MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market.
Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

• Genre Networks: Success-based genre collaboration networks
• Genre Mapping: Genre mapping from Spotify genres to super-genres
• Artist Networks: Success-based artist collaboration networks
• Artists: Some artist data
• Hit Songs: Hit Song data and features
• Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20,
 title = {Detecting Collaboration Profiles in Success-based Music Genre Networks},
 author = {Gabriel P. Oliveira and 
      Mariana O. Silva and 
      Danilo B. Seufitelli and 
      Anisio Lacerda and
      Mirella M. Moro},
 booktitle = {21st International Society for Music Information Retrieval Conference}
 pages = {726--732},
 year = {2020}
}

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

Music Instruct (MI) Dataset

This is the dataset used to train and evaluate the MusiLingo model. This dataset contains Q&A pairs related to individual musical compositions, specifically tailored for open-ended music queries. It originates from the music-caption pairs in the MusicCaps dataset. The MI dataset was created through prompt engineering and applying few-shot learning techniques to GPT-4. More details on dataset generation can be found in our paper MusiLingo: Bridging Music… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/Music-Instruct.

The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation

A retro-futurist drum machine groove drenched in bubbly synthetic sound effects and a hint of an acid bassline.

The Song Describer Dataset (SDD) contains ~1.1k captions for 706 permissively licensed music recordings. It is designed for use in evaluation of models that address music-and-language (M&L) tasks such as music captioning, text-to-music generation and music-language retrieval. More information about the data, collection method and validation is provided in the paper describing the dataset.

If you use this dataset, please cite our paper:

The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation, Manco, Ilaria and Weck, Benno and Doh, Seungheon and Won, Minz and Zhang, Yixiao and Bogdanov, Dmitry and Wu, Yusong and Chen, Ke and Tovstogan, Philip and Benetos, Emmanouil and Quinton, Elio and Fazekas, György and Nam, Juhan, Machine Learning for Audio Workshop at NeurIPS 2023, 2023

README

Music Genre fMRI Dataset by Tomoya Nakai, Naoko Koide-Majima, and Shinji Nishimoto

References: Nakai, Koide-Majima, and Nishimoto (2021). Correspondence of categorical and feature-based representations of music in the human brain. Brain and Behavior. 11(1), e01936. https://doi.org/10.1002/brb3.1936

We measured brain activity using functional MRI while five subjects (“sub-001”, …, “sub-005”) listened to music stimuli of 10 different genres.

The entire folder consists of subject-wise subfolders (“sub-001”,…). Each subject’s folder contains the following subfolders: 1) anat: T1-weighted structural images 2) func: functional signals (multi-band echo-planar images)

Each subject performed 18 runs consisting of 12 training runs and 6 test runs. The training and test data were assigned with the following notations: Training data: sub-00*_task-Training_run-**_bold.json Test data: sub-00*_task-Test_run-**_bold.json

Each *_event.tsv file contains following information: onset: stimulus onset Duration: stimulus duration genre: genre type (out of 10 genres) track: index to identify the original track start: onset of excerpt from the original track (second) end: offset of excerpt from the original track (second)

The duration of all stimuli is 15s.　For each clip, 2 s of fade-in and fade-out effects were applied, and the overall signal intensity was normalized in terms of the root mean square.

For the training runs, the 1st stimulus (0-15s) is the same as the last stimulus of the previous run (600-615s). For the test runs, the1st stimulus (0-15s) is the same as the last stimulus of the same run (600-615s).

The original music stimuli (GTZAN dataset) can be found here: http://marsyas.info/downloads/datasets.html

Caution This dataset can be used for research purposes only. The data were anonymized, and users shall not perform analyses to re-identify individual subjects.

Unlock powerful insights with our custom music datasets, offering access to millions of records from popular music platforms like Spotify, SoundCloud, Amazon Music, YouTube Music, and more. These datasets provide comprehensive data points such as track titles, artists, albums, genres, release dates, play counts, playlist details, popularity scores, user-generated tags, and much more, allowing you to analyze music trends, listener behavior, and industry patterns with precision. Use these datasets to optimize your music strategies by identifying trending tracks, analyzing artist performance, understanding playlist dynamics, and tracking audience preferences across platforms. Gain valuable insights into streaming habits, regional popularity, and emerging genres to make data-driven decisions that enhance your marketing campaigns, content creation, and audience engagement. Whether you’re a music producer, marketer, data analyst, or researcher, our music datasets empower you with the data needed to stay ahead in the ever-evolving music industry. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, these datasets ensure seamless integration into your workflows.

This dataset was studied on Temporal Analysis and Visualisation of Music paper, in the following link:

       https://sol.sbc.org.br/index.php/eniac/article/view/12155

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

The MuSe (Music Sentiment) dataset contains sentiment information for 90,408 songs. We computed scores for the affective dimensions of valence, dominance and arousal, based on the user-generated tags that are available for each song via Last.fm. In addition, we provide artist and title metadata as well as a Spotify ID and a MusicBrainz ID, which allow researchers to extend the dataset with further metadata, such as genre or year.

Though the tags themselves cannot be included in the dataset, we include a jupyter notebook in our accompanying Github repository that demonstrates how to fetch the tags of a given song from the Last.fm API (Last.fm_API.ipynb)

We further include a jupyter notebook in the same repository that demonstrates how one might enrich the dataset with audio features using different endpoints of the Spotify API using the included Spotify IDs (spotify_API.ipynb). Please note that in its current form, the dataset only contains tentative spotify IDs for a subset (around 68%) of the songs.

This dataset is a collection of mel-spectrogram features extracted from Indian regional music containing the following languages:
Hindi, Gujarati, Marathi, Konkani, Bengali, Oriya, Kashmiri, Assamese, Nepali, Konyak, Manipuri, Khasi & Jaintia, Tamil, Malayalam, Punjabi, Telugu, Kannada.

