MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results. This dataset was introduced in the paper "Learning Features of Music from Scratch." [1]

This repository consists of 3 top-level files:

musicnet.tar.gz - This file contains the MusicNet dataset itself, consisting of PCM-encoded audio wave files (.wav) and corresponding CSV-encoded note label files (.csv). The data is organized according to the train/test split described and used in "Invariances and Data Augmentation for Supervised Music Transcription". [2]

musicnet_metadata.csv - This file contains track-level information about recordings contained in MusicNet. The data and label files are named with MusicNet ids, which you can use to cross-index the data and labels with this metadata file.

musicnet_midis.tar.gz - This file contains the reference MIDI files used to construct the MusicNet labels.

A PyTorch interface for accessing the MusicNet dataset is available on GitHub. For an audio/visual introduction and summary of this dataset, see the MusicNet inspector, created by Jong Wook Kim. The audio recordings in MusicNet consist of Creative Commons licensed and Public Domain performances, sourced from the Isabella Stewart Gardner Museum, the European Archive Foundation, and Musopen. The provenance of specific recordings and midis are described in the metadata file.

[1] Learning Features of Music from Scratch. John Thickstun, Zaid Harchaoui, and Sham M. Kakade. In International Conference on Learning Representations (ICLR), 2017. ArXiv Report.

@inproceedings{thickstun2017learning, title={Learning Features of Music from Scratch}, author = {John Thickstun and Zaid Harchaoui and Sham M. Kakade}, year={2017}, booktitle = {International Conference on Learning Representations (ICLR)} }

[2] Invariances and Data Augmentation for Supervised Music Transcription. John Thickstun, Zaid Harchaoui, Dean P. Foster, and Sham M. Kakade. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018. ArXiv Report.

@inproceedings{thickstun2018invariances, title={Invariances and Data Augmentation for Supervised Music Transcription}, author = {John Thickstun and Zaid Harchaoui and Dean P. Foster and Sham M. Kakade}, year={2018}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)} }

MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note s position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.

MusicNet offers music characterizations. This Data Set contains the results obtained after the analysis of pieces of MIDI music. The analysis was performed by applying the concept of Fundamental Scale: the set of symbols producing the minimal entropy when reading a text consisting of a large sequence of characters.

Each piece is characterized by its character-length. The results are presented in terms of symbolic diversity, entropy, and second order entropy. These characterizations of music allow to represent music pieces, composers, styles and periods in a 3-dimensional space with symbolic diversity, entropy, and second order entropy as its axis. The MIDI pieces analyzed for each style of music can be found at these DOIs:Medieval: 10.6084/m9.figshare.5435983Renaissance: 10.6084/m9.figshare.5435992Baroque: 10.6084/m9.figshare.5435995Classical: 10.6084/m9.figshare.5436010Romantic: 10.6084/m9.figshare.5436013Impressionistic: 10.6084/m9.figshare.5436016Twentieth: 10.6084/m9.figshare.5436019Chinese: 10.6084/m9.figshare.5436022Hindu Raga: 10.6084/m9.figshare.5436025Venezuelan: 10.6084/m9.figshare.5436106Rock: 10.6084/m9.figshare.5436031

Movie Themes: 10.6084/m9.figshare.5436034Rock: 10.6084/m9.figshare.5436031

Movie Themes: 10.6084/m9.figshare.5436034

Context MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; a labeling error rate of 4% has been estimated. The MusicNet labels are offered to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.

Specifically, MusicNet labels is proposed as a tool to address the following tasks:

Identify the notes performed at specific times in a recording. Classify the instruments that perform in a recording. Classify the composer of a recording. Identify precise onset times of the notes in a recording. Predict the next note in a recording, conditioned on history. Content (Raw - recommended) The raw data is available in standard wav audio format, with corresponding label files in csv format. These data and label filenames are MusicNet ids, which you can use to cross-index the data, labels, and metadata files.

