A curated list of awesome article, tutorial, library, webpage, etc about music informatics. Many thanks to all the members of Openmusicinformatics (openmusicinformatics.slack.com) for sharing informations!! If you want to add new information to this list, please inform at issues.
- Librosa https://librosa.org/ Music and audio analysis
- madmom https://madmom.readthedocs.io/en/latest/ MIR signal processing
- essentia https://essentia.upf.edu/index.html MIR signal processing
- opensmile https://www.audeering.com/opensmile/ Audio feature extractor
- pyreaper https://github.com/r9y9/pyreaper Fundamental frequency estimation
- pyworld https://github.com/JeremyCCHsu/Python-Wrapper-for-World-Vocoder Wrapper of world vocoder
- crepe https://marl.github.io/crepe/ Fundamental frequency estimation
- kymatio https://www.kymat.io/ Wavelet scattering transform
- pysndfx https://pypi.org/project/pysndfx/ audio effect
- nnmnkwii https://github.com/r9y9/nnmnkwii speech synthesis
- Audiocraft https://github.com/facebookresearch/audiocraft PyTorch library for deep learning research on audio generation
- audiotools https://github.com/descriptinc/audiotools Object-oriented handling of audio signals, with fast augmentation routines, batching, padding, and more.
- SouPyX https://github.com/Yuan-ManX/SouPyX Audio Processing Library
- music21 https://web.mit.edu/music21/ Musicxml handling and musicological analysis
- pretty-midi https://craffel.github.io/pretty-midi/ Midi data io
- mido https://mido.readthedocs.io/en/latest/ Midi data io
- pypianoroll https://salu133445.github.io/pypianoroll/ Piano-roll
- muspy https://github.com/salu133445/muspy Music generation pipeline
- miditok https://github.com/Natooz/MidiTok MIDI tokenization for DNNs
- miditoolkit: https://github.com/YatingMusic/miditoolkit MIDI parser for handling both eventswith symbolic timing and piano-rolls
- note_seq https://github.com/magenta/note-seq MIDI data manipulating and converting for for ML model training (e.g., one-hot tensors)
- partitura https://github.com/CPJKU/partitura Python package for handling symbolic musical information
- Torchaudio https://pytorch.org/audio/stable/index.html Audio library with pytorch
- tf.signal https://www.tensorflow.org/api_docs/python/tf/signal Audio library with tensorflow
- kapre https://github.com/keunwoochoi/kapre Audio library with tensorflow
- nnAudio https://github.com/KinWaiCheuk/nnAudio Audio library with pytorch
- mir_eval https://craffel.github.io/mir_eval/ Evaluation of MIR tasks
- mirdata https://github.com/mir-dataset-loaders/mirdata Dataset handling
- musdb https://github.com/sigsep/sigsep-mus-db MUS-DB interface
- nussl https://github.com/nussl/nussl Music source separation
- pyroomacoustics https://github.com/LCAV/pyroomacoustics Audio Separation
- magenta https://magenta.tensorflow.org/ Music creation library from Google
- muzic https://github.com/microsoft/muzic Music creation library from Microsoft
- omnizart https://music-and-culture-technology-lab.github.io/omnizart-doc/ Musical transcription library
- spotipy https://spotipy.readthedocs.io/en/2.17.1/ Wrapper of spotify API
- espnet https://github.com/espnet/espnet Library for sppech tasks
- DDSP https://magenta.tensorflow.org/ddsp Differentiable Digital Signal Processing
- ALTA https://github.com/emirdemirel/ALTA Music lyric transcription recipe
- Muskit https://github.com/SJTMusicTeam/Muskits Open-source music processing toolkits
- compIAM https://github.com/MTG/compIAM computational tools for Indian music
- Tony https://www.sonicvisualiser.org/tony/
- Sonic Visualiser https://www.sonicvisualiser.org/documentation.html
- sox http://sox.sourceforge.net/
- AMPACT https://ampact.tumblr.com/
- essentia.js https://mtg.github.io/essentia.js/
- Songle https://songle.jp/
- Spleeter https://github.com/deezer/spleeter
- lab.js https://lab.js.org/
- wavesurfer.js https://wavesurfer-js.org/
- WebAudio API https://developer.mozilla.org/ja/docs/Web/API/Web_Audio_API
- curio audio annotator https://github.com/CrowdCurio/audio-annotator
- Demucs https://github.com/facebookresearch/demucs
- Basic Pitch https://basicpitch.spotify.com/
