AI composition
recognition competition

With the development
of artificial neural networks, many algorithms based on deep learning have been proposed and used in industry and academia for music generation. As a result, there are a series of coming legal issues such as the standards and procedures to identify the infringement of intellectual property rights. Exemplar cases include illegal application of AI composing algorithms, considering intentional plagiarism as incident of AI composing algorithms, the accuracy and objectiveness of professional authentication and the dependence of litigation procedure on professional identification. As a preparation of such legal challenges, it is necessary to develop an objective way to evaluate music melodic similarity and describe music melodies. Such techniques can potentially enable digital forensic investigations for musical intellectual property issues.


This data challenge aims to identify music melodies generated by artificial intelligence algorithms. The challenge provides a development dataset that contains melodies in two different music style and generated by a number of music generation algorithms. A month later, an evaluation dataset will be released. The final ranking of the challenge is determined by the AUC of judging the source of the melody in the evaluation dataset.

There is no enrolment process in this data challenge. Any participants following the guidance described in this page with a successful submission will be considered a participant. The participant is expected to use the developed algorithms to label how melodies in the evaluation dataset are generated. The submission should include a technical report on the developed algorithm and a CSV file containing labelling information, and should be submitted via the submission system of Conference on Sound and Music Technology (CSMT) at the following page: (

The technical report should follow the requirement of manuscript of CSMT (as a bilingual conference, both Chinese and English technical report is acceptable) and should be uploaded to arXiv before the deadline of challenge submission. When the result is submitted, please make sure the track of the system is selected as “Data Challenge”.

Participants need to submit source code for algorithm validation (an NDA could be signed if necessary). The participants would be disqualified if they refuse to submit source code upon request. Any program language is allowed. The complete code should include: function for reading the evaluation dataset, main algorithms to generate system output, and listing the packages used in the code.

Participants are not allowed to make subjective judgments of the evaluation data, which means label the music by the human musician. The participants would be disqualified if making subjective judgments

Development dataset

The development dataset contains 6000 MIDI files with monophonic melodies generated by artificial intelligence algorithms. The tempo is between the 68bpm and 118bpm (beat per minute). The length of each melody is 8 bars, and the melody does not necessarily include complete phrase structures. There are two datasets with different music styles used as the training dataset of a number of algorithms, where the melodies in the development dataset are generated. The MIDI files in the development dataset are named in the following format.


Evaluation dataset

The evaluation dataset contains 4000 MIDI files with exact configurations of development dataset with two exceptions: 1) A number of melodies composed by human composers are added, some of which are published and some of which are composed for this competition. The music style of the human composed melodies are the same as the styles of music in the training set. This was confirmed by musicologists. 2) There are a number of melodies generated by algorithms with minor algorithmic or parameter changes compared to the algorithms in the development dataset.


The final ranking of the challenge is determined by the AUC of judging the source of the melody in the evaluation dataset.

Data download

AI composition recognition competition development dataset.

AI composition recognition competition evaluation dataset.

External data resources

Use of external data is allowed under the following conditions:

• Only open source data that can be referenced is allowed. External data refers to public datasets or pre-trained models.

• The data must be public and freely available before 15th August 2020.

• The list of external data sources used in training must be properly cited and referenced in the technical report.


Please submit one ZIP file ONLY including an technical report (following instruction of CSMT submissions) and a sub ZIP file containing source code.

• Technical report explaining the method in sufficient detail(*.pdf), The report needs to be anonymous.

• A sub ZIP file includes: 1. A complete system code that can be run; 2. System output file (*.csv), System output should be presented as a single text-file (in CSV format, with a header row) containing the likelihood of the melody being human or machine composed (float type) for each midi file in the evaluation set. The higher score, the music is more likely composed by AI, the lower the score, the music is more likely composed by human.

NOTICE: Multiple CSV files, effectively multiple attempts, are allowed in a single ZIP file but the attached source code should be able to produce all CSV files.

