2006 |
MIR from a Singing Voice |
- |
Japanese |
children |
Music information retrieval (MIR) from singing voice, Lyrics recognition, Melody verification |
EURASIP |
|
2007 |
Accurate TempoEstimationBasedon Harmonic+NoiseDecomposition |
- |
Latin, Greek, Western |
- |
- |
EURASIP |
|
2011 |
Semantic structures of timbre emerging from social and acoustic descriptions of music |
- |
British, American, Finnish |
Rock, Alternative, Pop |
Alternative, Blues, Classical, Electronic, Folk, Gospel, Heavy, Hip-Hop, Iskelmä, Jazz, Pop, Rock, Soul, Soundtrack, World |
EURASIP |
|
2015 |
SIFT-based local spectrogram image descriptor: a novel feature for robust music identification |
177.35 |
- |
pop, rock, disco, jazz, country music, classical music, folk |
Music Tagging, Genre Classification |
EURASIP |
Genre wise distribution not given |
2021 |
Time–frequency scattering accurately models auditory similarities between instrumental playing techniques |
3 |
- |
folk, classical |
Annotated |
EURASIP |
|
2023 |
AAM: a dataset of Artificial Audio Multitracks |
- |
Western |
pop |
Music Transcription (MIDI) |
EURASIP |
|
2012 |
SCORE-INFORMEDTRANSCRIPTIONFORAUTOMATICPIANOTUTORING |
- |
Western |
classical |
Music Transcription (MIDI) |
EUSIPCO |
|
2015 |
Low latency real time |
745 |
- |
rock |
Music Annotation and Sequence Tagging |
EUSIPCO |
|
2015 |
Semantic description of sound quality of violins |
- |
Italian |
classical |
- |
EUSIPCO |
|
2016 |
Folk dance recognition in video |
1 |
Greece |
folk |
Music Classification |
EUSIPCO |
|
2016 |
Chroma Estimation using Powered Euclidean Distance |
- |
Western |
classical |
Music Transcription (MIDI) |
EUSIPCO |
|
2016 |
Digital Music Lab |
- |
Western & World Music |
jazz, rock, roll, reggae, classical, blues, folk |
- |
EUSIPCO |
|
2016 |
Greek Folk Music Classification |
3.46 |
Asia Minor, Pontus, Dodecanese, Epirus, Peloponnese, West Macedonia, Northeastern Aegean Islands, Crete, Greek |
classical, pop, jazz |
Music Classification |
EUSIPCO |
|
2016 |
Rhythm Transcription of MIDI Performances |
0.27 |
Japanese |
pop |
Music Transcription (MIDI) |
EUSIPCO |
|
2016 |
Unsupervised Singing Voice Detection |
6 |
Latin, Flamenco |
flamenco |
Annotated |
EUSIPCO |
|
2016 |
Unsupervised Singing Voice Detection |
1 |
Greece |
folk |
Annotated |
EUSIPCO |
|
2018 |
Beat tracking using RNN |
- |
Greece |
Folk |
Annotated |
EUSIPCO |
|
2018 |
Piano Legato Pedal Onset |
- |
Western |
Classical |
Music Transcription (MIDI) |
EUSIPCO |
|
2019 |
Deep Neural Network Based Poetic Meter |
- |
Carnatic |
Classical |
Music Classification |
EUSIPCO |
|
2019 |
Spectral Complexity Reduction Of Music Signals |
- |
- |
Orchestra, instrumental, classical, ensemble |
Music Transcription (MIDI) |
EUSIPCO |
|
2020 |
Signal Denoising Using a New Class of Robust |
1.01 |
- |
- |
Music Transcription (MIDI) |
EUSIPCO |
|
2021 |
Handling Structural Mismatches in Real time Opera |
8 |
Italian |
Orchestra, instrumental, classical, ensemble |
Music Transcription (MIDI) |
EUSIPCO |
|
2021 |
Violinist identification based on vibrato features |
- |
- |
Orchestra, instrumental, classical, ensemble |
Annotated |
EUSIPCO |
|
2022 |
An Open-Access System for Long-Range Chainsaw |
10.34 |
- |
- |
Music Classification |
EUSIPCO |
|
2022 |
Transfer Learning for Violinist Identification |
18 |
Western |
concerto, violin, classical |
Music Classification |
EUSIPCO |
|
2023 |
DAACI-VoDAn |
45.53 |
- |
- |
Music Tagging, Genre Classification |
EUSIPCO |
|
2023 |
MSED-4k |
8 |
- |
classical |
Music Classification |
EUSIPCO |
|
2008 |
Musical Sound Separation |
- |
Western |
classical, quartet, JSBach |
Music Transcription (MIDI), Segmentaion |
ICASSP |
|
2009 |
Cultural style based Music Classification |
100 |
Western, Chinese, Japanese, Indian, Arabic, African |
classical, folk ,traditional |
Music Classification |
ICASSP |
|
2012 |
Feature Extraction for Automatic Recognition |
0.077 |
- |
jazz |
Feature Extraction, Music Classification |
ICASSP |
|
2012 |
Feature Extraction for Automatic Recognition |
0.072 |
- |
pop |
Feature Extraction, Music Classification |
ICASSP |
|
2012 |
