music transcription; polyphonic music; mel's frequency cepstral coefficient; support vector machine; musicxml;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Automatic Music Transcription (AMT) which transcribes music into music sheet is a challenging task since it requires combination of three different knowledges: signal processing, machine learning, and musical model. The task is more challenging when AMT applied to the polyphonic music. Such task required the system to recognize the pitch, timbre, tempo, onset, and expression into a readable music sheet. This paper describes our works in building such system. In this research, the most promising and prominent approach is applied. Those are the Mel's Frequency Cepstral Coefficient (MFCC) as the features and the One-against-all Support Vector Machine (SVM) as its decoder. The combination of both methods had shown very promising results.

引用

页码：535 / 539

页数：5