Sage Journals: Discover world-class research

Abstract

Music recognition is an interdisciplinary field, in the field of music retrieval and automatic music has very important application value in technology. In order to study the improvement method of music recognition for piano music, this paper compared the characteristics of music signals and speech signals around music related theories, discussed the selection of dimension of feature vectors, and used RBF neural network to identify 88 monosyllabic pianos. At the same time, the characteristics and calculation methods of the sound level contour with high frequency in western music and chord recognition were studied, and the specific formulas were given. The final study shows that: The improved method gives intermediate weights more inclined note nest, which has a higher accuracy than the traditional method and fault tolerance.

Keywords

Music recognition RBF neural network base frequency extraction of music

Get full access to this article

View all access options for this article.

References

Rebelo ,

Fujinaga ,

Paszkiewicz , et al., Optical music recognition: State-of-the-art and open issues, International Journal of Multimedia Information Retrieval1(3) (2012), 173–190.

M.P.

Ryynänen and

A.P.

Klapuri , Automatic transcription of melody, bass line, and chords in polyphonic music, Computer Music Journal32(3) (2008), 72–86.

Benetos ,

Dixon ,

Giannoulis , et al., Automatic music transcription: Challenges and future directions, Journal of Intelligent Information Systems41(3) (2013), 407–434.

Xu ,

N.C.

Maddage and

Shao , Automatic music Classification and summarization, IEEE transactions on speech and audio processing13(3) (2005), 441–450.

M.S.

Lew ,

Sebe ,

Djeraba , et al., Content-based multimedia information retrieval: State of the art and challenges, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)2(1) (2006), 19.

Muller ,

D.P.W.

Ellis ,

Klapuri , et al., Signal processing for music analysis, IEEE Journal of Selected Topics in signal Processing5(6) (2012), 1088–1110.

Fujihara ,

Goto ,

Kitahara , et al., A Modeling of singing voice robust to accompaniment sounds and its application to singer identification and vocal-timbre-similarity-based music information retrieval, IEEE Transactions on Audio, Speech and Language Processing18(3) (2010), 638–648.

Salamon and

Gómez , Melody extraction from polyphonic music signals using pitch contour characteristics, IEEE Transactions on Audio, Speech, and Language Processing20(6) (2012), 1759–1770.

Emiya ,

Badeau and

David , Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle, IEEE Transactions on Audio, Speech, and Language Processing18(6) (2010), 1643–1654.

10.

Benetos and

Dixon , Joint multi-pitch detection using harmonic envelope estimation for polyphonic music transcription, IEEE Journal of Selected Topics in Signal Processing5(6) (2011), 1111–1123.

Research on the improved method of fundamental frequency extraction for music automatic recognition of piano music

Abstract

Keywords

Get full access to this article

References