Feature Extraction for Automatic Recognition |
0.082 |
- |
opera |
Feature Extraction, Music Classification |
ICASSP |
|
2013 |
Open Source Drum Transcription System |
- |
Western |
drums, beats, blues, rock, easy listening, ballad |
Music Classification |
ICASSP |
|
2013 |
TRIOS |
- |
Western |
Mozart, Schubert, classical, piano |
Annotated |
ICASSP |
|
2015 |
Ikala |
3 |
Chinese |
pop |
Annotated |
ICASSP |
|
2015 |
MER |
13.4 |
Western |
pop |
Emotion Recognition |
ICASSP |
|
2017 |
AudioSet |
24.48 |
American, Latin |
pop |
Annotated |
ICASSP |
|
2017 |
AudioSet |
21.51 |
Western |
hip-hop |
Annotated |
ICASSP |
|
2017 |
AudioSet |
23.54 |
American |
Rock |
Annotated |
ICASSP |
|
2017 |
AudioSet |
14.26 |
American |
R&B, Jazz |
Annotated |
ICASSP |
|
2017 |
AudioSet |
8.14 |
America |
Soul, gospel, R&B, Jazz |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
8.97 |
Jamaicasoul |
Reggae |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
15.36 |
American |
Country |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
11.29 |
USA |
Funk |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
6.17 |
- |
Folk |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
5.8 |
Middle Eastern |
Traditional |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
13.69 |
USA |
Jazz |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
11.49 |
- |
funk, pop, soul |
ANnotated |
ICASSP |
|
2017 |
AudioSet |
19.09 |
Western |
classical |
Annotated |
ICASSP |
|
2017 |
AudioSet |
108.22 |
- |
Electronic |
Annotated |
ICASSP |
|
2017 |
AudioSet |
69.02 |
Latin |
tango, rumba, salsa, samba, bossa nova |
Annotated |
ICASSP |
|
2017 |
AudioSet |
13.24 |
USA |
Blues |
Annotated |
ICASSP |
|
2017 |
AudioSet |
9.78 |
- |
children |
Annotated |
ICASSP |
|
2017 |
AudioSet |
8.78 |
- |
New age, easy listening |
Annotated |
ICASSP |
|
2017 |
AudioSet |
5.83 |
Africa |
- |
Annotated |
ICASSP |
|
2017 |
AudioSet |
10.9 |
East Asian, South Asian, Central Asian |
- |
Annotated |
ICASSP |
|
2017 |
AudioSet |
4.78 |
Jamaica |
ska |
Annotated |
ICASSP |
|
2017 |
AudioSet |
4.87 |
- |
Traditional |
Annotated |
ICASSP |
|
2017 |
AudioSet |
8.39 |
- |
Indie |
Annotated |
ICASSP |
|
2019 |
Intonation : Dataset of Quality Vocal Performances |
- |
- |
- |
- |
ICASSP |
|
2019 |
Affective Correspondence between Music & Image |
270 |
- |
- |
Annotated, Emotion Recognition |
ICASSP |
|
2019 |
Sound Event Classifiers |
1.39 |
Western |
Acoustic Guitar |
Classification |
ICASSP |
|
2019 |
Sound Event Classifiers |
1.83 |
Western |
Bass Guitar |
Classification |
ICASSP |
|
2019 |
Sound Event Classifiers |
2.29 |
Western |
Piano |
Classification |
ICASSP |
|
2019 |
Sound Event Classifiers |
1.09 |
Western |
Drums |
Classification |
ICASSP |
|
2019 |
Multi task learning for frame level |
3.17 |
- |
- |
Music Transcription(MIDI) |
ICASSP |
|
2020 |
Enhancing the labelling of audio samples |
2.92 |
- |
Instrumental(banjo, bass, accordion, cello, clarinest, cymbals, drums, flute, guitar, mandolin, organ, piano, saxophone, synthesizer, trombone, trumpet, ukulele, violin, voice) |
Classification |
ICASSP |
|
2020 |
VGG Sound |
12.083 |
Western Classical & European Music |
classical |
Classification |
ICASSP |
|
2020 |
VGG Sound |
9.38 |
USA |
Hammond Organ, Electric Guitar, Banjo, Bass Guitar, Drum Kit, Mandolin, Ukulele |
Classification |
ICASSP |
|
2020 |
VGG Sound |
0.56 |
South Asian |
classical |
Classification |
ICASSP |
|
2020 |
VGG Sound |
0.42 |
Central American |
Marimba |
Classification |
ICASSP |
|
2020 |
VGG Sound |
0.28 |
East Asian |
Erhu |
Classification |
ICASSP |
|
2020 |
VGG Sound |
0.28 |
Carribean |
Steelpan |
Classification |
ICASSP |
|
2020 |
VGG Sound |
0.28 |
Cuban |
Congas |
Classification |
ICASSP |
|
2020 |
VGG Sound |
0.28 |
SouthEast Asian |
Gong |
Classification |
ICASSP |
|
2021 |
Melon Playlist Dataset |
3300 |
Western |
- |
Playlist Formation |
ICASSP |
|
2021 |
Melon Playlist Dataset |
2475 |
Korean |
- |
Playlist Formation |
ICASSP |
|
2021 |
MIRST-500 |
30 |
Chinese |
Pop |
Singing Transcription |
ICASSP |
|
2022 |
Score to Audio Music Performance |
6.5 |
Western |
classical |
Score to Audio |
