Music Classification

MagnaTagATune dataset contains 25,863 music clips. Each clip is a 29-seconds-long excerpt belonging to one of the 5223 songs, 445 albums and 230 artists. The clips span a broad range of genres like Classical, New Age, Electronica, Rock, Pop, World, Jazz, Blues, Metal, Punk, and more. Each audio clip is supplied with a vector of binary annotations of 188 tags. These annotations are obtained by humans playing the two-player online TagATune game. In this game, the two players are either presented with the same or a different audio clip. Subsequently, they are asked to come up with tags for their specific audio clip. Afterward, players view each other’s tags and are asked to decide whether they were presented the same audio clip. Tags are only assigned when more than two players agreed. The annotations include tags like ’singer’, ’no singer’, ’violin’, ’drums’, ’classical’, ’jazz’. The top 50 most popular tags are typically used for evaluation to ensure that there is enough training data for each tag. There are 16 parts, and researchers comonnly use parts 1-12 for training, part 13 for validation and parts 14-16 for testing. Source: Brains on Beats Audio Source: http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset

VocalSet is a a singing voice dataset consisting of 10.1 hours of monophonic recorded audio of professional singers demonstrating both standard and extended vocal techniques on all 5 vowels. Existing singing voice datasets aim to capture a focused subset of singing voice characteristics, and generally consist of just a few singers. VocalSet contains recordings from 20 different singers (9 male, 11 female) and a range of voice types. VocalSet aims to improve the state of existing singing voice datasets and singing voice research by capturing not only a range of vowels, but also a diverse set of voices on many different vocal techniques, sung in contexts of scales, arpeggios, long tones, and excerpts.

EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in pop piano music, to facilitate research on various tasks related to music emotion. The dataset contains 1,087 music clips from 387 songs and clip-level emotion labels annotated by four dedicated annotators.

ATEPP is a dataset of expressive piano performances by virtuoso pianists. The dataset contains 11677 performances (~1000 hours) by 49 pianists and covers 1580 movements by 25 composers. All of the MIDI files in the dataset come from the piano transcription of existing audio recordings of piano performances. Scores in MusicXML format are also available for around half of the tracks. The dataset is organized and aligned by compositions and movements for comparative studies. Source: ATEPP: A Dataset of Automatically Transcribed Expressive Piano Performances

GiantMIDI-Piano contains 10,854 unique piano solo pieces composed by 2,786 composers. GiantMIDI-Piano contains 34,504,873 transcribed notes, and contains metadata information of each music piece. Source: GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music

The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability. To tackle these problems, we present GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks. Particularly, (1) we collect 80.59 hours of high-quality songs, forming the largest recorded singing dataset; (2) 20 professional singers across nine languages offer diverse timbres and styles; (3) we provide controlled comparison and phoneme-level annotations of six singing techniques, helping technique modeling and control; (4) GTSinger offers realistic music scores, assisting real-world musical composition; (5) singing voices are accompanied by manual phoneme-to-audio alignments, global style labels, and 16.16 hours of paired speech for various singing tasks. Moreover, to facilitate the use of GTSinger, we conduct four benchmark experiments: technique-controllable singing voice synthesis, technique recognition, style transfer, and speech-to-singing conversion.

A MIDI dataset of 500 4-part chorales generated by the KS_Chorus algorithm, annotated with results from hundreds of listening test participants, with 500 further unannotated chorales.

Lyra is a dataset of 1570 traditional and folk Greek music pieces that includes audio and video (timestamps and links to YouTube videos), along with annotations that describe aspects of particular interest for this dataset, including instrumentation, geographic information and labels of genre and subgenre, among others. Source: A Dataset for Greek Traditional and Folk Music: Lyra Image Source: https://arxiv.org/pdf/2211.11479v1.pdf

1. A unique dataset comprising multimodal creative and designed documents containing images with corresponding captions paired with music based on around 50mood/themes. 2. Motivation: To enhance user experience and to increase accessibility to wider community, motivate research in cross-modal retrieval field. 3. Use case: Music Retrieval for designed documents, Music augmentation for multimodal designed documents, Design image retrieval for music based on mood/themes.

This dataset contains annotations for 5000 music files on the following music properties: * Melodiousness * Articulation * Rhythmic stability * Rhythmic complexity * Dissonance * Tonal stability * Modality The annotations were given by musicians and collected through a crowd-sourcing platform (Toloka).

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XMIDI is a comprehensive, large-scale symbolic music dataset that includes accurate emotion and genre labels, consisting of 108,023 MIDI files. The average duration of the music pieces is approximately 176 seconds, yielding a total dataset length of around 5,278 hours.