Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Multipitch estimation, also known as multiple fundamental frequency (F0) estimation, is an important part of the Music Information Retrieval (MIR) field. Although there have been many different approaches proposed, none of them has ever exceeded the abilities of a trained musician. In this work, an iterative cancellation method is analysed, being applied to three different sound representations - salience spectrum obtained using Constant-Q Transform, cepstrum and enhanced autocorrelation result. Real-life recordings of different musical instruments are used as a database and the parameters of the solution are optimized using a simple yet effective metaheuristic approach - the Luus-Jaakola algorithm. The presented approach results in 85% efficiency on the test database.
Rocznik
Tom
Strony
751--757
Opis fizyczny
Bibliogr. 14, rys., tab., wykr.
Twórcy
autor
- Institute of Information Technology, Lodz University of Technology, 215 Wolczanska St., 90-924 Łódź, Poland
autor
- Institute of Information Technology, Lodz University of Technology, 215 Wolczanska St., 90-924 Łódź, Poland
Bibliografia
- [1] E. Benetos, S. Dixon, D. Giannoulis, H. Kirchoff, and A. Klapuri, “Automatic music transcription: challenges and future directions”, J. Intelligent Information Systems 41 (3), 407-434 (2013).
- [2] B. Stasiak, “Follow that tune - dynamic time warping refinement for query by humming”, Proc. Joint Conf. New Trends in Audio and Video Signal Processing: Algorithms, Architectures, Arrangements, and Applications 1, 109-114 (2012).
- [3] B. Stasiak and K. Rychlicki-Kicior, “Fundamental frequency extraction in speech emotion recognition”, In: Multimedia Communications, Services and Security, Communications in Computer and Information Science, pp. 287, 292-303, Springer-Verlag, Berlin, 2012.
- [4] J. Salomon, E. Gomez, D.P.W. Ellis, and G. Richard, “Melody extraction from polyphonic music signals”, IEEE Signal Processing Magazine 31 (2), 118-134 (2014).
- [5] M. Davy and A. Klapuri, Signal Processing Methods for Music Transcription, Springer-Verlag, Berlin, 2006.
- [6] F. Argenti, P. Nesi, and G. Pantaleo, “Automatic music transcription: from monophonic to polyphonic”, Musical Robots and Interactive Multimodal Systems, pp. 27-46, Springer- Verlag, Berlin, 2011.
- [7] K. Dressler, “Multiple fundamental frequency extraction for mirex 2012”, 13th Int. Conf. on Music Information Retrieval 1, CD-ROM (2012).
- [8] A. Klapuri, “Multiple fundamental frequency estimation by summing harmonic amplitudes”, Proc. 7th Int. Conf. on Music Information Retrieval 1, 216-221 (2006).
- [9] C. Yeh, “Multiple fundamental frequency estimation of polyphonic recordings”, Ph.D. Thesis, Universite de Paris, Paris, 2008.
- [10] T. Tolonen and M. Karjalainen, “A computationally efficient multipitch analysis model. Speech and audio processing”, IEEE Trans. on Speech and Audio Processing 8 (6), 708-716 (2000).
- [11] D. Mazzoni and R.B. Dannenberg, “Melody matching directly from audio”, 2nd Annual Int. Symp. on Music Information Retrieval 1, 17-18 (2001).
- [12] R. Luus and T. Jaakola, “Optimization by direct search and systematic reduction of the size of search region”, American Institute of Chemical Engineers J. (AIChE) 19, 760-766 (1973).
- [13] University of Iowa, “Musical instrument samples dataset”, http://theremin.music.uiowa.edu/, access date: 20/01/2013.
- [14] P. Boersma, “Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound”, IFA Proceedings 17, 97-110 (1993).Bull.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fa4c027d-feb5-4f5c-afe4-fe9df0e0b760