ICASSP |
|
2022 |
Polyphonic Guitar Music |
2 |
Western |
guitar |
Towards Automatic Trascription |
ICASSP |
|
2023 |
JazzNet |
26000 |
- |
Jazz |
Fundamental Piano Patterns Labelled |
ICASSP |
|
2017 |
Universal Music translation Network |
- |
Western |
Classical, sonata, chorales |
Music ztranslation |
ICLR |
|
2017 |
MusicNet |
34.13 |
Western |
Classical, sonata |
Music Segmentation, Segment Detection |
ICLR |
|
2019 |
MAESTRO |
172.3 |
Western |
Classical, sonata |
Music Segmentation, Segment Detection |
ICLR |
|
2019 |
TimbreTron |
1.75 |
Western |
Classical, sonata, bach, piano |
Timbre Transfer |
ICLR |
|
2019 |
TimbreTron |
2 |
Western |
Classical, sonata, harpsichord |
Timbre Transfer |
ICLR |
|
2019 |
TimbreTron |
3 |
Western |
Classical, sonata, violin |
Timbre Transfer |
ICLR |
|
2019 |
TimbreTron |
0.75 |
Western |
Classical, sonata, flute |
Timbre Transfer |
ICLR |
|
2021 |
Dance Revolution |
2.25 |
- |
Ballet |
Music to dance |
ICLR |
|
2021 |
Dance Revolution |
5 |
American |
hip-hop |
Music to dance |
ICLR |
|
2021 |
Dance Revolution |
6 |
Japanese |
pop |
Music to dance |
ICLR |
|
2005 |
SIgnal to Score Music Transcription |
1 |
Western |
classical, vocal |
Score Transcription |
IJCAI |
|
2005 |
Jazz Music Transcription |
- |
- |
Jazz |
Transcription |
IJCAI |
|
2007 |
Computational Model for Melody Identification |
0.31 |
Western |
Classical |
Music Transcription(MIDI) |
IJCAI |
|
2019 |
SynthNet |
1.4 |
Western |
Classical |
Music Segmentation, Segment Detection |
IJCAI |
|
2020 |
Pattern Based Music Generation |
- |
- |
acid jazz, soul, funk |
Unannotated |
IJCAI |
|
2020 |
Pattern Based Music Generation |
- |
- |
hip-hop, progressive trance |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
6.6 |
American |
hip-hop |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
5.94 |
- |
rock |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
4.95 |
- |
disco |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
3.3 |
- |
jazz |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
2.31 |
American |
country |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
0.33 |
- |
Metal |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
2.31 |
- |
Disco |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
7.26 |
Korean |
pop |
Labelled |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
109.76 |
- |
Jazz |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
82.32 |
- |
rock |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
109.76 |
Korean |
pop |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
89.18 |
American |
Blues |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
61.74 |
- |
Disco |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
109.76 |
- |
hip-hop |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
6.86 |
- |
Metal |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
68.6 |
American |
Country |
Unannotated |
IJCAI |
|
2021 |
Automatic Translation of Music-to-Dance for In-Game Characters |
48.02 |
- |
Classical |
UnAnnotated |
IJCAI |
|
2018 |
Audio Aligned Jazz Harmony Dataset |
7 |
American, Cuban |
Jazz |
UnAnnotated |
ISMIR |
|
2018 |
Tempo Estimation of EDM |
22.13 |
- |
edm |
Annotated |
ISMIR |
|
2018 |
ConcertStitch |
2.66 |
American |
rock |
Annotated |
ISMIR |
|
2018 |
BPS-FH |
- |
Western |
classical, sonata |
Annotated |
ISMIR |
|
2018 |
Bach’s Well Tempered Clavier |
- |
Western |
classical, sinfonias |
Annotated |
ISMIR |
|
2018 |
Music Score Images |
- |
Western |
classical, sonatas |
Music Transcription(MIDI) |
ISMIR |
|
2018 |
Genre Agnostic Key Classification |
12.53 |
- |
classical |
Annotated |
ISMIR |
|
2018 |
GuitarSet |
3 |
- |
rock, jazz, funk |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
12.5 |
- |
rock |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
9.72 |
- |
Electronic |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
6.95 |
- |
Experimental |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
5.56 |
- |
Classical |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
4.18 |
- |
Folk |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
2.78 |
USA |
