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   |

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

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.

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.

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).

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

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.

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 contains 3,000 artificial music audio tracks with rich annotations. It is based on real instrument samples and generated by algorithmic composition with respect to music theory.

It provides full mixes of the songs as well as single instrument tracks. The midis used for generation are also available. The annotation files include: Onsets, Pitches, Instruments, Keys, Tempos, Segments, Melody instrument, Beats, and Chords.

A presentation paper was published open-access in EURASIP Journal on Audio, Speech, and Music Processing.

Current development and source code of the generator tool can be found on GitHub.

For a tiny version for demonstration and testing purposes see: zenodo.6771120

This dataset contains the multitask annotation for the Ragas in the Carnatic style of Indian classical music. The multitasks present in the dataset are the Swaras set, Melakarta set, Aaroh set, Avroh set, Janak/janya, and Raag Id. The music for extracting the mel-spectrogram feature is taken from the Dunya corpus [1]. The dataset contains 40 Ragas, and each contains 12 music recordings.

[1] Porter, Alastair, Mohamed Sordo, and Xavier Serra. "Dunya: A system for browsing audio music collections exploiting cultural context." Britto A, Gouyon F, Dixon S. 14th International Society for Music Information Retrieval Conference (ISMIR); 2013 Nov 4-8; Curitiba, Brazil.[place unknown]: ISMIR; 2013. p. 101-6.. International Society for Music Information Retrieval (ISMIR), 2013.

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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.

This is an improved version of the dataset originally referred to as SMD MIDI-Audio Piano Music. For more details, please visit the website: https://www.audiolabs-erlangen.de/resources/MIR/SMD/midi

Saarland Music Data provides audio recordings along with perfectly synchronized MIDI files for various piano pieces. The pieces were performed by students of the Hochschule für Musik Saar on a hybrid acoustic/digital piano Yamaha Disklavier. The Disklavier allows for capturing key and pedal movements of the piano while playing. This information, which can be stored in a MIDI file, yields an accurate annotation of the corresponding audio recording in form of a symbolic description of all played musical note events. The SMD MIDI-Audio pairs constitute a valuable dataset for various music analysis tasks such as music transcription, performance analysis, music synchronization, audio alignment, or source separation.

All performances were recorded in the studios of the Hochschule für Musik Saar, played by students of piano classes of different levels, on a Yamaha Disklavier model DCFIIISM4PRO. Using two cardioid-condenser microphones fixed over the resonating body of the piano, all performances were directly recorded into Steinberg Cubase 4. Except for trimming the beginnings and ends of the recordings, no further post-processing (filters, effects) was applied to the musical material. From each Cubase project, an audio file (44.1 kHz, stereo) as well as a synchronized standard MIDI file (SMF) were exported. Besides these files, we also provide the audio files as WAV (22.05 kHz, mono) and the MIDI files encoded as CSV files and as WAV files (22.05 kHz, mono) rendered using the Software synthesizer FluidSynth.

SMD MIDI-Audio Piano Music (V1) contains the following data:

• wav_44100_stereo: Audio file (44.1 kHz, stereo)
• wav_22050_mono: Audio file (22.05 kHz, mono)
• midi: MIDI file
• csv: Export of note events from MIDI file into CSV format
• midi_wav_22050_mono: MIDI file rendered as audio file (22.05 kHz, mono)

If you publish results obtained using this dataset, please cite:

Meinard Müller, Verena Konz, Wolfgang Bogler, Vlora Arifi-Müller: Saarland Music Data (SMD). In Late-Breaking and Demo Session of the 12th International Conference on Music Information Retrieval (ISMIR), 2011. [pdf] [bib]

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.

Music emotion recognition delineates and categorises the spectrum of emotions expressed within musical compositions by conducting a comprehensive analysis of fundamental attributes, including melody, rhythm, and timbre. This task is pivotal for the tailoring of music recommendations, the enhancement of music production, the facilitation of psychotherapeutic interventions, and the execution of market analyses, among other applications. The cornerstone is the establishment of a music emotion recognition dataset annotated with reliable emotional labels, furnishing machine learning algorithms with essential training and validation tools, thereby underpinning the precision and dependability of emotion detection. The Music Emotion Dataset with 2496 Songs (Memo2496) dataset, comprising 2496 instrumental musical pieces annotated with valence-arousal (VA) labels and acoustic features, is introduced to advance music emotion recognition and affective computing. The dataset is meticulously annotated by 30 music experts proficient in music theory and devoid of cognitive impairments, ensuring an unbiased perspective. The annotation methodology and experimental paradigm are grounded in previously validated studies, guaranteeing the integrity and high calibre of the data annotations.Memo2496 R1 updated by Qilin Li @12Feb20251. Remove some unannotated music raw data, now the music contained in MusicRawData.zip file are all annotated music.2. The ‘Music Raw Data.zip’ file on FigShare has been updated to contain 2496 songs, consistent with the corpus described in the manuscript. The metadata fields on “Title”, “Contributing Artists”, “Genre”, and/or “Album” have been removed to ensure the songs remain anonymous.3. Adjusted the file structure, now the files on FigShare are placed in folders named ‘Music Raw Data’, ‘Annotations’, ‘Features’, and ‘Data Processing Utilities’ to reflect the format of the Data Records section in the manuscript.Memo2496 R2 updated by Qilin Li @14Feb2025The source of each song's download platform has been added in ‘songs_info_all.csv’ to enable users to search within the platform itself if necessary. This approach aims to balance the privacy requirements of the data with the potential needs of the dataset's users.

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.

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).