- Meyda https://github.com/meyda/meyda
- Visualization for AI-assisted composition https://visvar.github.io/vis-ai-comp/
- Sheetsage https://github.com/chrisdonahue/sheetsage
- Song Describer https://github.com/ilaria-manco/song-describer
- Essentia API https://github.com/CDrummond/essentia-api
- Riffusion https://www.riffusion.com/
- Audio Content Analysis http://www.audiocontentanalysis.org/datasets.html
- ISMIR Datasets https://ismir.net/resources/datasets/
- SigSep Multi-track https://sigsep.github.io/datasets/
- UPF-MTG datasets https://www.upf.edu/web/mtg/software-datasets
- paperwithcode https://paperswithcode.com/
- MCR https://github.com/dharasim/MCR/wiki
- FMA: A Dataset For Music Analysis https://github.com/mdeff/fma
- ismir2022-datasets: https://github.com/otnemrasordep/ismir2022-datasets
- DadaGP https://github.com/dada-bots/dadaGP
- Fundamentals of Music Processing https://www.audiolabs-erlangen.de/fau/professor/mueller/bookFMP (Jupyter notebook: https://www.audiolabs-erlangen.de/resources/MIR/FMP/C0/C0.html)
- KAIST, GCT634 (AI613) Fall 2021. Musical Applications of Machine Learning https://mac.kaist.ac.kr/~juhan/gct634/index.html
- Stanford CCRMA Notes https://musicinformationretrieval.com/
- Audio Signal Processing for Music Applications https://ja.coursera.org/learn/audio-signal-processing
- ISMIR Tutorials https://ismir.net/resources/tutorials/
- Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity https://www.springer.com/gp/book/9783030721152
- Deep Learning Techniques for Music Generation: https://link.springer.com/book/10.1007/978-3-319-70163-9
- Preparation Course Python Notebooks https://github.com/meinardmueller/PCP
- Audio content analysis https://www.audiocontentanalysis.org/
- A tutorial on AI music composition https://www.microsoft.com/en-us/research/uploads/prod/2021/10/Tutorial-on-AI-Music-Composition-@ACM-MM-2021.pdf
- Open Source Tools & Data for Music Source Separation https://source-separation.github.io/tutorial/intro/src_sep_101.html
- Music-Recommendation-Tutorial-2017 https://www.slideshare.net/FabienGouyon/musicrecommendationtutorial2017
- Music and Human-Computer Interaction https://link.springer.com/book/10.1007/978-1-4471-2990-5
- Music + AI Reading Group (MILA) https://www.youtube.com/channel/UCdrzCFRsIFGw2fiItAk5_Og/videos
- Dagstuhl Seminar Series
- Multimodal Music Processing 2011 https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=11041
- Computational Music Structure Analysis 2016 https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16092
- Computational Methods for Melody and Voice Processing in Music Recordings 2019 https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=19052
- Deep Learning and Knowledge Integration for Music Audio Analysis 2022 https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=22082
- Singing and AI https://www.youtube.com/@singingandai7016
- Creative ML https://github.com/acids-ircam/creative_ml
- Content-Based Music Information Retrieval: Current Directions and Future Challenges, Casey et al., 2008 https://ieeexplore.ieee.org/document/4472077?tp=&arnumber=4472077
- A Survey of Music Recommendation Systems and Future Perspectives Song et al., 2012 https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.414.6614&rep=rep1&type=pdf
- Music Information Retrieval: Recent Developments and Applications, Schedl et al., 2014 https://www.nowpublishers.com/article/Details/INR-042
- Automatic Music Transcription: An Overview, Benetos et al., 2018 https://ieeexplore.ieee.org/document/8588423
- Musical Source Separation: An Introduction, Cano et al., 2018 https://ieeexplore.ieee.org/document/8588410
- Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach, Nam et al., 2018 https://ieeexplore.ieee.org/document/8588424
- Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies, Müller et al., 2018 https://ieeexplore.ieee.org/document/8588416
- Audiovisual Analysis of Music Performances: Overview of an Emerging Field, Duan et al., 2018 https://ieeexplore.ieee.org/document/8588405
- An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music, Humphrey et al., 2018 https://ieeexplore.ieee.org/document/8588417
