Context

What makes us, humans, able to tell apart two songs of different genres? Maybe you have ever been in the diffcult situation to explain show it sounds the music style that you like to someone. Then, could an automatic genre classifcation be possible?

Content

Each row is an electronic music song. The dataset contains 100 song for each genre among 23 electronic music genres, they were the top (100) songs of their genres on November 2016. The 71 columns are audio features extracted of a two random minutes sample of the file audio. These features have been extracted using pyAudioAnalysis (https://github.com/tyiannak/pyAudioAnalysis).

The song names for each track is exactly the same as in the https://www.kaggle.com/caparrini/electronic-music-features-201611-beatporttop100 dataset.

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).

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.

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.

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… See the full description on the dataset page: https://huggingface.co/datasets/google/MusicCaps.

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   |

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

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.

The Free Music Archive (FMA) is a large-scale dataset for evaluating several tasks in Music Information Retrieval. It consists of 343 days of audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies.

There are four subsets defined by the authors:

Full: the complete dataset, Large: the full dataset with audio limited to 30 seconds clips extracted from the middle of the tracks (or entire track if shorter than 30 seconds), Medium: a selection of 25,000 30s clips having a single root genre, Small: a balanced subset containing 8,000 30s clips with 1,000 clips per one of 8 root genres.

The official split into training, validation and test sets (80/10/10) uses stratified sampling to preserve the percentage of tracks per genre. Songs of the same artists are part of one set only.

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.

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} }

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.

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.

Neural Audio Fingerprint Dataset

(c) 2021 by Sungkyun Chang https://github.com/mimbres/neural-audio-fp This dataset includes all music sources, background noise and impulse-reponses (IR) samples that have been used in the work "https://arxiv.org/abs/2010.11910">"Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning".

  Format:

16-bit PCM Mono WAV, Sampling rate 8000 Hz

  Description:

/… See the full description on the dataset page: https://huggingface.co/datasets/arch-raven/music-fingerprint-dataset.

Context

This dataset contains 80K music album reviews by the users of rateyourmusic.com.

Content

The dataset was acquired by scraping on May 2022. It contains 79922 album reviews and ratings (if available).

The scraper can be found at this GitHub repo.

Acknowledgements

The album chart of albums from which the reviews were scraped from can be found here.

Inspiration

This dataset can be used to practice NLP.

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).

The MUSIC dataset used in this paper for spectral CT reconstruction.

Our Moroccan Music Dataset is a comprehensive collection of audio samples representing six prominent genres of Moroccan music. The dataset is structured into six folders, each dedicated to a specific genre: Chaabi, Rap, Andalusian, Rai, Gnawa, and Imazighn.

Each folder contains a diverse range of audio samples showcasing the unique characteristics and styles of the respective genre. From the infectious beats of Chaabi to the powerful lyrics of Rap, the soulful melodies of Andalusian, the energetic rhythms of Rai, the spiritual chants of Gnawa, and the traditional sounds of Imazighn, the dataset encompasses the rich musical landscape of Morocco.

thumbnail : https://www.freepik.com/free-photo/still-life-art-photography-concept-with-antique-samovar-violin-isolated-black-background_13499739.htm#query=moroccan%20music&position=24&from_view=search&track=ais

Context

he music industry has a well-developed market with a global annual revenue around $15 billion. The recording industry is highly competitive and is dominated by three big production companies which make up nearly 82% of the total annual album sales.

Artists are at the core of the music industry and record labels provide them with the necessary resources to sell their music on a large scale. A record label incurs numerous costs (studio recording, marketing, distribution, and touring) in exchange for a percentage of the profits from album sales, singles and concert tickets.

Unfortunately, the success of an artist's release is highly uncertain: a single may be extremely popular, resulting in widespread radio play and digital downloads, while another single may turn out quite unpopular, and therefore unprofitable.

Knowing the competitive nature of the recording industry, record labels face the fundamental decision problem of which musical releases to support to maximize their financial success.

How can we use analytics to predict the popularity of a song? In this assignment, we challenge ourselves to predict whether a song will reach a spot in the Top 10 of the Billboard Hot 100 Chart.

Taking an analytics approach, we aim to use information about a song's properties to predict its popularity. The dataset songs.csv consists of all songs which made it to the Top 10 of the Billboard Hot 100 Chart from 1990-2010 plus a sample of additional songs that didn't make the Top 10. This data comes from three sources: Wikipedia, Billboard.com, and EchoNest.

The variables included in the dataset either describe the artist or the song, or they are associated with the following song attributes: time signature, loudness, key, pitch, tempo, and timbre.

Content

Here's a detailed description of the variables:

year = the year the song was released songtitle = the title of the song artistname = the name of the artist of the song songID and artistID = identifying variables for the song and artist timesignature and timesignature_confidence = a variable estimating the time signature of the song, and the confidence in the estimate loudness = a continuous variable indicating the average amplitude of the audio in decibels tempo and tempo_confidence = a variable indicating the estimated beats per minute of the song, and the confidence in the estimate key and key_confidence = a variable with twelve levels indicating the estimated key of the song (C, C#, . . ., B), and the confidence in the estimate energy = a variable that represents the overall acoustic energy of the song, using a mix of features such as loudness pitch = a continuous variable that indicates the pitch of the song timbre_0_min, timbre_0_max, timbre_1_min, timbre_1_max, . . . , timbre_11_min, and timbre_11_max = variables that indicate the minimum/maximum values over all segments for each of the twelve values in the timbre vector (resulting in 24 continuous variables) Top10 = a binary variable indicating whether or not the song made it to the Top 10 of the Billboard Hot 100 Chart (1 if it was in the top 10, and 0 if it was not)

Acknowledgements

MITx ANALYTIX