(Python) The Python version of the dataset is distributed as a NumPy npz file. This is a binary format specific to Python (WARNING: if you attempt to read this data in Python 3, you need to set encoding='latin1' when you call np.load or your process will hang without any informative error messages). This format has three dependencies:

Python - This version of MusicNet is distributed as a Python object. NumPy - The MusicNet features are stored in NumPy arrays. intervaltree - The MusicNet labels are stored in an IntervalTree. Acknowledgements The MusicNet labels apply exclusively to Creative Commons and Public Domain recordings, and as such we can distribute and re-distribute the MusicNet labels together with their corresponding recordings. The music that underlies MusicNet is sourced from the Isabella Stewart Gardner Museum, the European Archive, and Musopen.

This work was supported by the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery, and the program "Learning in Machines and Brains" (CIFAR).

About this version:

This particular variant of the MusicNet dataset has been resampled to a 16 kHz-mono-16-bit-wav format, which makes it more suitable for certain audio processing tasks, particularly those that require lower sampling rates. We redistribute this data as a part of YourMT3 project. The license for redistribution is attached.

Moreover, this version of the dataset includes various split options derived from previous works on automatic music transcription as python dictionary (see README.md). Below is a brief description of available split options:

MUSICNET_SPLIT_INFO = {
  'train_mt3': [], # the first 300 songs are synth dataset, while the remaining 300 songs are acoustic dataset. 
  'train_mt3_synth' : [], # Note: this is not the synthetic dataset of EM (MIDI Pop 80K) nor pitch-augmented. Just recording of MusicNet MIDI, split by MT3 author's split. But not sure if they used this (maybe not).
  'train_mt3_acoustic': [],
  'validation_mt3': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611],
  'validation_mt3_synth': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611],
  'validation_mt3_acoustic': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611],
  'test_mt3_acoustic': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621],
  'train_thickstun': [], # the first 327 songs are synth dataset, while the remaining 327 songs are acoustic dataset. 
  'test_thickstun': [1819, 2303, 2382],
  'train_mt3_em': [], # 293 tracks. MT3 train set - 7 missing tracks[2194, 2211, 2227, 2230, 2292, 2305, 2310], ours
  'validation_mt3_em': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], # ours
  'test_mt3_em': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], # ours
  'train_em_table2' : [], # 317 tracks. Whole set - 7 missing tracks[2194, 2211, 2227, 2230, 2292, 2305, 2310] - 6 test_em
  'test_em_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # strings and winds from Cheuk's split, using EM annotations
  'test_cheuk_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # strings and winds from Cheuk's split, using Thickstun's annotations
}

About MusicNet:

The MusicNet dataset, originally released in 2016 by Thickstun et al., "Learning Features of Music from Scratch". It is a collection of music recordings annotated with labels for various tasks, such as automatic music transcription, instrument recognition, and genre classification. The original dataset contains over 330 hours of audio, sourced from various public domain recordings of classical music, and is labeled with instrument activations and note-wise annotations.

About MusicNet EM:

MusicNetEM are refined labels for the MusicNet dataset, in the form of MIDI files. They are aligned with the recordings, with onset timing within 32ms. They were created using an EM process, similar to the one described in the Ben Maman and Amit H. Bermano, "Unaligned Supervision for Automatic Music Transcription in The Wild". Their split (Table 2 of this paper) derived from another paper, Kin Wai Cheuk et al., "ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data".

License:

CC-BY-4.0

UNDER CONSTRUCTION >

About this version:

This particular variant of the MusicNet dataset has been resampled to a 16 kHz-mono-16-bit-wav format, which makes it more suitable for certain audio processing tasks, particularly those that require lower sampling rates. We redistribute this data as a part of YourMT3 project. The license for redistribution is attached.

