Proyecto Final

Repositorio

https://github.com/Sirivasv/MCC-AA/tree/master/ProyectoFinal

Reporte Final Aprendizaje Automatizado

https://github.com/Sirivasv/MCC-AA/blob/master/ProyectoFinal/latex/TISMIRtemplate.pdf

Descarga del Dataset Completo (40+GB)

https://magenta.tensorflow.org/datasets/nsynth#instrument-classes

Extracción de atributos

https://colab.research.google.com/drive/1FFOlA5Q2O5TdxpVnBq7qKkHAbK6a0fvj?usp=sharing

Entrenamiento

https://colab.research.google.com/drive/1NcyNzKjgONhlmHEuIdWb67d3MGnz05eI?usp=sharing

Evaluación Empírica

https://colab.research.google.com/drive/1UqCR0zx6se6Go1ZHXuzgFjn01IHa3-eV?usp=sharing

Presentación final

https://docs.google.com/presentation/d/1xMIzk_UIBOG3w5R-Jm9oBevp_OEt8-nWKTHc1Po3FPI/edit?usp=sharing

References

[1]J. Brown y M. Puckette, “An efficient algorithm for the calculation of a constant Q transform”, Journal of the Acoustical Society of America, vol. 92, p. 2698, nov. 1992, doi: 10.1121/1.404385.

[2]J. Schnupp, I. Nelken, y A. King, Auditory neuroscience: Making sense of sound. Cambridge, MA, US: MIT Press, 2011, pp. x, 356.

[3]P. Herrera-Boyer, A. Klapuri, y M. Davy, “Automatic Classification of Pitched Musical Instrument Sounds”, en Signal Processing Methods for Music Transcription, A. Klapuri y M. Davy, Eds. Boston, MA: Springer US, 2006, pp. 163–200.

[4]E. Benetos, S. Dixon, Z. Duan, y S. Ewert, “Automatic Music Transcription: An Overview”, IEEE Signal Processing Magazine, vol. 36, núm. 1, pp. 20–30, ene. 2019, doi: 10.1109/MSP.2018.2869928.

[5]E. Benetos, S. Dixon, D. Giannoulis, H. Kirchhoff, y A. Klapuri, “Automatic music transcription: challenges and future directions”, J Intell Inf Syst, vol. 41, núm. 3, pp. 407–434, dic. 2013, doi: 10.1007/s10844-013-0258-3.

[6]F. Fuhrmann, “Automatic musical instrument recognition from polyphonic music audio signals”, p. 265.

[7]F. Argenti, P. Nesi, y G. Pantaleo, “Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, núm. 6, pp. 1610–1630, ago. 2011, doi: 10.1109/TASL.2010.2093894.

[8]J. C. Brown, “Calculation of a constant Q spectral transform”, The Journal of the Acoustical Society of America, vol. 89, núm. 1, pp. 425–434, ene. 1991, doi: 10.1121/1.400476.

[9]C. Schoerkhuber y K. Anssi, “Constant-Q transform toolbox for music processing”, 7th Sound and Music Computing Conference, 2010.

[10]J. W. Kim, J. Salamon, P. Li, y J. P. Bello, “CREPE: A Convolutional Representation for Pitch Estimation”, arXiv:1802.06182 [cs, eess, stat], feb. 2018, Consultado: feb. 27, 2020. [En línea]. Disponible en: http://arxiv.org/abs/1802.06182.

[11]D. Bogdanov et al., “ESSENTIA: an Audio Analysis Library for Music Information Retrieval”, presentado en Proceedings - 14th International Society for Music Information Retrieval Conference, nov. 2013.

[12]M. Müller y F. Zalkow, “FMP Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing”, Delft, The Netherlands, nov. 2019.

[13]Y.-N. Hung y Y.-H. Yang, “Frame-level Instrument Recognition by Timbre and Pitch”, arXiv:1806.09587 [cs, eess], jun. 2018, Consultado: abr. 14, 2020. [En línea]. Disponible en: http://arxiv.org/abs/1806.09587.

[14]T. Kitahara, “Instrument Identification in Polyphonic Music: Feature Weighting with Mixed Sounds, Pitch-Dependent Timbre Modeling, and Use of Musical Context”, p. 6.

[15]B. McFee et al., “librosa: Audio and Music Signal Analysis in Python”, ene. 2015, doi: 10.25080/majora-7b98e3ed-003.

[16]J. Engel et al., “Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders”, arXiv:1704.01279 [cs], abr. 2017, Consultado: feb. 27, 2020. [En línea]. Disponible en: http://arxiv.org/abs/1704.01279.

[17]D. Gerhard, Pitch Extraction and Fundamental Frequency: History and Current Techniques. 2003.

[18]M. V. A. Rao y P. K. Ghosh, “Pitch prediction from Mel-frequency cepstral coefficients using sparse spectrum recovery”, en 2017 Twenty-third National Conference on Communications (NCC), mar. 2017, pp. 1–6, doi: 10.1109/NCC.2017.8077130.

[19]K. Siedenburg, C. Saitis, S. McAdams, A. N. Popper, y R. R. Fay, Eds., Timbre: Acoustics, Perception, and Cognition. Springer International Publishing, 2019.

[20]T. Drugman, G. Huybrechts, V. Klimkov, y A. Moinet, “Traditional Machine Learning for Pitch Detection”, IEEE Signal Processing Letters, vol. 25, núm. 11, pp. 1745–1749, nov. 2018, doi: 10.1109/LSP.2018.2874155.

[21]J. Salamon y E. Gomez, “Melody Extraction from Polyphonic Music Signals using Pitch Contour Characteristics”, IEEE TRANSACTIONS ON AUDIO, p. 12.