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   |

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 represents real-time data collected from strategically placed microphones and sensors in a music education platform. It contains information about students' performances during music lessons and the associated teaching effectiveness. The dataset aims to simulate the analysis of musical performances, including the accuracy of pitch, rhythm, and dynamics, as well as the evaluation of student engagement and teacher feedback.

Features: Timestamp:

Description: The exact date and time when the data was collected. Type: DateTime Example: 2024-12-20 10:00:00 Sensor ID:

Description: Unique identifier for each microphone or sensor monitoring the performance. Type: Categorical Example: Sensor_001, Sensor_002 Student ID:

Description: Identifier for the student whose performance is being monitored. Type: Categorical Example: Student_001, Student_002 Instrument Type:

Description: The type of musical instrument being played during the lesson. Type: Categorical Example: Piano, Guitar, Violin Pitch (Hz):

Description: The frequency (in Hertz) of the sound produced by the instrument. Type: Numerical (Continuous) Example: 440 Hz (A4 note) Rhythm (BPM):

Description: The tempo of the music being played, measured in beats per minute (BPM). Type: Numerical (Continuous) Example: 120 BPM Dynamics (dB):

Description: The loudness or intensity of the sound produced by the instrument, measured in decibels (dB). Type: Numerical (Continuous) Example: 75 dB Note Duration (s):

Description: The length of time for which each note is held during the performance. Type: Numerical (Continuous) Example: 0.5 seconds Pitch Accuracy (%):

Description: The accuracy with which the pitch produced matches the intended pitch, expressed as a percentage. Type: Numerical (Continuous) Example: 95% Rhythm Accuracy (%):

Description: The accuracy with which the rhythm (tempo and timing) matches the intended pattern, expressed as a percentage. Type: Numerical (Continuous) Example: 100% Teaching Effectiveness Rating:

Description: A rating given to evaluate the effectiveness of the teacher’s instruction based on student performance. Type: Categorical (Ordinal) Example: 5/5, 4/5 Lesson Type:

Description: The type of lesson or session being conducted (e.g., beginner, advanced, or practice). Type: Categorical Example: Beginner Lesson, Advanced Lesson, Practice Session Student Engagement Level:

Description: The level of student engagement during the lesson, measured as either High, Medium, or Low. Type: Categorical Example: High, Medium, Low Teacher Feedback:

Description: Feedback provided by the teacher based on the student's performance. Type: Categorical Example: Good rhythm, Needs improvement, Excellent performance Environmental Factors:

Description: The environmental conditions in which the lesson is conducted, which could influence the quality of the performance data (e.g., background noise). Type: Categorical Example: Quiet, Slight Background Noise, Noisy Student Progress (%):

Description: A measure of student progress over time, expressed as a percentage of improvement in skills. Type: Numerical (Continuous) Example: 85%, 60% Target (Performance Evaluation):

Description: A classification based on performance score. Students are classified as either High Performance or Low Performance based on their pitch and rhythm accuracy. Type: Categorical Example: High Performance, Low Performance Target Column Definition: Performance Score: The average of pitch and rhythm accuracy. If the performance score exceeds 90%, the student is classified as High Performance; otherwise, they are classified as Low Performance.

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.

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.

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.

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.

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

References:

1. 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

2. Nakai, Koide-Majima, and Nishimoto (2022). Music genre neuroimaging dataset. Data in Brief. 40, 107675. https://doi.org/10.1016/j.dib.2021.107675

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:

1. Training data: sub-00*_task-Training_run-**_bold.json

2. Test data: sub-00*_task-Test_run-**_bold.json

Each *_event.tsv file contains following information:

1. Onset: stimulus onset

2. Genre: genre type (out of 10 genres)

3. Track: index to identify the original track

4. Start: onset of excerpt from the original track (second)

5. 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).

Preprocessed data are available from Zenodo (https://doi.org/10.5281/zenodo.8275363). Experimental stimuli can be generated using GTZAN_Preprocess.py included in the same repository.

The original music stimuli (GTZAN dataset) can be found here: https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification

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.

The large-scale MUSIC-AVQA dataset of musical performance contains 45,867 question-answer pairs, distributed in 9,288 videos for over 150 hours. All QA pairs types are divided into 3 modal scenarios, which contain 9 question types and 33 question templates. Finally, as an open-ended problem of our AVQA tasks, all 42 kinds of answers constitute a set for selection.

Music

## Overview

Music is a dataset for object detection tasks - it contains Musical Instrument annotations for 230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

JVS-MuSiC is a Japanese multispeaker singing-voice corpus called "JVS-MuSiC" with the aim to analyze and synthesize a variety of voices. The corpus consists of 100 singers' recordings of the same song, Katatsumuri, which is a Japanese children's song. It also includes another song that is different for each singer.

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 includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

This dataset is a patched version of The Taste & Affect Music Database by D. Guedes et al. It is a set of captions that describe 100 musical pieces and associate with them gustatory keywords on the basis of Guedes findings.

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.

The RWC (Real World Computing) Music Database is a copyright-cleared music database (DB) that is available to researchers as a common foundation for research. It contains around 100 complete songs with manually labeled section boundaries. For the 50 instruments, individual sounds at half-tone intervals were captured with several variations of playing styles, dynamics, instrument manufacturers and musicians.

According to a study on music consumption worldwide in 2022, ******* generations tended to find new songs via music apps and social media, while ***** generations also used the radio as a format to discover new audio content.

MusicCaps is a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts. For each 10-second music clip, MusicCaps provides:

1) A free-text caption consisting of four sentences on average, describing the music and

2) A list of music aspects, describing genre, mood, tempo, singer voices, instrumentation, dissonances, rhythm, etc.