Moreover, this version of the dataset includes various split options derived from previous works on automatic music transcription as python dictionary (see README.md). Below is a brief description of available split options:

MUSICNET_SPLIT_INFO = { 'train_mt3': [], # the first 300 songs are synth dataset, while the remaining 300 songs are acoustic dataset. 'train_mt3_synth' : [], # Note: this is not the synthetic dataset of EM (MIDI Pop 80K) nor pitch-augmented. Just recording of MusicNet MIDI, split by MT3 author's split. But not sure if they used this (maybe not). 'train_mt3_acoustic': [], 'validation_mt3': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'validation_mt3_synth': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'validation_mt3_acoustic': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'test_mt3_acoustic': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], 'train_thickstun': [], # the first 327 songs are synth dataset, while the remaining 327 songs are acoustic dataset.
'test_thickstun': [1819, 2303, 2382], 'train_mt3_em': [], # 293 tracks. MT3 train set - 7 missing tracks[2194, 2211, 2227, 2230, 2292, 2305, 2310], ours 'validation_mt3_em': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], # ours 'test_mt3_em': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], # ours 'train_em_table2' : [], # 317 tracks. Whole set - 7 missing tracks[2194, 2211, 2227, 2230, 2292, 2305, 2310] - 6 test_em 'test_em_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # strings and winds from Cheuk's split, using EM annotations 'test_cheuk_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # strings and winds from Cheuk's split, using Thickstun's annotations }

About MusicNet:

The MusicNet dataset, originally released in 2016 by Thickstun et al., "Learning Features of Music from Scratch". It is a collection of music recordings annotated with labels for various tasks, such as automatic music transcription, instrument recognition, and genre classification. The original dataset contains over 330 hours of audio, sourced from various public domain recordings of classical music, and is labeled with instrument activations and note-wise annotations.

About MusicNet EM:

MusicNetEM are refined labels for the MusicNet dataset, in the form of MIDI files. They are aligned with the recordings, with onset timing within 32ms. They were created using an EM process, similar to the one described in the Ben Maman and Amit H. Bermano, "Unaligned Supervision for Automatic Music Transcription in The Wild". Their split (Table 2 of this paper) derived from another paper, Kin Wai Cheuk et al., "ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data".

License:

CC-BY-4.0

epr-labs/music-net dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Rohit Singh

Contents

In 2024, South Korean music label Source Music posted a net profit of around **** billion South Korean won. While this represents a decrease from the previous year, it was also the second year of profit. Their only signed artist for the past few years, K-pop girl group GFRIEND disbanded in May of 2021, leaving the label without a direct income stream for the rest of the year. Having been acquired by HYBE Corporation in 2019, the label debuted a new girl group, LE SSERAFIM, in May of 2022.

Slovenian Folk Song Ballads 404 Slovenian folk song ballads with family themes, collected between the years 1819 and 1995. This dataset is an archive of the “Slovenian Folk Song Ballads” corpus (scores, measure maps, analyses, recordings, synchronizations, metadata). It provides both raw data and data for integration with the Dezrann music web platform: https://www.dezrann.net/explore/slovenian-folk-song-ballads. Family songs are folk song ballads, i.e., songs with relatively short but repetitive melodic sections through which the singer narrates a longer story through the lyrics. The melody and lyrics are known not to be fully dependent on each other, meaning, the same melody could be adapted for another lyric and vice versa. Thematically, they fall into the category of those that sing about family destinies, including several motifs from Slovenian, Slavic, and broader European themes. The content often focuses on describing the natural course of family life, from courtship to childbirth. The themes and motifs of family ballads (feudal, rural environment, the time of Turkish invasions, pilgrimages, etc.) revolve around socio-legal relations and both immediate and broader family matters in historical periods from which individual ballads originate. The collection of Slovenian folk song ballads contains transcribed field material collected by Slovenian ethnologists, folklorists, ethnomusicologists and various colleagues of Glasbenonarodopisni inštitut ZRC SAZU spanning from the years 1819 to 1995. Categorized thematically as family ballads, this collection features 404 folk songs, and includes the initial verse of the lyrics, extensive metadata, and musical analysis, encompassing contours, harmony, and song structure (melody and lyric) (see (Borsan et al., 2023) and (Borsan et al., under submission)). 23 of these songs have historical recordings. License: CC-BY-NC-SA-4.0 Maintainers: Vanessa Nina Borsan vanessa@algomus.fr, Mathieu Giraud mathieu@algomus.fr References (Borsan et al., submitted) https://dx.doi.org/10.57745/SINZFK https://www.algomus.fr/data Dataset content slovenian-folk-song-ballads.json. Main archive catalog, with metadata, as described on https://doc.dezrann.net/metadata score/: Scores measure-map/: Measure maps as defined by https://doi.org/10.1145/3625135.3625136, analysis/: Analyses, in .dez format, as described on https://doc.dezrann.net/dez-format audio/: Audio recordings synchro/: Sychronizations between musical time and audio time, as described on https://doc.dezrann.net/synchro