Jazz |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
2.78 |
- |
pop |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
2.22 |
- |
hip-hop |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
0.83 |
USA |
country |
Annotated |
ISMIR |
|
2018 |
OpenMic-18 |
0.82 |
USA |
blues |
Annotated |
ISMIR |
|
2018 |
NES Music DB |
46.1 |
- |
- |
Music Transcription(MIDI) |
ISMIR |
|
2018 |
1.2 Million Song Dataset |
- |
Latin, Indian, Western |
Jazz, rock, blues, rap, disco, classical |
Audio Tagging |
ISMIR |
|
2018 |
MIDI VAE |
300.8 |
American |
Jazz |
UnAnnotated |
ISMIR |
|
2018 |
MIDI VAE |
391.5 |
Western |
Classical |
UnAnnotated |
ISMIR |
|
2018 |
MIDI VAE |
273.7 |
- |
pop |
UnAnnotated |
ISMIR |
|
2018 |
A data driven approach to mid-level perceptual musical feature modeling |
20.83 |
- |
pop, rock, jazz, classical, electronic, hip-hop, rap |
Annotated |
ISMIR |
Genre wise division not given |
2018 |
VocalSet |
10.1 |
Western |
acapella, classical |
Singer Identification |
ISMIR |
|
2018 |
Tranferring Style of Homophonic Music |
- |
American |
jazz |
UnAnnotated |
ISMIR |
|
2018 |
Skeleton |
3.13 |
Western |
classical |
Annotated |
ISMIR |
|
2018 |
Musical Texture and Expressivity Features |
7.5 |
- |
- |
Annotated |
ISMIR |
|
2018 |
JSymbolic 2.2 |
- |
Western, American |
jazz, rap, classical, rock, blues |
Annotated |
ISMIR |
|
2018 |
Relevance of musical pieces |
- |
Western |
classical, quartet |
Annotated |
ISMIR |
|
2019 |
CBF Dataset |
- |
Chinese |
folk, flute |
Annotated |
ISMIR |
|
2019 |
BandNet |
- |
American |
beattles rock |
Music Transcription(MIDI) |
ISMIR |
|
2019 |
Automatic Choreography Generation |
6.26 |
Korean |
pop |
UnAnnotated |
ISMIR |
|
2019 |
Predicting musical future |
- |
Latin |
pop |
UnAnnotated |
ISMIR |
|
2019 |
AIST Dance |
- |
American |
hip-hop,jazz,pop |
Genre Classification |
ISMIR |
|
2019 |
AcousticBrainz |
- |
- |
All genres |
Genre Classification |
ISMIR |
Genre wise distribution not mentioned |
2019 |
Improving singing aid |
1 |
Japanese |
childrens songs |
Voice conversion |
ISMIR |
|
2019 |
Unsupervised Drum Transcription |
249 |
Western |
pop, rock |
Annotated |
ISMIR |
|
2019 |
MAST |
- |
Turkey |
- |
Automated Assessment |
ISMIR |
|
2019 |
DaTacos |
- |
- |
rock, pop, metal, jazz |
Annotated |
ISMIR |
|
2019 |
DaTacos |
1495.83 |
- |
rock, pop, metal, jazz |
Annotated |
ISMIR |
|
2019 |
DB-MTC |
5 |
Western |
classical |
Annotated |
ISMIR |
|
2019 |
Fiddle Corpus |
0.5 |
Scotland |
folk |
Annotated |
ISMIR |
|
2019 |
Sambaset |
40.5 |
Brazil |
Samba |
Genre Classification |
ISMIR |
|
2019 |
Query by Blending |
361.63 |
- |
- |
UnAnnotated |
ISMIR |
|
2019 |
Traverse Latent Spaces |
- |
Scottish, Irish |
folk |
UnAnnotated |
ISMIR |
|
2019 |
IL Lauro Secco |
2 |
Italian |
madrigals, classical |
Annotated |
ISMIR |
|
2019 |
Josquintab |
5 |
European, Western |
classical |
Music Transcription(MIDI) |
ISMIR |
|
2019 |
VGMIDI |
25 |
- |
video games |
Video to MIDI |
ISMIR |
|
2019 |
Towards automatically correcting tapped beat |
7.25 |
Latin, Irish, etc |
Different genres |
Audio Transcription |
ISMIR |
Distribution of region wise music or genre wise music not given |
2019 |
Harmonix Set |
11.5 |
Western |
rock |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
21 |
Western |
pop |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
5 |
Western |
country |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
11 |
Western |
Dance, edm |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
7.5 |
Western |
hip-hop |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
2.5 |
Western |
blues |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
1.2 |
Western |
Reggae |
Segmentation |
ISMIR |
|
2019 |
Harmonix Set |
4 |
Western |
Alternative |
Segmentation |
ISMIR |
|
2020 |
ASAP Dataset |
- |
Western |
classical, piano |
Music Transcription(MIDI) |
ISMIR |
|
2020 |
Co-listen Embeddings for playlist |
- |
Latin, American, Mexican, Korean |
hip-hop, rap, soul, jazz, rock, pop, metal, edm, alternative, reggae, classical, ballad |
Playlist Analysis |
ISMIR |
|
2020 |
Chord Jazzification |
3.3 |
Japanese |
jazz, pop |
Chord Symbols Annotation |
