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All Projects → ybayle → Awesome Deep Learning Music

ybayle / Awesome Deep Learning Music

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List of articles related to deep learning applied to music

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Deep Learning for Music (DL4M) Awesome

By Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website).

TL;DR Non-exhaustive list of scientific articles on deep learning for music: summary (Article title, pdf link and code), details (table - more info), details (bib - all info)

The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

Table of contents

DL4M summary

 Year Articles, Thesis and Reports Code
1988 Neural net modeling of music No
1988 Creation by refinement: A creativity paradigm for gradient descent learning networks No
1988 A sequential network design for musical applications No
1989 The representation of pitch in a neural net model of chord classification No
1989 Algorithms for music composition by neural nets: Improved CBR paradigms No
1989 A connectionist approach to algorithmic composition No
1994 Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing No
1995 Automatic source identification of monophonic musical instrument sounds No
1995 Neural network based model for classification of music type No
1997 A machine learning approach to musical style recognition No
1998 Recognition of music types No
1999 Musical networks: Parallel distributed perception and performance No
2001 Multi-phase learning for jazz improvisation and interaction No
2002 A supervised learning approach to musical style recognition No
2002 Finding temporal structure in music: Blues improvisation with LSTM recurrent networks No
2002 Neural networks for note onset detection in piano music No
2004 A convolutional-kernel based approach for note onset detection in piano-solo audio signals No
2009 Unsupervised feature learning for audio classification using convolutional deep belief networks No
2010 Audio musical genre classification using convolutional neural networks and pitch and tempo transformations No
2010 Automatic musical pattern feature extraction using convolutional neural network No
2011 Audio-based music classification with a pretrained convolutional network No
2012 Rethinking automatic chord recognition with convolutional neural networks No
2012 Moving beyond feature design: Deep architectures and automatic feature learning in music informatics No
2012 Local-feature-map integration using convolutional neural networks for music genre classification No
2012 Learning sparse feature representations for music annotation and retrieval No
2012 Unsupervised learning of local features for music classification No
2013 Multiscale approaches to music audio feature learning No
2013 Musical onset detection with convolutional neural networks No
2013 Deep content-based music recommendation No
2014 The munich LSTM-RNN approach to the MediaEval 2014 Emotion In Music task No
2014 End-to-end learning for music audio No
2014 Deep learning for music genre classification No
2014 Recognition of acoustic events using deep neural networks No
2014 Deep image features in music information retrieval No
2014 From music audio to chord tablature: Teaching deep convolutional networks to play guitar No
2014 Improved musical onset detection with convolutional neural networks No
2014 Boundary detection in music structure analysis using convolutional neural networks No
2014 Improving content-based and hybrid music recommendation using deep learning No
2014 A deep representation for invariance and music classification No
2015 Auralisation of deep convolutional neural networks: Listening to learned features GitHub
2015 Downbeat tracking with multiple features and deep neural networks No
2015 Music boundary detection using neural networks on spectrograms and self-similarity lag matrices No
2015 Classification of spatial audio location and content using convolutional neural networks No
2015 Deep learning, audio adversaries, and music content analysis No
2015 Deep learning and music adversaries GitHub
2015 Singing voice detection with deep recurrent neural networks No
2015 Automatic instrument recognition in polyphonic music using convolutional neural networks No
2015 A software framework for musical data augmentation No
2015 A deep bag-of-features model for music auto-tagging No
2015 Music-noise segmentation in spectrotemporal domain using convolutional neural networks No
2015 Musical instrument sound classification with deep convolutional neural network using feature fusion approach No
2015 Environmental sound classification with convolutional neural networks No
2015 Exploring data augmentation for improved singing voice detection with neural networks GitHub
2015 Singer traits identification using deep neural network No
2015 A hybrid recurrent neural network for music transcription No
2015 An end-to-end neural network for polyphonic music transcription No
2015 Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network No
2015 Folk music style modelling by recurrent neural networks with long short term memory units GitHub
2015 Deep neural network based instrument extraction from music No
2015 A deep neural network for modeling music No
2016 An efficient approach for segmentation, feature extraction and classification of audio signals No
2016 Text-based LSTM networks for automatic music composition No
2016 Towards playlist generation algorithms using RNNs trained on within-track transitions No
2016 Automatic tagging using deep convolutional neural networks No
2016 Automatic chord estimation on seventhsbass chord vocabulary using deep neural network No
2016 DeepBach: A steerable model for Bach chorales generation GitHub
2016 Bayesian meter tracking on learned signal representations No
2016 Deep learning for music No
2016 Learning temporal features using a deep neural network and its application to music genre classification No
2016 On the potential of simple framewise approaches to piano transcription No
2016 Feature learning for chord recognition: The deep chroma extractor GitHub
2016 A fully convolutional deep auditory model for musical chord recognition No
2016 A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction No
2016 Event localization in music auto-tagging GitHub
2016 Deep convolutional networks on the pitch spiral for musical instrument recognition GitHub
2016 SampleRNN: An unconditional end-to-end neural audio generation model GitHub
2016 Robust audio event recognition with 1-max pooling convolutional neural networks No
2016 Experimenting with musically motivated convolutional neural networks GitHub
2016 Singing voice melody transcription using deep neural networks No
2016 Singing voice separation using deep neural networks and F0 estimation Website
2016 Learning to pinpoint singing voice from weakly labeled examples No
2016 Analysis of time-frequency representations for musical onset detection with convolutional neural network No