Explore the historical Whois records related to royalty-free-classical-music.net (Domain). Get insights into ownership history and changes over time.

MSMD is a synthetic dataset of 497 pieces of (classical) music that contains both audio and score representations of the pieces aligned at a fine-grained level (344,742 pairs of noteheads aligned to their audio/MIDI counterpart). It can be used for training and evaluating multimodal models that enable crossing from one modality to the other, such as retrieving sheet music using recordings or following a performance in the score image.

Please find further information and a corresponding Python package on this Github page: https://github.com/CPJKU/msmd

If you use this dataset, please cite:
[1] Matthias Dorfer, Jan Hajič jr., Andreas Arzt, Harald Frostel, Gerhard Widmer.
Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification (PDF).
Transactions of the International Society for Music Information Retrieval, issue 1, 2018.

Tencent Music Entertainment, the music division of the Chinese tech giant Tencent, has demonstrated sustained profitability in recent years. In 2024, the company reported **** billion yuan in net profit with a robust growth of online music-paying users.

Music classification tree MusicNet and the data associated with top levels of the tree.

CompMusic Hindustani Rhythm Dataset is a rhythm annotated test corpus for automatic rhythm analysis tasks in Hindustani Music. The collection consists of audio excerpts from the CompMusic Hindustani research corpus, manually annotated time aligned markers indicating the progression through the taal cycle, and the associated taal related metadata. A brief description of the dataset is provided below.

For a brief overview and audio examples of taals in Hindustani music, please see

http://compmusic.upf.edu/examples-taal-hindustani

THE DATASET

Audio music content

The pieces are chosen from the CompMusic Hindustani music collection. The pieces were chosen in four popular taals of Hindustani music, which encompasses a majority of Hindustani khyal music. The pieces were chosen include a mix of vocal and instrumental recordings, new and old recordings, and to span three lays. For each taal, there are pieces in dhrut (fast), madhya (medium) and vilambit (slow) lays (tempo class). All pieces have Tabla as the percussion accompaniment. The excerpts are two minutes long. Each piece is uniquely identified using the MBID of the recording. The pieces are stereo, 160 kbps, mp3 files sampled at 44.1 kHz. The audio is also available as wav files for experiments.

Annotations

There are several annotations that accompany each excerpt in the dataset.

Sam, vibhaag and the maatras: The primary annotations are audio synchronized time-stamps indicating the different metrical positions in the taal cycle. The sam and matras of the cycle are annotated. The annotations were created using Sonic Visualizer by tapping to music and manually correcting the taps. Each annotation has a time-stamp and an associated numeric label that indicates the position of the beat marker in the taala cycle. The annotations and the associated metadata have been verified for correctness and completeness by a professional Hindustani musician and musicologist. The long thick lines show vibhaag boundaries. The numerals indicate the matra number in cycle. In each case, the sam (the start of the cycle, analogous to the downbeat) are indicated using the numeral 1.

