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.

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   |

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.

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.

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.

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 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 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

"Folk Music" is an exceptional AI music dataset meticulously curated to preserve and celebrate the timeless beauty of folk music. This comprehensive collection focuses exclusively on folk music, capturing its essence through a rich assortment of melodies, rhythms, and cultural expressions.

By leveraging the diversity and quality of the folk music samples provided, "Folk Music" empowers machine learning applications to generate authentic and evocative folk compositions.

The detailed metadata associated with each sample in the dataset provides a wealth of contextual information, including instrument types, playing techniques, specific melodies, tempos, and dynamics. This allows for a deeper understanding and exploration of the intricate dynamics that make folk music unique.

This exceptional AI Music Dataset encompasses an array of vital data categories, contributing to its excellence. It encompasses Machine Learning (ML) Data, serving as the foundation for training intricate algorithms that generate musical pieces. Music Data, offering a rich collection of melodies, harmonies, and rhythms that fuel the AI's creative process. AI & ML Training Data continuously hone the dataset's capabilities through iterative learning. Copyright Data ensures the dataset's compliance with legal standards, while Intellectual Property Data safeguards the innovative techniques embedded within, fostering a harmonious blend of technological advancement and artistic innovation.

This dataset can also be useful as Advertising Data to generate music tailored to resonate with specific target audiences, enhancing the effectiveness of advertisements by evoking emotions and capturing attention. It can be a valuable source of Social Media Data as well. Users can post, share, and interact with the music, leading to increased user engagement and virality. The music's novelty and uniqueness can spark discussions, debates, and trends across social media communities, amplifying its reach and impact.

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.

In 2024, the total number of on-demand audio music streams in the United States hit an astronomical 1.4 trillion. This is an increase of nearly 100 billion from the previous year, reaching the highest number of streams.

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.

Our Cinematic Dataset is a carefully selected collection of audio files with rich metadata, providing a wealth of information for machine learning applications such as generative AI music, Music Information Retrieval (MIR), and source separation. This dataset is specifically created to capture the rich and expressive quality of cinematic music, making it an ideal training environment for AI models. This dataset, which includes chords, instrumentation, key, tempo, and timestamps, is an invaluable resource for those looking to push AI's bounds in the field of audio innovation.

Strings, brass, woodwinds, and percussion are among the instruments used in the orchestral ensemble, which is a staple of film music. Strings, including violins, cellos, and double basses, are vital for communicating emotion, while brass instruments, such as trumpets and trombones, contribute to vastness and passion. Woodwinds, such as flutes and clarinets, give texture and nuance, while percussion instruments bring rhythm and impact. The careful arrangement of these parts produces distinct cinematic soundscapes, making the genre excellent for teaching AI models to recognize and duplicate complicated musical patterns.

Training models on this dataset provides a unique opportunity to explore the complexities of cinematic composition. The dataset's emphasis on important cinematic components, along with cinematic music's natural emotional storytelling ability, provides a solid platform for AI models to learn and compose music that captures the essence of engaging storylines. As AI continues to push creative boundaries, this Cinematic Music Dataset is a valuable tool for anybody looking to harness the compelling power of music in the digital environment.

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.

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).

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).

Music

## Overview

Music is a dataset for classification tasks - it contains Instruments annotations for 1,421 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).

Music Sales by Format and Year

Sales data for music industry by format and year

By Charlie Hutcheson [source]

About this dataset

The Music Industry Sales by Format and Year dataset provides comprehensive information on the sales data for different music formats over a span of 40 years. The dataset aims to analyze and visualize the trends in music industry sales, specifically focusing on various formats and metrics used to measure these sales.

The dataset includes several key columns to facilitate data analysis, including Format which represents the different formats of music sales such as physical (CDs, vinyl) or digital (downloads, streaming). Additionally, the column Metric indicates the specific measure used to quantify the sales data, such as units sold or revenue generated. The column Year specifies the particular year in which the sales data was recorded.

To provide a more comprehensive understanding of each combination of format, metric, and year, additional columns are included. The Number of Records column denotes the total number of entries or records available for each unique combination. This information helps assess sample size reliability for further analysis. Moreover, there is an Actual Value column that presents precise numerical values representing the actual recorded sales figure corresponding to each format-metric-year combination.

This dataset is obtained from credible sources including RIAA's U.S Sales Database and was originally presented through a visualization by Visual Capitalist. It offers insights into historical trends in music industry sales patterns across different formats over four decades.

In order to enhance this dataset visual representation and further explore its potential insights accurately, it would be necessary to perform an exploratory analysis assessing: seasonal patterns within each format; changes in market share across multiple years; growth rates comparison between physical and digital formats; etc. These analyses can help identify emerging trends in consumer preferences along with underlying factors driving shifts in market dynamics. Additionally,the presentation media (such as charts or graphs) could benefit from improvements such as clearer labeling, more detailed annotations,captions that allow viewers to easily interpret visualized information,and arrangement providing a logical flow conducive to understanding the data

How to use the dataset

Dataset Overview

The dataset consists of the following columns:

• Format: The format of the music sales, such as physical (CDs, vinyl) or digital (downloads, streaming).
• Metric: The metric used to measure the sales, such as units sold or revenue generated.
• Year: The year in which the sales data was recorded.
• Number of Records: The number of records or entries for each combination of format, metric and year.
• Value (Actual): The actual value of the sales for each combination of format, metric and year.

Key Considerations

Before diving into analyzing this dataset, here are some key points to consider:

• Categorical Variables: Both Format and Metric columns contain categorical variables that represent different aspects related to music industry sales.
• Numeric Variables: Year, Number of Records, and Value (Actual) are numeric variables providing chronological information about record counts and actual sale values.

Interpreting Insights

To make meaningful interpretations using this data set:

Analyzing Different Formats:

• You can compare different formats' popularity over time based on units sold/revenue generated.
• Explore how digital formats have influenced physical format sales over time.
• Understand which formats have experienced growth or decline in specific years.

Evaluating Different Metrics:

• Analyze revenue trends compared to unit count trends for different formats each year.
• Identify metrics showing exceptional growth/decline compared across differing years/formats.

Understanding Sales Trends:

• Examine the relationship between the number of records and actual sales value each year.
• Identify periods where significant changes in music industry sales occurred.
• Observe trends and fluctuations based on different formats/metrics.

Visualizing Data

To enhance your analysis, create visualizations using this dataset:

• Time Series Analysis: Create line plots to visualize the trend in music sales for different formats over time.
• Comparative Analysis: Generate bar charts or grouped bar plots...

Explore 66,000 original instrumental tracks across various genres. Ideal for music recommendation systems, instrument recognition, and enhanced audio search capabilities.