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awesome-deep-learning-music
List of articles related to deep learning applied to music
https://github.com/ybayle/awesome-deep-learning-music
Last synced: 4 days ago
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DL4M summary
- Creation by refinement: A creativity paradigm for gradient descent learning networks
- Creation by refinement: A creativity paradigm for gradient descent learning networks
- The representation of pitch in a neural net model of chord classification
- Algorithms for music composition by neural nets: Improved CBR paradigms
- A connectionist approach to algorithmic composition
- Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing
- Automatic source identification of monophonic musical instrument sounds
- A machine learning approach to musical style recognition
- Recognition of music types
- Musical networks: Parallel distributed perception and performance
- Multi-phase learning for jazz improvisation and interaction
- A supervised learning approach to musical style recognition
- Finding temporal structure in music: Blues improvisation with LSTM recurrent networks
- Neural networks for note onset detection in piano music
- A convolutional-kernel based approach for note onset detection in piano-solo audio signals
- Unsupervised feature learning for audio classification using convolutional deep belief networks
- Audio musical genre classification using convolutional neural networks and pitch and tempo transformations
- Automatic musical pattern feature extraction using convolutional neural network
- Audio-based music classification with a pretrained convolutional network
- Moving beyond feature design: Deep architectures and automatic feature learning in music informatics
- Local-feature-map integration using convolutional neural networks for music genre classification
- Learning sparse feature representations for music annotation and retrieval
- Unsupervised learning of local features for music classification
- Multiscale approaches to music audio feature learning
- Musical onset detection with convolutional neural networks
- Deep content-based music recommendation
- The munich LSTM-RNN approach to the MediaEval 2014 Emotion In Music task
- Deep learning for music genre classification
- Recognition of acoustic events using deep neural networks
- Deep image features in music information retrieval
- From music audio to chord tablature: Teaching deep convolutional networks to play guitar
- Improved musical onset detection with convolutional neural networks
- Boundary detection in music structure analysis using convolutional neural networks
- Improving content-based and hybrid music recommendation using deep learning
- A deep representation for invariance and music classification
- Auralisation of deep convolutional neural networks: Listening to learned features
- Downbeat tracking with multiple features and deep neural networks
- Music boundary detection using neural networks on spectrograms and self-similarity lag matrices
- Classification of spatial audio location and content using convolutional neural networks
- Deep learning, audio adversaries, and music content analysis
- Deep learning and music adversaries - mgr) |
- Automatic instrument recognition in polyphonic music using convolutional neural networks
- A software framework for musical data augmentation
- A deep bag-of-features model for music auto-tagging
- Music-noise segmentation in spectrotemporal domain using convolutional neural networks
- Musical instrument sound classification with deep convolutional neural network using feature fusion approach
- Environmental sound classification with convolutional neural networks
- Exploring data augmentation for improved singing voice detection with neural networks
- Singer traits identification using deep neural network
- A hybrid recurrent neural network for music transcription
- An end-to-end neural network for polyphonic music transcription
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Folk music style modelling by recurrent neural networks with long short term memory units - rnn) |
- Deep neural network based instrument extraction from music
- A deep neural network for modeling music
- An efficient approach for segmentation, feature extraction and classification of audio signals
- Text-based LSTM networks for automatic music composition
- Towards playlist generation algorithms using RNNs trained on within-track transitions
- Automatic tagging using deep convolutional neural networks
- DeepBach: A steerable model for Bach chorales generation
- Bayesian meter tracking on learned signal representations
- Deep learning for music
- Learning temporal features using a deep neural network and its application to music genre classification
- On the potential of simple framewise approaches to piano transcription
- Feature learning for chord recognition: The deep chroma extractor
- A fully convolutional deep auditory model for musical chord recognition
- Event localization in music auto-tagging
- Deep convolutional networks on the pitch spiral for musical instrument recognition
- SampleRNN: An unconditional end-to-end neural audio generation model
- Robust audio event recognition with 1-max pooling convolutional neural networks
- Experimenting with musically motivated convolutional neural networks
- Singing voice melody transcription using deep neural networks
- Singing voice separation using deep neural networks and F0 estimation
- Learning to pinpoint singing voice from weakly labeled examples
- Note onset detection in musical signals via neural-network-based multi-ODF fusion
- Music transcription modelling and composition using deep learning - rnn) |
- Convolutional neural network for robust pitch determination
- Deep convolutional neural networks and data augmentation for acoustic event detection
- Gabor frames and deep scattering networks in audio processing
- Vision-based detection of acoustic timed events: A case study on clarinet note onsets
- Deep learning techniques for music generation - A survey
- JamBot: Music theory aware chord based generation of polyphonic music with LSTMs
- XFlow: 1D <-> 2D cross-modal deep neural networks for audiovisual classification
- Machine listening intelligence
- Monoaural audio source separation using deep convolutional neural networks
- A tutorial on deep learning for music information retrieval
- A comparison on audio signal preprocessing methods for deep neural networks on music tagging
- Transfer learning for music classification and regression tasks
- An evaluation of convolutional neural networks for music classification using spectrograms
- Large vocabulary automatic chord estimation using deep neural nets: Design framework, system variations and limitations
- Basic filters for convolutional neural networks: Training or design?