Taal related metadata: For each excerpt, the taal and the lay of the piece are recorded. Each excerpt can be uniquely identified and located with the MBID of the recording, and the relative start and end times of the excerpt within the whole recording. A separate 5 digit taal based unique ID is also provided for each excerpt as a double check. The artist, release, the lead instrument, and the raag of the piece are additional editorial metadata obtained from the release. There are optional comments on audio quality and annotation specifics.

Data subsets

The dataset consists of excerpts with a wide tempo range from 10 MPM (matras per minute) to 370 MPM. To study any effects of the tempo class, the full dataset (HMDf) is also divided into two other subsets - the long cycle subset (HMDl) consisting of vilambit (slow) pieces with a median tempo between 10-60 MPM, and the short cycle subset (HMDs) with madhyalay (medium, 60-150 MPM) and the drut lay (fast, 150+ MPM).

Possible uses of the dataset

Possible tasks where the dataset can be used include taal, sama and beat tracking, tempo estimation and tracking, taal recognition, rhythm based segmentation of musical audio, audio to score/lyrics alignment, and rhythmic pattern discovery.

Dataset organization

The dataset consists of audio, annotations, an accompanying spreadsheet providing additional metadata, a MAT-file that has identical information as the spreadsheet, and a dataset description document.

Using this dataset

Please cite the following publication if you use the dataset in your work:

Ajay Srinivasasmurthy, Andre Holzapfel, Ali Taylan Cemgil, Xavier Serra, "A generalized Bayesian model for tracking long metrical cycles in acoustic music signals", in Proc. of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Shanghai, China, March 2016

http://hdl.handle.net/10230/32090

We are interested in knowing if you find our datasets useful! If you use our dataset please email us at mtg-info@upf.edu and tell us about your research.

Contact

If you have any questions or comments about the dataset, please feel free to write to us.

Ajay Srinivasamurthy
Music Technology Group
Universitat Pompeu Fabra,
Barcelona, Spain
ajays.murthy@upf.edu

Kaustuv Kanti Ganguli
DAP lab, Dept. of Electrical Engineering,
Indian Institute of Technology Bombay
Mumbai, India
kaustuvkanti@ee.iitb.ac.in

http://compmusic.upf.edu/hindustani-rhythm-dataset

This dataset extends the Rondo DB dataset_rdb_100_20200115.json.gz (checksum 49540f7855bed26cdaa28ef038d16321) with the following information:

Chord extraction with algorithm Chordino as defined in Mauch, Matthias and Dixon, Simon. Approximate Note Transcription for the Improved Identification of Difficult Chords, Proc. of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), 2010 and available as a Vamp plugin (accessible in Python) here: http://www.isophonics.net/nnls-chroma

Chord extraction with algorithm Crema as defined in McFee, Brian, Juan Pablo Bello. Structured training for large-vocabulary chord recognition. In ISMIR, 2017 and available here: https://github.com/bmcfee/crema

Melody extraction with algorithm Melodia as defined in J. Salamon and E. Gómez. Melody Extraction from Polyphonic Music Signals using Pitch Contour Characteristics, IEEE Transactions on Audio, Speech and Language Processing, 20(6):1759-1770, Aug. 2012 and demonstrated here: https://github.com/justinsalamon/melodia_python_tutorial/blob/master/melodia_python_tutorial.ipynb

Melody extraction with algorithm Piptrack as defined in https://librosa.github.io/librosa/generated/librosa.core.piptrack.html

Chord and melody extraction are performed on the first 120 seconds of referenced recordings.

This dataset uses the format JAMS as described in E. J. Humphrey, J. Salamon, O. Nieto, J. Forsyth, R. M. Bittner, & J. P. Bello. JAMS: A JSON Annotated Music Specification for Reproducible MIR Research. In ISMIR (pp. 591-596), October 2014 and here: https://github.com/marl/jams.