ISMIR |
|
2020 |
Bebopnet |
5 |
American |
jazz, bebop |
Music Transcription(MIDI) |
ISMIR |
|
2020 |
BCFB |
- |
Western |
classical |
Annotated |
ISMIR |
|
2020 |
Bambuco |
0.05 |
Columbian |
sesquialtera |
Annotated |
ISMIR |
|
2020 |
Bach Chorales |
3.8 |
Western |
chorales, bach, classical |
Music Transcription |
ISMIR |
|
2020 |
Disruption in song similarity |
- |
Brazil |
Forro |
Genre trajectory analysis |
ISMIR |
|
2020 |
dMelodies |
- |
- |
- |
UnAnnotated |
ISMIR |
Monophonic, 1.3 million 2 bars |
2020 |
Freesound Loop Dataset |
- |
North American, Indian, Western* |
rock, jazz, drums, base, edm, reggae, lofi, hip-hop |
Annotated, Loop Recognition |
ISMIR |
|
2020 |
GQ39 |
0.56 |
Chinese |
classical |
Annotated |
ISMIR |
|
2020 |
Fifteen Songs Dataset |
- |
North American |
rock |
Annotated |
ISMIR |
|
2020 |
Aligned Lyrics Informed Singing Voice Separation |
0.733 |
Korean |
pop |
Annotated |
ISMIR |
|
2020 |
ACME Version 1.0 |
- |
Western |
classical |
Annotated |
ISMIR |
|
2020 |
MARACATU DE BAQUE SOLTO |
0.05 |
Brazil |
samba |
Annotated |
ISMIR |
|
2020 |
MARACATU DE BAQUE SOLTO |
0.32 |
Brazil |
marcha |
Annotated |
ISMIR |
|
2020 |
Pop909 |
60 |
- |
pop |
Annotated |
ISMIR |
|
2020 |
Voice Leading Schema Recognition |
- |
Western |
classical |
Annotated |
ISMIR |
|
2021 |
ADTOF |
114 |
Western |
rock, pop, metal, indie-rock, alternative |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
2.84 |
African |
Classical |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
10.21 |
Latin |
Classical |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
5.02 |
North American |
Classical |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
8.7 |
Brazilian |
Classical |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1.34 |
Polish |
Classical |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1 |
Latin |
Hip-hop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1 |
French |
Electronic |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
29.86 |
North American |
Electronic |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1.67 |
Brazilian |
Electronic |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
13.89 |
North American |
Blues |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1.09 |
Polish |
Blues |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
12.63 |
British |
Blues |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1.76 |
North American |
Country |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
17.4 |
French |
Reggae |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
1.09 |
Italian |
Reggae |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
4.02 |
American |
Reggae |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
2.09 |
Mexican |
Reggae |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
3.26 |
Argentinian |
Reggae |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models |
2.09 |
Polish |
Reggae |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
57.3 |
French |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
79.89 |
German |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
17.48 |
Spanish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
9.45 |
Italian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
43.5 |
Russian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
20.91 |
Japanese |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.84 |
African |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
37.64 |
Latin |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
121.97 |
North American |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
38.31 |
Canadian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
40.15 |
Australian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
4.02 |
Israeli |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
4.27 |
Turkish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
99.04 |
Brazilian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
18.49 |
Mexican |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
35.55 |
Argentinian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
45.42 |