2016 Note onset detection in musical signals via neural-network-based multi-ODF fusion No
2016 Music transcription modelling and composition using deep learning GitHub
2016 Convolutional neural network for robust pitch determination No
2016 Deep convolutional neural networks and data augmentation for acoustic event detection Website
2017 Gabor frames and deep scattering networks in audio processing No
2017 Vision-based detection of acoustic timed events: A case study on clarinet note onsets No
2017 Deep learning techniques for music generation - A survey No
2017 JamBot: Music theory aware chord based generation of polyphonic music with LSTMs GitHub
2017 XFlow: 1D <-> 2D cross-modal deep neural networks for audiovisual classification No
2017 Machine listening intelligence No
2017 Monoaural audio source separation using deep convolutional neural networks GitHub
2017 Deep multimodal network for multi-label classification No
2017 A tutorial on deep learning for music information retrieval GitHub
2017 A comparison on audio signal preprocessing methods for deep neural networks on music tagging GitHub
2017 Transfer learning for music classification and regression tasks GitHub
2017 Convolutional recurrent neural networks for music classification GitHub
2017 An evaluation of convolutional neural networks for music classification using spectrograms No
2017 Large vocabulary automatic chord estimation using deep neural nets: Design framework, system variations and limitations No
2017 Basic filters for convolutional neural networks: Training or design? No
2017 Ensemble Of Deep Neural Networks For Acoustic Scene Classification No
2017 Robust downbeat tracking using an ensemble of convolutional networks No
2017 Music signal processing using vector product neural networks No
2017 Transforming musical signals through a genre classifying convolutional neural network No
2017 Audio to score matching by combining phonetic and duration information GitHub
2017 Interactive music generation with positional constraints using anticipation-RNNs No
2017 Deep rank-based transposition-invariant distances on musical sequences No
2017 GLSR-VAE: Geodesic latent space regularization for variational autoencoder architectures No
2017 Deep convolutional neural networks for predominant instrument recognition in polyphonic music No
2017 CNN architectures for large-scale audio classification No
2017 DeepSheet: A sheet music generator based on deep learning No
2017 Talking Drums: Generating drum grooves with neural networks No
2017 Singing voice separation with deep U-Net convolutional networks GitHub
2017 Music emotion recognition via end-to-end multimodal neural networks No
2017 Chord label personalization through deep learning of integrated harmonic interval-based representations No
2017 End-to-end musical key estimation using a convolutional neural network No
2017 MediaEval 2017 AcousticBrainz genre task: Multilayer perceptron approach No
2017 Classification-based singing melody extraction using deep convolutional neural networks No
2017 Multi-level and multi-scale feature aggregation using pre-trained convolutional neural networks for music auto-tagging No
2017 Multi-level and multi-scale feature aggregation using sample-level deep convolutional neural networks for music classification GitHub
2017 Sample-level deep convolutional neural networks for music auto-tagging using raw waveforms No
2017 A SeqGAN for Polyphonic Music Generation GitHub
2017 Harmonic and percussive source separation using a convolutional auto encoder No
2017 Stacked convolutional and recurrent neural networks for music emotion recognition No
2017 A deep learning approach to source separation and remixing of hiphop music No
2017 Music Genre Classification Using Masked Conditional Neural Networks No
2017 Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask GitHub
2017 Generating data to train convolutional neural networks for classical music source separation GitHub
2017 Monaural score-informed source separation for classical music using convolutional neural networks GitHub
2017 Multi-label music genre classification from audio, text, and images using deep features GitHub
2017 A deep multimodal approach for cold-start music recommendation GitHub
2017 Melody extraction and detection through LSTM-RNN with harmonic sum loss No
2017 Representation learning of music using artist labels No
2017 Toward inverse control of physics-based sound synthesis Website
2017 DNN and CNN with weighted and multi-task loss functions for audio event detection No
2017 Score-informed syllable segmentation for a cappella singing voice with convolutional neural networks GitHub
2017 End-to-end learning for music audio tagging at scale GitHub
2017 Designing efficient architectures for modeling temporal features with convolutional neural networks GitHub
2017 Timbre analysis of music audio signals with convolutional neural networks GitHub
2017 Deep learning and intelligent audio mixing No
2017 Deep learning for event detection, sequence labelling and similarity estimation in music signals No
2017 Music feature maps with convolutional neural networks for music genre classification No
2017 Automatic drum transcription for polyphonic recordings using soft attention mechanisms and convolutional neural networks GitHub
2017 Adversarial semi-supervised audio source separation applied to singing voice extraction No
2017 Taking the models back to music practice: Evaluating generative transcription models built using deep learning GitHub
2017 Generating nontrivial melodies for music as a service No
2017 Invariances and data augmentation for supervised music transcription GitHub
2017 Lyrics-based music genre classification using a hierarchical attention network GitHub
2017 A hybrid DSP/deep learning approach to real-time full-band speech enhancement GitHub
2017 Convolutional methods for music analysis No
2017 Extending temporal feature integration for semantic audio analysis No
2017 Recognition and retrieval of sound events using sparse coding convolutional neural network No
2017 A two-stage approach to note-level transcription of a specific piano No
2017 Reducing model complexity for DNN based large-scale audio classification No
2017 Audio spectrogram representations for processing with convolutional neural networks Website
2017 Unsupervised feature learning based on deep models for environmental audio tagging No
2017 Attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging GitHub
2017 Surrey-CVSSP system for DCASE2017 challenge task4 GitHub
2017 A study on LSTM networks for polyphonic music sequence modelling Website
2018 MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment GitHub
2018 Music transformer: Generating music with long-term structure No
2018 Music theory inspired policy gradient method for piano music transcription No
2019 Enabling factorized piano music modeling and generation with the MAESTRO dataset GitHub
2019 Generating Long Sequences with Sparse Transformers GitHub