Here is an example of a document in this JSON file:

{

"chord": [
 {
  "data": {
   "annotation_metadata": {
    "annotator": {},
    "annotation_tools": "nnls-chroma:chordino",
    "version": "",
    "annotation_rules": "",
    "data_source": "program",
    "corpus": "",
    "validation": "",
    "curator": {
     "name": "",
     "email": ""
    }
   },
   "sandbox": {},
   "data": [
    {
     "time": 0.185759637,
     "confidence": null,
     "duration": null,
     "value": "N"
    },

// a lot of data ] } }, { "data": [ { "annotation_metadata": { "annotator": {}, "annotation_tools": "CREMA 0.1.0", "version": "d65ffd9.0", "annotation_rules": "", "data_source": "program", "corpus": "", "validation": "", "curator": { "name": "", "email": "" } }, "data": [ { "time": 0.0, "confidence": 0.2663787007331848, "duration": 0.09287981859410431, "value": "G:min"

     }

// a lot of data ], "namespace": "chord", "time": 0, "duration": 118.1, "sandbox": {} } ] } ], "melody": [ { "data": [ { "value": [ -201.7408905029297, -199.42369079589844, -199.42369079589844, // a lot of data ], "time": [ 0.023219954648526078, 0.026122448979591838, 0.029024943310657598, // a lot of data ] } ], "annotation_metadata": {} }, { "data": [ { "value": [ 402.3492126464844, 403.3961181640625, 393.2862548828125, // a lot of data ], "time": [ 0.023219954648526078, 0.026122448979591838, 0.029024943310657598, // a lot of data } ], "annotation_metadata": {} } ], "file_metadata": { "version": "1.0",

 "identifiers": {
  "youtube_id": "0RUhgsuDDe8",
  "rondodb_piece_id": 82,
  "rondodb_movement_id": null
 },
 "artist": "Ludwig Van Beethoven",
 "title": "Ludwig Van Beethoven: Symphony No.5 in C minor, Op. 67",
 "release": null,
 "duration": 120
},
"sandbox": {
 "catalogue_number": "Op. 67",
 "composer": {
  "birth_date": "1770-12-17",
  "death_date": "1827-03-26",
  "name": "Ludwig Van Beethoven",
  "rdb_id_people": 13
 },
 "composition_year": 1808,
 "name": "Symphony",
 "number": "5",
 "piece_full_name": "Symphony No.5 in C minor, Op. 67",
 "piece_full_name_with_composer": "Ludwig Van Beethoven: Symphony No.5 in C minor, Op. 67",
 "rdb_id_piece": 82,
 "recordings": [
  {
   "is_live": false,
   "start_at": 0,
   "url": "https://www.youtube.com/watch?v=0RUhgsuDDe8",
   "claraprints": {
    "120s_chords_chordino": "ejdmmfmfmeahcifmmmfmfmfmffmcmfmfmfmfmcmeimcceicmjihffmfmmffmfmlejdmmfmfmeahcimimmibmfmf",
    "120s_chords_crema": "ejdbfmfmfmeahcimmdmmmfmffmcmfmfmfmceceimccjlmmimmimfmjdbfmfmfmelfhcdnammdmmmfmf",
    "120s_melody_piptrack": "szsxr$$pyry$yy$t$ptxx$rywxx$ryrs$zsotrrt$t%qt$t$rrqxrbqrrqxqosz$sqxy$$xxwxq$szqqxpzsxrszsqxqtpwpwpw$xyryxt$tpzzyrs$xz$xqtzsxq$oyqxtszq$t$obtxtx$qb%ry$yryrbzszwxyrp$ostqxxbryxxbysqxz$yrq%y$t%yrryryrp$rbt$xtw$bzqxqsz%wwxszq$xtszz$stqstszqxqst$t$t$tsz$tsz$tsz$t$t$zst$xrtttptt$yyrrt$ywxq$wytt$wyr$tsqt$xqtt$bqt$rrszr$sqrzzqpwosqsqxqr$szsqxxtpwtxstqstszszw$s",
    "120s_melody_melodia": "sxr$sqx$oyy%qrbqx$qzqbz$tt$$wsqtpwpwywxyqxywpz$xqobxbozsqxqyoqxqq%otrzszbxt$troxobosoyzqsobxtxos$t$t$t$t$t$zsxrbxwsqtbqxybzbxwqxszw$sq",
    "30s_chords_chordino": "ejdmmfmfmeahcifmmmfmf",
    "30s_chords_crema": "ejdbfmfmfmeahcimmdmm",
    "30s_melody_piptrack": "szsxr$$pyry$yy$t$ptxx$rywxx$ryrs$zsotrrt$t%qt$t$rrqxrbqrrqxqosz$sqxy$$xxwxq$szqqxpzsxrszsqxqtpwpwpw$xyryxt$tpzzyrs$xz$xqtzsxq$oyqxtszq$t$obtxtx$qb%ry$yryrbzszwxyrp$ostqxxbryxxbysqxz$yrq%y$t%yrryryrp$rbt$xtw$bzqxqsz%wwxszq$xtszz$stqstszqxqst$t$t$tsz$tsz$tsz$t$t$zst$xrtttptt$yyrrt$ywxq$wytt$wyr$tsqt$xqtt$bqt$rrszr$sqrzzqpwosqsqxqr$szsqxxtpwtxstqstszszw$s",
    "30s_melody_melodia": "sxr$sqx$oyy%qrbqx$qzq"
   }
  },

