Abstract
This work makes use of instrument-dependent models to separate the different sources of multiple instrument mixtures. Three different models are applied: (a) basic spectral model with harmonic constraint, (b) source-filter model with harmonic-comb excitation and (c) source-filter model with multi-excitation per instrument. The parameters of the models are optimized by an augmented NMF algorithm and learnt in a training stage. The models are presented in [1], here the experimental setting for the application to source separation is explained. The instrument-dependent NMF models are first trained and then a test stage is performed. A comparison with other state-of-the-art software is presented. Results show that source-filter model with multi-excitation per instrument outperforms the other compared models.
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References
Carabias-Orti, J.J., Virtanen, T., Vera-Candeas, P., Ruiz-Reyes, N., Canadas-Quesada, F.J.: Musical Instrument Sound Multi-Excitation Model for Non-Negative Spectrogram Factorization. IEEE Journal on Selected Topics on Signal Processing 5(6), 1144–1158 (2011)
Virtanen, T., Klapuri, A.: Analysis of polyphonic audio using source-filter model and non-negative matrix factorization. In: Advances in Models for Acoustic Processing, Neural Information Processing Systems Workshop (2006)
Heittola, T., Klapuri, A., Virtanen, T.: Musical instrument recognition in polyphonic audio using source-filter model for sound separation. In: Proc. 10th Int. Society for Music Information Retrieval Conf. (ISMIR), Kobe, Japan (2009)
Virtanen, T.: Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. IEEE Transactions on Audio, Speech, and Language Processing 15(3), 1066–1074 (2007)
Goto, M.: Development of the RWC Music Database. In: Proc. of the 18th International Congress on Acoustics (ICA 2004), pp.I-553–I-556 (April 2004) (invited paper)
Mirex 2007: Music information retrieval evaluation exchange, http://www.music-ir.org/mirex/wiki/2007:MIREX_HOME
Vincent, E., Bertin, N., Badeau, R.: Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation. IEEE Transactions on Audio, Speech, and Language Processing 18(3), 528–537 (2010)
Ruiz-Reyes, N., Vera-Candeas, P.: Adaptive Signal Modeling Based on Sparse Approximations for Scalable Parametric Audio Coding. IEEE Transactions on Audio, Speech, and Language Processing 18(3), 447–460 (2010)
Every, M.R., Szymanski, J.E.: Separation of synchronous pitched notes by spectral filtering of harmonics. IEEE Trans. Audio, Speech, Lang. Process. 14(5), 1845–1856 (2006)
Ozerov, A., Vincent, E.: A general flexible framework for the handling of prior information in audio source separation. IEEE Trans. Audio, Speech, Lang. Process (to appear)
Vincent, E.: Musical source separation using time-frequency source priors. IEEE Transactions on Audio, Speech, and Language Processing 14(1), 91–98 (2006)
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Rodriguez-Serrano, F.J., Carabias-Orti, J.J., Vera-Candeas, P., Virtanen, T., Ruiz-Reyes, N. (2012). Multiple Instrument Mixtures Source Separation Evaluation Using Instrument-Dependent NMF Models. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_47
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DOI: https://doi.org/10.1007/978-3-642-28551-6_47
Publisher Name: Springer, Berlin, Heidelberg
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