- Ensemble Of Deep Neural Networks For Acoustic Scene Classification
- Music signal processing using vector product neural networks
- Transforming musical signals through a genre classifying convolutional neural network
- Audio to score matching by combining phonetic and duration information
- Interactive music generation with positional constraints using anticipation-RNNs
- Deep rank-based transposition-invariant distances on musical sequences
- GLSR-VAE: Geodesic latent space regularization for variational autoencoder architectures
- Deep convolutional neural networks for predominant instrument recognition in polyphonic music
- CNN architectures for large-scale audio classification
- Talking Drums: Generating drum grooves with neural networks
- Singing voice separation with deep U-Net convolutional networks - Ming/UNet-VocalSeparation-Chainer) |
- Music emotion recognition via end-to-end multimodal neural networks
- Chord label personalization through deep learning of integrated harmonic interval-based representations
- End-to-end musical key estimation using a convolutional neural network
- MediaEval 2017 AcousticBrainz genre task: Multilayer perceptron approach
- Classification-based singing melody extraction using deep convolutional neural networks
- Multi-level and multi-scale feature aggregation using pre-trained convolutional neural networks for music auto-tagging
- Multi-level and multi-scale feature aggregation using sample-level deep convolutional neural networks for music classification
- Sample-level deep convolutional neural networks for music auto-tagging using raw waveforms
- A SeqGAN for Polyphonic Music Generation - music) |
- Harmonic and percussive source separation using a convolutional auto encoder
- Stacked convolutional and recurrent neural networks for music emotion recognition
- A deep learning approach to source separation and remixing of hiphop music
- Music Genre Classification Using Masked Conditional Neural Networks
- Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask - Mim/mss_pytorch) |
- Generating data to train convolutional neural networks for classical music source separation
- Monaural score-informed source separation for classical music using convolutional neural networks
- Multi-label music genre classification from audio, text, and images using deep features
- A deep multimodal approach for cold-start music recommendation
- Representation learning of music using artist labels
- Toward inverse control of physics-based sound synthesis
- DNN and CNN with weighted and multi-task loss functions for audio event detection
- Score-informed syllable segmentation for a cappella singing voice with convolutional neural networks
- End-to-end learning for music audio tagging at scale - audio-tagging-at-scale-models) |
- The MUSDB18 corpus for music separation
- Deep learning and intelligent audio mixing
- Deep learning for event detection, sequence labelling and similarity estimation in music signals
- Music feature maps with convolutional neural networks for music genre classification
- Automatic drum transcription for polyphonic recordings using soft attention mechanisms and convolutional neural networks
- Adversarial semi-supervised audio source separation applied to singing voice extraction
- Taking the models back to music practice: Evaluating generative transcription models built using deep learning - rnn) |
- Generating nontrivial melodies for music as a service
- Invariances and data augmentation for supervised music transcription
- Lyrics-based music genre classification using a hierarchical attention network
- A hybrid DSP/deep learning approach to real-time full-band speech enhancement
- Convolutional methods for music analysis
- Extending temporal feature integration for semantic audio analysis
- A two-stage approach to note-level transcription of a specific piano
- Reducing model complexity for DNN based large-scale audio classification
- Audio spectrogram representations for processing with convolutional neural networks
- Unsupervised feature learning based on deep models for environmental audio tagging
- Attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging
- Surrey-CVSSP system for DCASE2017 challenge task4
- A study on LSTM networks for polyphonic music sequence modelling
- MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment
- Music transformer: Generating music with long-term structure
- Music theory inspired policy gradient method for piano music transcription
- Enabling factorized piano music modeling and generation with the MAESTRO dataset
- Generating Long Sequences with Sparse Transformers
- DadaGP: a Dataset of Tokenized GuitarPro Songs for Sequence Models - bots/dadaGP) |
- Creation by refinement: A creativity paradigm for gradient descent learning networks
