ybayle/awesome-deep-learning-music：与应用于音乐的深度学习相关的文章列表

技术解读

以下内容由 GitRead 中文社区编辑团队基于项目 README 与官方资料整理，结合中文开发者的常见使用场景补充技术选型建议。具体以项目官方文档为准。

一份清单撑起的领域地图：拆解 awesome-deep-learning-music

项目背景与痛点

“深度学习 + 音乐”是一个典型的交叉领域困境：论文散落在 ISMIR、ICASSP、Interspeech、NeurIPS 与 arXiv 之间，同一件事有五六个名字——音乐生成、符号音乐生成、audio synthesis、music continuation，初学者很难判断它们是不是同一个问题的不同叫法。

这个仓库解决的核心问题，是发现成本，而不是技术问题。它把散落的研究按任务族聚拢成一份可 Ctrl+F 的清单，让研究者在选题冷启动时，不必先花两周时间搞清楚“哪些人在做这件事”。对于需要快速建立领域全景的工程师和研究生来说，它的价值相当于一张手工绘制的地图草图：不精确，但足够指路。

核心技术揭秘

必须说清楚一点：这不是一个软件项目，没有构建脚本、没有测试、没有 API。它的“架构”是知识组织架构，技术栈本质上就是 Markdown + Git + PR 流程。真正的设计决策集中在三处：

第一，以任务为一级索引。 README 按分类、聚类、生成、推荐、源分离、转录、合成等任务大类分区，而不是按年份或会议。这个选择的含义很明确：使用者的检索路径是“我要解决什么问题”，而不是“某年某会发了什么”。任务优先的分类法天然贴近工程视角。

第二，以论文为原子条目，以链接为价值载体。 每条记录压缩到标题、作者、年份、原文链接，能附带代码仓库的就附上。信息密度被刻意压低，换来的是单页可扫读性和极低的维护成本——新增一条只需要一行文本。

第三，人工策展替代自动抓取。 更新路径是提交者发 PR、维护者审核合并。这是一套单点信任模型：精度高、噪声低，但吞吐量取决于维护者的时间。

graph TD A[新论文 / 新工具出现] --> B{"属于深度学习与音乐交叉?"} B -->|"否"| C[排除，不收录] B -->|"是"| D{"归入哪个任务族?"} D -->|"分类识别"| E[Classification 分区] D -->|"生成合成"| F[Generation 分区] D -->|"分离转录"| G[Separation / Transcription 分区] D -->|"推荐检索"| H[Recommendation 分区] E --> I[校验元数据：作者年份原文链接代码] F --> I G --> I H --> I I --> J{"链接与代码是否可用?"} J -->|"可用"| K[提交 PR 并由维护者合并] J -->|"失效"| L[退回补充或搁置] K --> M[README 渲染为可检索清单] M --> N[研究者按任务定位基线模型]

隐含的技术难点在于分类体系的滞后性。领域每年都会冒出新的任务命名（音乐描述生成、文本到音乐、可控生成），而清单的分类树是静态的，新条目只能往旧分区里塞，久而久之边界就模糊了。这是所有人工策展型知识库的共同债务。

功能亮点与差异

和 Papers with Code 比，它的覆盖面小两个数量级，但精度高得多——后者靠自动关联论文与代码，常把仓库链接错误关联；前者靠人眼确认，至少不出明显错误。和 ISMIR 官方 proceedings 比，它跨会议聚合，并且把代码链接一并收进来，而不只是提供 PDF 索引。和通用的 awesome-machine-learning 比，它足够垂直，不会让你在一千条条目里翻找音频相关的那二十条。

真正有差异化的设计是把论文、数据集、教程、工具放在同一个知识空间里。这意味着你可以顺着一条链走完“读论文 → 找数据集 → 找开源实现”的完整路径，而不是在三个站点之间来回跳。这是清单类项目里少见的闭环意识。

应用场景与落地建议

适用场景很明确：课题选题前的领域扫描、复现某类基线模型时的入口、团队内部技术雷达的初始输入。

生产环境使用有三点必须注意。其一，不要把它当综述用。它没有覆盖度声明，也没有质量分级，经典工作与三流论文混排在一起，取舍责任在使用者。其二，链接腐化是常态，建议 fork 一份内部镜像，挂上链接检查 CI（如 lychee）定期跑，否则半年后大量 arXiv 旧版链接会失效。其三，如果要接进内部检索系统，最好先把 README 转成 YAML/JSON 的结构化条目，手工补上任务标签与年份字段，这样才可能做过滤和排序——纯文本清单的可机读性几乎为零。

