course description

Music information retrieval is the foundation of music data science and artificial intelligence. In the first half of the course, students learn to use the music21 library of the Python programming language to analyze symbolic datasets. Students also have to operate the Sonic Visualiser software, and participate in expert annotation and labeling, as well as data collection and cleaning processes. In the second half of the semester, the librosa library of Python is added to analyze audio datasets. Finally, the method of digital archive and the spirit of digital humanities research are discussed. The course adopts cross-platform open-source software that the general public can legally download and install for free, in order to facilitate sustainable development and promotion.

references

books:

1. M. Müller, Fundamentals of Music Processing: Using Python and Jupyter Notebooks. Cham: Springer, 2021.

2. D. Meredith, Ed., Computational Music Analysis. Cham: Springer, 2016.

3. W. B. Hewlett and E. Selfridge-Field, Eds., Melodic Similarity: Concepts, Procedures, and Applications. Cambridge, MA: The MIT Press, 1998.

4. M. Leman, Ed., Music, Gestalt, and Computing: Studies in Cognitive and Systematic Musicology. Berlin: Springer-Verlag, 1997.

5. Z. W. Ras and A. A. Wieczorkowska, Eds., Advances in Music Information Retrieval. Berlin: Springer-Verlag, 2010.

6. M. Schedl, E. Gómez, and J. Urbano, Music Information Retrieval: Recent Developments and Applications. Hanover, MA: Now Publishers, 2014.

7. M. Müller, M. Goto, and M. Schedl, Eds., Multimodal Music Processing. Wadern: Schloss Dagstuhl, 2012.

8. A. Lerch, An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics. Hoboken: Wiley-IEEE Press, 2012.

9. J. V. Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data, 3rd ed. Cambridge, MA: The MIT Press, 2021.

papers:

1. R. S. Huang, et al. "Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music," Transactions of the International Society for Music Information Retrieval, vol. 6, no.1, pp. 43–59, 2023.

2. M. Kassler, “Toward Musical Information Retrieval,” Perspectives of New Music, vol. 4, no. 2, pp. 59–67, 1966.

courses:

1. Studies in Western Music History: Quantitative and Computational Approaches to Music History, Department of Music and Theater Arts, Massachusetts Institute of Technology.

2. Music Signal Processing, Department of Electrical Engineering, Columbia University.

3. Audio Signal Processing for Music Application, Coursera.

4. Audio Signal Processing for Machine Learning by Valerio Velardo.

5. Music Information Retrieval, Department of Computer Science, National Tsing Hua University.

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office hour

one hour every time by appointment