Finnish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
33.8 |
Norwegian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
6.11 |
Dutch |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.09 |
Belgian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
25.1 |
Polish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
2.93 |
Swiss |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
5.1 |
Portuguese |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
4.02 |
Austrian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
3.6 |
Czech |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
3.85 |
Hungarian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
0.84 |
Greek |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.84 |
Serbian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
5.02 |
Croatian |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
9.12 |
Irish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.42 |
Scottish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
26.1 |
British |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
6.27 |
Danish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
95.45 |
Swedish |
Rock |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
29.95 |
French |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
8.7 |
German |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
3.26 |
Spanish |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
5.44 |
Italian |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
9.87 |
Russian |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.67 |
Japanese |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
9.7 |
Latin |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
7.7 |
European |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
6.69 |
North American |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
9.54 |
Canadian |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.84 |
Australian |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
3.18 |
Israeli |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
6.52 |
Mexican |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
2.43 |
Finnish |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.84 |
Dutch |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
3.68 |
Polish |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.25 |
Czech |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.92 |
Hungarian |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
3.68 |
Croatian |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.92 |
Danish |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.76 |
Swedish |
Pop |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
2.26 |
French |
Jazz |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
19.99 |
North American |
Jazz |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
1.42 |
Turkish |
Jazz |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
10.21 |
Brazilian |
Jazz |
Annotated |
ISMIR |
|
2021 |
DADAGP: A Dataset of Tokenized GuitarPro Songs |
15.73 |
Japanese |
Classical |
Annotated |
ISMIR |
|
2021 |
Tabla Gharana Detection |
16 |
Indian |
Classical, tabla |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
4140 |
- |
Classical |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
3420 |
- |
Pop |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
2340 |
- |
Rock |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
1800 |
- |
Jazz |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
1440 |
- |
Electronic |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
1260 |
- |
Folk |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
900 |
- |
Indie |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
540 |
- |
Metal |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
540 |
- |
Musica |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
360 |
- |
Experimental |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
360 |
- |
Hip-hop |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
270 |
- |
Punk |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
270 |
- |
Choir |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
180 |
- |
Punk Rock |