                                    file_name          score
                                    0.mid              0.8
                                    1.mid              0.25
                                    2.mid              0.6
                                    3.mid              0.1

To avoid overlapping labels among all submitted systems, use following way to form your label:

[first name]_[last name]_[Abbreviation of institute of the corresponding author]

For example the systems would have the following labels:



Make sure your zip-package follows provided file naming convention and directory structure. The zip package example can be downloaded here.

                                              Zip-package root, Task submissions
                        └───Hua_Li_BUPT_technical_report.pdf           Technical report
                               Hua_Li_BUPT_code_1                        Task System code
                              (Any language is allowed)
                               Hua_Li_BUPT_output_1.csv                  Task System output
                               Hua_Li_BUPT_code_2                        Task System code
                               Hua_Li_BUPT_output_2.csv                  Task System output

Baseline system

The baseline system is an AutoEncoder. The encoder and the decoder employ four fully connected layers respectively. In the training process, the AutoEncoder is trained using AI generated music, in that case, the decoder learns how to generate outputs following the distribution of the AI generated music. In the inference process, the AI generated music and the human composed music get their outputs through the trained AutoEncoder, the reconstruction error of the AI generated music will be lower than the reconstruction error of the human composed, due to the training process above.



Rank Submission File Author Affiliation Method/Technical Report External AUC
1 You_Li_NYU_Zhuowen_Lin_
You Li New York University Stacked LSTM Reddit 0.8812
2 You_Li_NYU_Zhuowen_Lin_
You Li New York University Stacked bi-LSTM Reddit 0.8032
3 Yang_Deng_NetEase_output Yang Deng Netease Cloud Music Math Non 0.7626
4 Mingshuo_Ding_PKU_Yinghao_Ma_CMU_output Mingshuo Ding Peking University Transformer Reddit 0.6821
5 Jinyue_Guo_PATech_output_2step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.6259
6 Yiting_Xia_CSMT_challenge_output Yiting Xia Southern University of Science and Technology LSTM + Logistic Regression Self Collected MIDI 0.5996
7 Jinyue_Guo_PATech_output_1step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.5649
8 Jiaxing_Yu_ZJU_output Jiaxing Yu Zhejiang University AutoEncoder Self-generated 0.5561
9 Baseline Baseline Baseline Baseline Non 0.5494
10 Jinyue_Guo_PATech_output_5step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.5325
11 Jinyue_Guo_PATech_output_10step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.5263
12 Wang_Yuxiang_code Yuxiang Wang Beijing Institute of Techonology SVM Nottingham 0.5134
13 Jinyue_Guo_PATech_output_20step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.4336
14 Jinyue_Guo_PATech_output_50step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.4251
15 Jinyue_Guo_PATech_output_100step Jinyue Guo Ping An Insurance (Group) Company of China BLSTM Wikifonia 0.3985


Contact us: Jing Yinji, challenge coordinator, Beijing University of Posts and Telecommunications, mail:

Do I need to classify styles?
There is no need to classify styles. This challenge only asks participants to judge whether the music is composed by human or AI, but we would like to remind the participants that there are different styles in the dataset.


July 15, 2020

Development Dataset Released

August 15, 2020

Evaluation Dataset Released

September 15, 2020

Submission Due

September 15 - October 15, 2020

Validation of winners and randomly selected participants

October 1 - October 20, 2020

Review technical report

October 20, 2020

Acceptance Notice of Technical Report for CSMT 2020

November 4, 2020 (duration meeting)

Challenge Result Announcement and Presentation of Accepted Technical Reports on CSMT 2020


Zhang Ru

Professor, Beijing University of Posts and Telecommunications

George Fazekas

Senior Lecturer, Queen Mary University of London

Li Zijin

Associate Professor, China Conservatory of Music

Zhu Yidan

Secretary General, Beijing Acoustics Society

Zhou Wei

Founding Partner of Beijing Zhongwen Law Firm

Li Shengchen

Lecturer, Beijing University of Posts and Telecommunications