- Music performance analysis: A survey, Lerch et al., 2020 https://transactions.ismir.net/articles/10.5334/tismir.53/
- Deep Learning for Audio Signal Processing, Putwins et al., 2019 https://arxiv.org/abs/1905.00078
- A functional taxonomy of music generation systems, Herremans et al., 2017 https://dl.acm.org/doi/abs/10.1145/3108242?casa_token=qkSGMAo2GgUAAAAA:ojfdHvKlXLB8my1J8Fw3zBlOm7M59H9Meo-RSGsXKzvDJlN3fqsUFhb1fDX2jPismOgjBV8LsQRyBg
- Automatic Melody Harmonization with Triad Chords: A Comparative Study, Yeh et al., 2021 https://arxiv.org/pdf/2001.02360.pdf
- A Survey on Recent Deep Learning-driven Singing Voice Synthesis Systems, Cho et al., 2021 https://arxiv.org/pdf/2110.02511.pdf
- Music Composition with Deep Learning: A Review, Hernandez-Olivan et al., 2021 https://arxiv.org/abs/2108.12290
- Melody Extraction from Polyphonic Music by Deep Learning Approaches: A Review Reddy M et al., 2022 https://arxiv.org/pdf/2202.01078.pdf
- 20th Anniversary of ISMIR, 2019. https://transactions.ismir.net/collections/special/20th-anniversary-of-ismir/
- Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications., Gómez-Cañón et al., 2021 https://github.com/juansgomez87/datasets_emotion, https://ieeexplore.ieee.org/document/9591555
- A Survey of Music Visualization Techniques., Lima et al., 2021 https://dl.acm.org/doi/pdf/10.1145/3461835
- Expression Control in Singing Voice Synthesis: Features, approaches, evaluation, and challenges., Bonada et al. 2015 https://ieeexplore.ieee.org/abstract/document/7298564
- A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions, Ji et al., 2020 https://arxiv.org/abs/2011.06801
- TOWARDS A (BETTER) DEFINITION OF THE DESCRIPTION OF ANNOTATED MIR CORPORA., Peeters et al., 2012 https://ismir2012.ismir.net/event/papers/025_ISMIR_2012.pdf
- Deep Learning Approaches in Topics of Singing Information Processing, C. Gupta et al., 2022 https://ieeexplore.ieee.org/document/9829265
- awesome-awesome https://github.com/sindresorhus/awesome
- awesome-python-scientific-audio https://github.com/faroit/awesome-python-scientific-audio#music-information-retrieval
- awesome-music https://github.com/ciconia/awesome-music
- awesome-sheet-music https://github.com/ad-si/awesome-sheet-music
- awesome-audio-visualization https://github.com/willianjusten/awesome-audio-visualization
- awesome-deep-learning-music https://github.com/ybayle/awesome-deep-learning-music
- awesome-web-audio https://github.com/notthetup/awesome-webaudio
- awesome-musicdsp https://github.com/olilarkin/awesome-musicdsp
- awesome-data-labeling https://github.com/heartexlabs/awesome-data-labeling
- Awesome Singing Voice Synthesis and Singing Voice Conversion https://github.com/guan-yuan/Awesome-Singing-Voice-Synthesis-and-Singing-Voice-Conversion
- ai-audio-startups https://github.com/csteinmetz1/ai-audio-startups
- Art with AI cource https://github.com/mathigatti/ArtWithAICourse
- Music creation with DL https://github.com/umbrellabeach/music-generation-with-DL
- Hands on music generation with magenta https://github.com/PacktPublishing/hands-on-music-generation-with-magenta
- genmusic_demo_list https://github.com/affige/genmusic_demo_list
- multimodal learning for music https://github.com/ilaria-manco/multimodal-ml-music
- Audio AI timeline (from 2023) https://github.com/archinetai/audio-ai-timeline
- Audio Development Tools (ADT) 🔥 https://github.com/Yuan-ManX/audio-development-tools
- Deep learning for music generation https://github.com/carlosholivan/DeepLearningMusicGeneration
- MIR-AIDJ https://github.com/mir-aidj
- GenMusic Demo List https://github.com/affige/genmusic_demo_list
- Preparing a Successful ISMIR Submission https://ismir2021.ismir.net/blog/preparing/
- Reviewing ISMIR Papers: Some Personal Thoughts (Meinard Müller) https://www.youtube.com/watch?v=hSsVktr1huQ
- Music Technology conference list http://conferences.smcnetwork.org/
- ISMIR community https://ismir.net/about/
- ISMIR 2021 Lab showcase https://ismir2021.ismir.net/labshowcase/
- Song describer https://song-describer.streamlit.app/
To the extent possible under law, Yuya Yamamoto has waived all copyright and related or neighboring rights to this work.