// 4 other recordings ], "tonality": "C Minor", "wikipedia_url": "https://en.wikipedia.org/wiki/Symphony_No._5_(Beethoven)" } },

The chord and melody sections contain the result of the automatic extraction according to each algorithm.

The sandbox section contains the original data from the source dataset (Rondo DB), with the addition of the claraprints section with various claraprints for different durations and algorithms. For example, 30s_chords_chordino is the claraprint for the 30 first seconds of this recording using the Chordino algorithm.

The original versions of the sandox data are described on https://www.rondodb.com/about and are copied below:

This dataset contains 100 of such works. Here are a detailed field description: • rdb_id_piece (100/100): This is the unique piece ID of this work in Rondo DB. If you prefix it with https://www.rondodb.com/piece/ you will reach its official page on Rondo DB. • name (100/100): The raw name of the piece. As most of classical pieces don't have titles, this is in general the form of the piece, like here: "Symphony". • number (37/100): Applicable to pieces with numbering. Only 32 of them in this dataset. Otherwise the field is absent. • piece_full_name (100/100): Generally accepted name for the given piece. Contains catalog information, subname and tonality. • piece_full_name_with_composer (100/100): Same as piece_full_name but prefixed with composer first name and last name and a semi column. • catalog_number (79/100): This is the full information of how this piece is generally catalogued. Despite the name of the field, this is not a number but a textual information, such as "Op. 37" • tonality (57/100): A full tonality (key and mode) of the given piece. Flats and sharps are writen like "E-flat" or "C-sharp". • composition_year (91/100): The year on four digit of the composition of this piece. • wikipedia_url (82/100): The full URL of the English Wikipedia page describing this work. • rdb_id_movement (26/100): The ID of a movement on Rondo DB. By prefixing this ID with URL https://www.rondodb.com/movement/ you will go to the unique movement page on Rondo DB. A "movement" can also be a "part" in a piece edited with several parts, like a book of preludes, études, or arias in an opera. • movement_full_name (26/100): The name of a movement, which can contain the number of the movement, like in "5. Andaluza". The movement name is part of the piece_full_name and piece_full_name_with_composer as described above. composer (100/100) is a nested object and contains the following fields: • rdb_id_people (100/100): The Rondo DB ID of this composer. By prefixing this ID with URL https://www.rondodb.com/people/ you will go to the unique composer page on Rondo DB. • birth_date (100/100): The date of birth in format YYYY-MM-DD. • death_date (100/100): The date of death in format YYYY-MM-DD. • name (100/100): The full name of the composer like "Heitor Villa-Lobos". recordings (100/100) contains a like of five recording objects. Each recording contains the following fields: • is_live (500/500) indicates if the recording has been performed live or not. When recorded live, it indicates that it can contain applauses before/after the recording, tuning orchestra, noises during the performances... • start_at (500/500) the time where the piece is actually starting at, in seconds. Will roughly starts in less than a second after this point. The instruction given to the