Annotated |
ISMIR |
|
2021 |
MetaMIDI |
180 |
- |
Electronic |
Annotated |
ISMIR |
|
2021 |
Emopia |
11 |
Japanese, Korean, Western |
Classical, pop |
Emotion Recognition |
ISMIR |
|
2022 |
ATEPP |
1007 |
Western |
classical |
Music Transcription |
ISMIR |
|
2022 |
KDC |
2.5 |
Iran |
Classical, dastgahi |
Pitch Annotations, Annotated |
ISMIR |
|
2022 |
LYRA |
80 |
Greek |
Traditional, Folk |
Sub genre Classification |
ISMIR |
|
2022 |
YM2413-MDB |
14.87 |
- |
Video games |
Emotion Recognition |
ISMIR |
|
2022 |
StarNet |
22 |
- |
Classical |
Music Stem Transfer |
ISMIR |
|
2023 |
DISCO-10M |
201,895 |
- |
Pop |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
180,643 |
- |
Rock |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
138,139 |
- |
Electronic |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
95,634 |
- |
Country |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
85,008 |
- |
Classical |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
74,382 |
- |
Easy Listening |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
63,756 |
- |
Hip-hop |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
53,130 |
- |
Instrumental |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
42,504 |
- |
Soul-RnB |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
31,878 |
- |
Jazz |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
31,878 |
- |
International |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
21,252 |
- |
Folk |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
21,252 |
- |
Blues |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
10,626 |
- |
Experimental |
Music IR, Genre Retrieval |
NeurIPS |
|
2023 |
DISCO-10M |
10,626 |
- |
Old-time / historic |
Music IR, Genre Retrieval |
NeurIPS |
|
2022 |
M4Singer |
29.77 |
Chinese |
pop |
Music Tagging, Music Transcription |
NeurIPS |
|
2022 |
ComMU |
500 |
- |
cinematic, newage |
Music Transcription, Music Tagging |
NeurIPS |
Exact genre wise distribution not given |
2020 |
Bollywood Song Dataset |
8 |
Indian |
pop, bollywood |
Audio Classification |
FRSM |
|
2020 |
Bengali folk song seggregation |
29.57 |
Indian, Bengali |
folk |
Audio Classification |
FRSM |
|
2020 |
Real time opera tracking |
9 |
Mozart, Western |
opera, classical |
Real Time Opera Tracking |
FRSM |
|
2020 |
IFSC |
0.51 |
Indian |
folk |
Audio Classification |
FRSM |
Indian region wise distribution also mentioned |
2020 |
Rabindra Sangeet Classification |
- |
Indian |
folk |
Audio Classification |
FRSM |
|
2023 |
Indian classical music |
20 |
Indian |
classical |
Raga Classification |
FRSM |
|
2023 |
Bengali rhymes dataset |
- |
Indian, Bengali |
childrens songs |
UnAnnotated |
FRSM |
|
2024 |
V2Meow dataset |
5000 |
- |
- |
UnAnnotated |
AAAI |
|
2024 |
Call-Response Dataset |
108 |
North American |
pop |
Annotated, Music Transcription(MIDI) |
AAAI |
|
2021 |
Ayumix2000 |
6.67 |
Japanese |
pop |
UnAnnotated |
AAAI |
|
2021 |
MashupDB |
- |
Asian, Western |
pop, rock, folk, electronic, hip-hop |
UnAnnotated |
AAAI |
Hours wise distribution not given |
2021 |
MashupDB |
26.7 |
- |
- |
UnAnnotated |
AAAI |
Hours wise distribution not given |
2021 |
Compound Word Transformer |
108 |
- |
pop, piano |
Annotated |
AAAI |
|
2019 |
Play as you like |
4.45 |
- |
piano, guitar |
UnAnnotated |
AAAI |
genre, Region not mentioned |
2015 |
Carnatic Rhythm Dataset |
16.6 |
Indian, Carnatic |
classical |
UnAnnotated |
AAAI |
|
2007 |
LABROSA covers80 |
- |
North American |
pop |
UnAnnotated |
ISMIR |
8674 samples |
2017 |
FMA : A dataset for Music Analysis |
317.5 |
- |
experimental |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
286.78 |
- |
electronic |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
274.36 |
- |
rock |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
124.48 |
- |
instrumental |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
115.38 |
- |
pop |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
105.88 |
- |
folk |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
69.91 |
- |
hip-hop |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
43.93 |
- |
international |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
