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

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)} }

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).

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.

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

seungheondoh/cmd-musicnet-metadata dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Rohit Singh

Contents

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

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

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