Warner Music Group recorded a net income of ****million U.S. dollars in 2025, down from *** million reported in the previous fiscal year. The record company also reported revenues of **** billion U.S. dollars in the same year.

A collection of automatically generated DJ mixes with ground truth, based on creative-commons-licensed freely available and redistributable electronic dance tracks.

In order to evaluate the DJ mix analysis and reverse engineering methods, we created a dataset of excerpts of open licensed dance tracks and automatically generated mixes based on these.

Each mix is based on a playlist that mixes 3 track excerpts beat-synchronously, such that the middle track is embedded in a realistic context of beat-aligned linear cross fading to the other tracks.
The first track's BPM is used as the seed tempo onto which the other tracks are adapted.

Each playlist of 3 tracks is mixed 12 times with combinations of 4 variants of effects and 3 variants of time scaling using the treatments of the sox open source command-line program [http://sox.sourceforge.net].

Each track excerpt contains about 20s of the beginning and 20s of the end of the source track. However, the exact choice is made taking into account the metric structure of the track. The cue-in region, where the fade-in will happen, is placed on the second beat marker starting a new measure, and lasts for 4 measures. The cue-out region ends with the 2nd to last measure marker. We assure at least 20s for the beginning and end parts. The cut points where they are spliced together is again placed on the start of a measure, such that no artefacts due to beat discontinuity are introduced.

The UnmixDB dataset contains the ground truth for the source tracks and mixes in ASCII label format with tab-separated columns starttime, endtime, label.
For each mix, the start, end, and cue points of the constituent tracks are given, along with their BPM and speed factors.
We use the convention that the label starts with a number indicating which of the 3 source tracks the label refers to.

The song excerpts are accompanied by their cue region and tempo information in .txt files in table format.

Additionally, we provide the .beat.xml files containing the beat tracking results for the full tracks available from Sonnleitner et. al. 2016.

Our DJ mix dataset is based on the curatorial work of Sonnleitner et. al. (ISMIR 2016), who collected Creative-Commons licensed source tracks of 10 free dance music mixes from Mixotic. We used their collected tracks to produce our track excerpts, but regenerated artificial mixes with perfectly accurate ground truth.

The code used to create the dataset from the above is published at https://github.com/Ircam-RnD/unmixdb-creation, such that other researchers can create test data from other track collections or in other variants.

Context Disclaimer this is for beginners It is a small dataset containing the top 100 songs in youtube charts from the year 2020 to 2023. The columns are only two,

1. the video of the song(with artist name and song title)
2. the views of the artist It is arranged in ascending order with the top song in first position.

Source The dataset is collected from https://kworb.net/youtube/stats.html

Inspiration It is part of a larger dataset containing the top songs. Using this dataset, you can do some cleaning and prepocessing, exploration around the most popular artist, titles.

The net cash of Indigo Books & Music with headquarters in Canada amounted to **** million Canadian dollars in 2022. The reported fiscal year ends on March 27.Compared to the earliest depicted value from 2018 this is a total increase by approximately ***** million Canadian dollars. The trend from 2018 to 2022 shows, however, that this increase did not happen continuously.