综合评价

优点：垂直度高，筛选克制，单页可扫读；维护者本身在该领域内，选品有判断力；论文与代码、数据集的并置带来了实用的检索闭环。

缺点：只有一个维护者，bus factor 为 1，更新节奏完全依赖个人意愿；条目不区分“奠基性工作”和“最新预印本”，缺乏质量分层；分类体系对新兴任务的响应明显滞后；没有收录标准文档，读者无法判断遗漏是有意还是疏漏。

一句话总结：它是进入这个领域性价比最高的入口，但绝不是终点。把它当地图用，不要当路标用。

关于

GitHub 原始描述（英文）

List of articles related to deep learning applied to music

awesome awesome-list unicorns list lists resources deeplearning deep-learning deep-neural-networks neural-network

MIT License
3.0k Stars
347 Forks
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描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

3836

价值分

1918

活跃状态

活跃

GitHub

语言占比

Python

TeX

贡献者

⚠️ This repo is unmaintained. While the info are still relevant, contributions to keep it up to date are welcome! A good starting point are the articles referenced here: https://github.com/ybayle/awesome-deep-learning-music/issues/5

Deep Learning for Music (DL4M)

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.

DL4M summary
DL4M details
Code without articles
Statistics and visualisations
Advices for reviewers of dl4m articles
How To Contribute
FAQ
Acronyms used
Sources
Contributors
Other useful related lists
Cited by
License

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	The MUSDB18 corpus for music separation	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
2021	DadaGP: a Dataset of Tokenized GuitarPro Songs for Sequence Models	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

167 papers referenced. See the details in dl4m.bib. There are more papers from 2017 than any other years combined. Number of articles per year:
If you are applying DL to music, there are 364 other researchers in your field.
34 tasks investigated. See the list of tasks. Tasks pie chart:
55 datasets used. See the list of datasets. Datasets pie chart:
30 architectures used. See the list of architectures. Architectures pie chart:
9 frameworks used. See the list of frameworks. Frameworks pie chart:
Only 47 articles (28%) 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

Yann Bayle (GitHub) - Instigator and principal maintainer
Vincent Lostanlen (GitHub)
Keunwoo Choi (GitHub)
Bob L. Sturm (GitHub)
Stefan Balke (GitHub)
Jordi Pons (GitHub)
Mirza Zulfan (GitHub) for the logo
Devin Walters
https://github.com/LegendJ

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Audio

DL4MIR tutorial with keras - Tutorial for Deep Learning on Music Information Retrieval by Thomas Lidy
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
DL4MIR tutorial - Python tutorials for learning to solve MIR tasks with DL
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
MIR community on Slack - Link to subscribe to the MIR community’s Slack
Unclassified list of MIR-related links - Cory McKay’s list of various links on DL, MIR, …
MIRDL - Unmaintained list of DL articles for MIR from Jordi Pons
WWW 2018 Challenge - Learning to Recognize Musical Genre on the FMA dataset
Music generation with DL - List of resources on music generation with deep learning
Auditory Scene Analysis - Book about the perceptual organization of sound by Albert Bregman, the “father of Auditory Scene Analysis”.
- Demonstrations of Auditory Scene Analysis - Audio demonstrations, which illustrate examples of auditory perceptual organization.

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Music datasets

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Deep learning

DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers -
Model Convertors - Convertors for DL frameworks and backend
Deep architecture genealogy - Genealogy of DL architectures
Deep Learning as an Engineer - Slides from Jan Schlüter
Awesome Deep Learning - General deep learning resources
Awesome Deep Learning Resources - Papers regarding deep learning and deep reinforcement learning
Awesome RNNs - RNNs code, theory and applications
Cheatsheets AI - Cheat Sheets for Keras, neural networks, scikit-learn,…
DL PaperNotes - Summaries and notes on general deep learning research papers
General lists
Echo State Network
DL in NLP - Best practices for using neural networks by Sebastian Ruder
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
ML from scratch - Python implementations of ML models and algorithms from scratch from Data Mining to 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
GitHub repo for sklearn add-on for imbalanced learning - ML in uneven datasets
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

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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 :

Readme checklist - To build an universal Readme.
Pylint - To clean the python code.
Numpy - To manage python structure.
Matplotlib - To plot nice figures.
Bibtexparser - To deal with the bib entries.

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awesome-deep-learning-music

技术解读

一份清单撑起的领域地图：拆解 awesome-deep-learning-music

项目背景与痛点

核心技术揭秘

功能亮点与差异

应用场景与落地建议

综合评价

相关仓库

关于

简要分析

语言占比

贡献者

Deep Learning for Music (DL4M)

Table of contents

DL4M summary

DL4M details

Code without articles

Statistics and visualisations

Advices for reviewers of dl4m articles

How To Contribute

FAQ

Acronyms used

Sources

Contributors

Other useful related lists and resources

Audio

Music datasets

Deep learning

Cited by

License

评论