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DL4M details

A human-readable table summarized version if displayed in the file dl4m.tsv. All details for each article are stored in the corresponding bib entry in dl4m.bib. Each entry has the regular bib field:

  • author
  • year
  • title
  • journal or booktitle

Each entry in dl4m.bib also displays additional information:

  • link - HTML link to the PDF file
  • code - Link to the source code if available
  • archi - Neural network architecture
  • layer - Number of layers
  • task - The proposed tasks studied in the article
  • dataset - The names of the dataset used
  • dataaugmentation - The type of data augmentation technique used
  • time - The computation time
  • hardware - The hardware used
  • note - Additional notes and information
  • repro - Indication to what extent the experiments are reproducible

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Code without articles

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Statistics and visualisations

  • 165 papers referenced. See the details in dl4m.bib. There are more papers from 2017 than any other years combined. Number of articles per year: Number of articles per year
  • If you are applying DL to music, there are 356 other researchers in your field.
  • 34 tasks investigated. See the list of tasks. Tasks pie chart: Tasks pie chart
  • 53 datasets used. See the list of datasets. Datasets pie chart: Datasets pie chart
  • 30 architectures used. See the list of architectures. Architectures pie chart: Architectures pie chart
  • 9 frameworks used. See the list of frameworks. Frameworks pie chart: Frameworks pie chart
  • Only 45 articles (27%) provide their source code. Repeatability is the key to good science, so check out the list of useful resources on reproducibility for MIR and ML.

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Advices for reviewers of dl4m articles

Please refer to the advice_review.md file.

How To Contribute

Contributions are welcome! Please refer to the CONTRIBUTING.md file.

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FAQ

How are the articles sorted?

The articles are first sorted by decreasing year (to keep up with the latest news) and then alphabetically by the main author's family name.

Why are preprint from arXiv included in the list?

I want to have exhaustive research and the latest news on DL4M. However, one should take care of the information provided in the articles currently in review. If possible you should wait for the final accepted and peer-reviewed version before citing an arXiv paper. I regularly update the arXiv links to the corresponding published papers when available.

How much can I trust the results published in an article?

The list provided here does not guarantee the quality of the articles. You should either try to reproduce the experiments described or submit a request to ReScience. Use one article's conclusion at your own risks.

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Acronyms used

A list of useful acronyms used in deep learning and music is stored in acronyms.md.

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Sources

The list of conferences, journals and aggregators used to gather the proposed materials is stored in sources.md.

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Contributors

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Other useful related lists and resources

Audio

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Music datasets

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Deep learning

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Cited by

If you use the information contained in this repository, please let us know! This repository is cited by:

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License

You are free to copy, modify, and distribute Deep Learning for Music (DL4M) with attribution under the terms of the MIT license. See the LICENSE file for details. This project use another projects and you may refer to them for appropriate license information :

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Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].