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Designing efficient architectures for modeling temporal features with convolutional neural networks
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Creation by refinement: A creativity paradigm for gradient descent learning networks
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Classification of spatial audio location and content using convolutional neural networks
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep neural network based instrument extraction from music
- A deep neural network for modeling music
- Learning temporal features using a deep neural network and its application to music genre classification
- A fully convolutional deep auditory model for musical chord recognition
- Monaural score-informed source separation for classical music using convolutional neural networks
- Music feature maps with convolutional neural networks for music genre classification
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Neural networks for note onset detection in piano music
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- A supervised learning approach to musical style recognition
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Improving content-based and hybrid music recommendation using deep learning
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Automatic musical pattern feature extraction using convolutional neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Generating data to train convolutional neural networks for classical music source separation
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
- Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network
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Code without articles
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Other useful related lists and resources
- Video talk from Ron Weiss - Ron Weiss (Google) Talk on Training neural network acoustic models on waveforms
- Slides on DL4M - A personal (re)view of the state-of-the-art by [Jordi Pons](http://www.jordipons.me/)
- Awesome Python Scientific Audio - Python resources for Audio and Machine Learning
- ISMIR resources - Community maintained list
- ISMIR Google group - Daily dose of general MIR
- Awesome Python - Audio section of Python resources
- Awesome Web Audio - WebAudio packages and resources
- Awesome Music - Music softwares
- Awesome Music Production - Music creation
- The Asimov Institute - 6 deep learning tools for music generation
- DLM Google group - Deep Learning in Music group
- Unclassified list of MIR-related links - [Cory McKay](http://www.music.mcgill.ca/~cmckay/)'s list of various links on DL, MIR, ...
- MIRDL - Unmaintained list of DL articles for MIR from [Jordi Pons](http://www.jordipons.me/)
- Auditory Scene Analysis - Book about the perceptual organization of sound by [Albert Bregman](https://en.wikipedia.org/wiki/Albert_Bregman), the "father of [Auditory Scene Analysis](https://en.wikipedia.org/wiki/Auditory_scene_analysis)".
- Demonstrations of Auditory Scene Analysis - Audio demonstrations, which illustrate examples of auditory perceptual organization.
- Teaching MIR
- Wikipedia's list of datasets for machine learning research
- Datasets for deep learning
- Awesome public datasets
- DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
- Deep Learning as an Engineer - Slides from Jan Schlüter
- 
- CNN overview - Stanford Course
- Dilated Recurrent Neural Networks - How to improve RNNs?
- Encoder-Decoder in RNNs - How Does Attention Work in Encoder-Decoder Recurrent Neural Networks
- On the use of DL - Misc fun around DL
- Comparison of DL frameworks - Presentation describing the different existing frameworks for DL
- ELU > ReLU - Article describing the differences between ELU and ReLU
- Reinforcement Learning: An Introduction - Book about reinforcement learning
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Video on DL from Nando de Freitas, Scott Reed and Oriol Vinyals - Deep Learning: Practice and Trends (NIPS 2017 Tutorial, parts I & II)
- Article "Are GANs Created Equal? A Large-Scale Study" - Actually comparing DL algorithms
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Black-box optimization - There are other optimization algorithms than just gradient descent
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Estimating Optimal Learning Rate - Blog post on the learning rate optimisation
- Battle of the Deep Learning frameworks - DL frameworks comparison and evolution
- Encoder-Decoder in RNNs - How Does Attention Work in Encoder-Decoder Recurrent Neural Networks
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- Yann Bayle - Instigator and principal maintainer
- GitHub
- Keunwoo Choi
- Bob L. Sturm
- Stefan Balke - balke))
- Jordi Pons
- GitHub
- Devin Walters
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