FMP Notebooks
The FMP Notebooks are a collection of educational material for teaching and learning Fundamentals of Music Processing (FMP) with a particular focus on the audio domain. Covering well-established topics in Music Information Retrieval (MIR) as motivating application scenarios, the FMP Notebooks provide detailed textbook-like explanations of central techniques and algorithms in combination with Python code examples that illustrate how to implement the theory. All components including the introductions of MIR scenarios, illustrations, sound examples, technical concepts, mathematical details, and code examples are integrated into a consistent and comprehensive framework based on Jupyter notebooks. The FMP notebooks are suited for studying the theory and practice, for generating educational material for lectures, as well as for providing baseline implementations for many MIR tasks, thus addressing students, teachers, and researchers.
The FMP Notebooks are maintained by Meinard Müller. For comments, please email meinard.mueller@audiolabs-erlangen.de. I am grateful for any feedback and suggestions.
Publications
- Meinard Müller and Frank Zalkow
FMP Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing
In Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2019. PDF Demo@inproceedings{MuellerZ19_FMP_ISMIR, author = {Meinard M{\"u}ller and Frank Zalkow}, title = {{FMP} Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing}, booktitle = {Proceedings of the International Conference on Music Information Retrieval ({ISMIR})}, address = {Delft, The Netherlands}, month = {November}, year = {2019}, url-demo={https://www.audiolabs-erlangen.de/FMP} url-pdf = {2019_MuellerZalkow_FMP_ISMIR.pdf} } - Meinard Müller
An Educational Guide Through the FMP Notebooks for Teaching and Learning Fundamentals of Music Processing
Signals, 2(2): 245–285, 2021. PDF Demo DOI@article{Mueller21_FMP_Signals, author = {Meinard M{\"u}ller}, title = {An Educational Guide Through the {FMP} Notebooks for Teaching and Learning Fundamentals of Music Processing}, journal = {Signals}, volume = {2}, number = {2}, pages = {245--285}, year = {2021}, doi = {10.3390/signals2020018}, url-demo = {https://www.audiolabs-erlangen.de/FMP}, url-pdf = {2021_Mueller_FMP_Signals.pdf} } - Meinard Müller
Fundamentals of Music Processing — Using Python and Jupyter Notebooks
Springer Verlag, ISBN: 978-3-030-69807-2, 2021. Details DOI@book{Mueller21_FMP_SPRINGER, author = {Meinard M\"{u}ller}, title = {Fundamentals of Music Processing -- Using Python and Jupyter Notebooks}, type = {Monograph}, year = {2021}, isbn = {978-3-030-69807-2}, doi = {10.1007/978-3-030-69808-9}, publisher = {Springer Verlag}, edition = {2nd}, pages = {1--495}, url-details = {http://www.music-processing.de} } - Meinard Müller
Fundamentals of Music Processing — Audio, Analysis, Algorithms, Applications
Springer Verlag, ISBN: 978-3-319-21944-8, 2015. Details@book{Mueller15_FMP_SPRINGER, author = {Meinard M\"{u}ller}, title = {Fundamentals of Music Processing -- Audio, Analysis, Algorithms, Applications}, type = {Monograph}, year = {2015}, isbn = {978-3-319-21944-8}, publisher = {Springer Verlag}, url-details={http://www.music-processing.de} } - Meinard Müller, Brian McFee, and Katherine Kinnaird
Interactive Learning of Signal Processing Through Music: Making Fourier Analysis Concrete for Students
IEEE Signal Processing Magazine, 38(3): 73–84, 2021. PDF DOI@article{MuellerMK21_LearningMusicSP_IEEE-SPM, author = {Meinard M{\"u}ller and Brian McFee and Katherine Kinnaird}, title = {Interactive Learning of Signal Processing Through Music: Making Fourier Analysis Concrete for Students}, journal = {{IEEE} Signal Processing Magazine}, volume = {38}, number = {3}, pages = {73--84}, year = {2021}, doi = {10.1109/MSP.2021.3052181}, url-pdf = {2021_MuellerMK_LearningMusicSP_IEEE-SPM_Preprint.pdf} }