Five recordings are collected for each language for four artists (2Male + 2Female) each. 2 artists out of 4 for each language are old veteran performers, and the remaining 2 are contemporary performers. Overall, the dataset includes 17 languages, 68 artists (34 Males and 34 Females). There are 340 recordings in the dataset, with a total duration of 29.3 hrs.

Mel-spectrogram is extracted from a 1-second segment with a 1/2 second sliding window for each song. Extracted mel-spectrogram for each segment is annotated with language, location, local_song_index, global_song_index, language_id, location_id, artist_id, gender_id.

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This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).

The dataset contains Electroencephalography (EEG) responses from 20 Indian participants, on 12 songs of different genres (from Indian Classical to Goth Rock). Each session indicates a song by its number.

For the experiment, the participants were indicated to close their eyes indicated by a single beep, and the song was presented to them on speakers. After listening to each song, a double beep was presented, asking them to open their eyes and rate their familiarity and enjoyment to the song. The responses were taken on a scale of 1 to 5, where 1 meant most familiar or most enjoyable, and 5 meant least familiar or least enjoyable.

This dataset is a collection of mel-spectrogram features extracted from Indian folk music containing the following 15 folk styles: Bauls, Bhavageethe, Garba, Kajri, Maand, Sohar, Tamang Selo, Veeragase, Bhatiali, Bihu, Gidha, Lavani, Naatupura Paatu, Sufi, Uttarakhandi.

The number of recordings varies from 16 to 50 in the mentioned folk styles representing the scarcity of availability of given folk styles on the Internet. There are at least 4 artists and a maximum of 22. Overall there are 125 artists (34 female + 91 male) in these 15 folk styles.

There is a total of 606 recordings in the dataset, with a total duration of 54.45 hrs. Mel-spectrogram is extracted from a 3-second segment with each song's 1/2 second sliding window. Extracted mel-spectrogram for each segment is annotated with folk_style, state, artist, gender, song, source, no_of_artists, folk_style_id, state_id, artist_id, gender_id.

This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

YouTube Music Hits Dataset

A collection of YouTube music video data sourced from Wikidata, focusing on videos with significant viewership metrics.

  Dataset Description





  Overview

24,329 music videos View range: 1M to 5.5B views Temporal range: 1977-2024

  Features

youtubeId: YouTube video identifier itemLabel: Video/song title performerLabel: Artist/band name youtubeViews: View count year: Release year genreLabel: Musical genre(s)

  View… See the full description on the dataset page: https://huggingface.co/datasets/akbargherbal/youtube-music-hits.

Dataset Card for MusicCaps

  Dataset Summary

The MusicCaps dataset contains 5,521 music examples, each of which is labeled with an English aspect list and a free text caption written by musicians. An aspect list is for example "pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead", while the caption consists of multiple sentences about the music, e.g., "A low sounding male voice is rapping over a fast paced drums… See the full description on the dataset page: https://huggingface.co/datasets/google/MusicCaps.

1) Data Introduction • The Music Dataset : 1950 to 2019 is a large-scale music dataset that includes various musical metadata such as sadness, danceability, loudness, acoustics, and more, along with song-specific lyrics from 1950 to 2019.

2) Data Utilization (1) Music Dataset : 1950 to 2019 has characteristics that: • The dataset consists of more than 30 numerical and categorical variables, including artist name, song name, release year, lyrics, song length, emotion (sad, etc.), danceability, volume, acousticity, instrument use, energy, and subject matter, and provides both lyric text and musical characteristics. (2) Music Dataset : 1950 to 2019 can be used to: • Analysis of Music Trends and Emotional Changes: By analyzing changes in major music characteristics such as sadness, danceability, and volume by year in time series, you can explore music trends and emotional changes by period. • Lyrics-based Natural Language Processing and Genre Classification: Using song-specific lyrics and metadata, it can be used for various text and music data fusion analysis such as natural language processing-based emotion analysis, music genre classification, and recommendation system.

Dataset Card for Music Genre

The Default dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 22,050 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16… See the full description on the dataset page: https://huggingface.co/datasets/ccmusic-database/music_genre.

This dataset is a collection of CSV files that include the time interval annotations of Nyas swara present in the compositions of Hindustani classical music. The selected music files comprise the files from the Nyas dataset [1] of the Dunya Corpus. The annotations are for the Nyas segments in the truncated music files (MBID included). In total, there are 2269 Nyaas segments. Overall, there are 67 recordings in the dataset, with a total duration of approx 100 minutes. Annotations for the Nyas segments comprise Alap sections, middle sections (medium tempo), and end sections (fast tempo).

References: [1]. Gulati, S., Serrà, J., Ganguli, K. K., & Serra, X. (2014). Landmark detection in Hindustani music melodies. In Proceedings of the International Computer Music Conference / Sound and Music Computing Conference (ICMC-SMC), pp. 1062- 1068. Athens, Greece.

This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).

The MUSIC (MUerte Subita en Insuficiencia Cardiaca) study is a prospective, multicentre, longitudinal study designed to assess risk predictors of cardiac mortality and sudden cardiac death (SCD) in ambulatory patients with chronic heart failure (CHF).

The study population consisted of 992 patients with CHF consecutively enrolled from the specialized HF clinics of eight University Spanish Hospitals between April 2003 and December 2004, and followed up for a median of 44 months (until November 2008). All patients had a 3-lead resting electrocardiogram (ECG), a 24 h, 2-(4%) or 3-lead (96%) Holter ECG, chest X-ray, echocardiography, and blood laboratory parameters performed at enrolment.

Primary outcomes were cardiac death, either sudden cardiac death (SCD) or pump failure death (PFD) at the end of the follow-up period.