34.38 |
- |
jazz |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
34.22 |
- |
classical |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
16.56 |
- |
country |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
14.6 |
- |
blues |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
12.5 |
- |
Soul-RnB |
UnAnnotated |
ISMIR |
Analysing the trim data |
2017 |
FMA : A dataset for Music Analysis |
6.08 |
- |
Easy Listening |
UnAnnotated |
ISMIR |
Analysing the trim data |
2019 |
GrooveMIDI |
13.6 |
western |
drums |
Music Transcription(MIDI) |
ICML |
|
2009 |
Magnatagatune |
86.43 |
western |
classical |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
33.45 |
- |
rock |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
5.22 |
- |
blues |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
31 |
- |
folk |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
1.94 |
- |
pop |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
0.32 |
- |
hip-hop |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
25.34 |
- |
electronic |
UnAnnotated |
ISMIR |
|
2009 |
Magnatagatune |
2.96 |
- |
jazz |
UnAnnotated |
ISMIR |
|
2011 |
MillionSongDataset |
- |
Western, European, North American, Indian, South Asian, Latin American, African, Japanese, Chinese, East Asian, Middle Eastern |
jazz, electronic, hip-hop, classical, rock, blues, folk, pop, electronic, easy listening, experimental |
Annotated |
ISMIR |
Genre or region wise distribution not provided |
- |
GTZAN |
8.33 |
- |
classical |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
blues |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
disco |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
hip-hop |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
jazz |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
rock |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
reggae |
Genre Classification |
- |
|
- |
GTZAN |
8.33 |
- |
rock |
Genre Classification |
- |
|
2010 |
Tonas |
0.6 |
Italian |
classical |
Music Transcription |
ISMIR |
|
2002 |
RWC |
1 |
American |
pop |
Music Classification |
ISMIR |
|
2002 |
RWC |
4 |
Japanese |
pop |
Music Classification |
ISMIR |
|
2002 |
RWC |
4.51 |
Western |
classical |
Music Classification |
ISMIR |
|
2002 |
RWC |
4.01 |
- |
Jazz |
Music Classification |
ISMIR |
|
2002 |
RWC |
1.41 |
- |
Pop |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.32 |
- |
Rock |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.69 |
- |
Hip-hop |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.61 |
- |
bossa nova, tango, rumba |
Music Classification |
ISMIR |
|
2002 |
RWC |
1.08 |
- |
classical |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.71 |
- |
blues, reggae, country |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.32 |
African |
- |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.32 |
Indian |
- |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.32 |
Flamenco |
- |
Music Classification |
ISMIR |
|
2002 |
RWC |
0.31 |
Japanese |
folk |
Music Classification |
ISMIR |
|
2023 |
Steelpan Dataset |
9.33 |
Carribean |
- |
Music Transcription |
NIME |
|
2023 |
Slurtest Dataset |
0.5 |
- |
folk |
Music Transcription |
SMC |
|
2022 |
JS Fake Chorales |
- |
Western |
classical |
Synthetic Music Generation |
SMC |
|
2019 |
MTG Jamendo |
108.46 |
- |
rock |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
32.9 |
- |
jazz |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
105.53 |
- |
classical |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
50.41 |
- |
blues |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
12.3 |
- |
folk |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
28.04 |
- |
hip-hop |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
21.95 |
- |
easy Listening |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
23.8 |
- |
avant-garde, experimental |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
176.15 |
- |
electronic |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
88.74 |
- |
pop |
Audio Tagging |
ICML |
|
2019 |
MTG Jamendo |
2.19 |
- |
country |
Audio Tagging |
ICML |
|