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Download MusicNet and Listen to the Fusion of Country, Mozart, and the Beatles


MusicNet: A Large-Scale Dataset for Music Research




If you are interested in music research, you might have heard of MusicNet, a large-scale dataset of classical music recordings and annotations. MusicNet is a valuable resource for training and evaluating machine learning models for various music-related tasks, such as note identification, instrument recognition, composer classification, onset detection, and next-note prediction. In this article, we will introduce MusicNet, its features and content, its applications and challenges, and how to download and use it for your own projects.




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What is MusicNet and why is it important?




MusicNet is a collection of 330 freely-licensed classical music recordings by 10 composers, written for 11 instruments, 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.


MusicNet is important because it offers a large-scale and diverse dataset of high-quality music recordings and annotations that can serve as a source of supervision and evaluation of machine learning methods for music research. Music research is a challenging domain that requires complex representations of audio signals, musical structures, styles, emotions, and contexts. Existing datasets are often limited in size, quality, diversity, or availability. MusicNet aims to fill this gap by providing a rich and accessible dataset that covers a wide range of musical genres, instruments, composers, and recording conditions.


MusicNet features and content




MusicNet has several features that make it suitable for music research. Some of these features are:


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  • It contains 34 hours of chamber music performances under various studio and microphone conditions.



  • It covers 10 composers from different periods and styles: Bach, Beethoven, Brahms, Dvorak, Haydn, Mozart, Schubert, Schumann, Tchaikovsky, and Vivaldi.



  • It includes 11 instruments from different families: violin, viola, cello, bass, flute, oboe, clarinet, bassoon, horn, trumpet, and piano.



  • It provides over 1 million temporal labels that indicate the onset time, offset time, pitch class, instrument id, note id (within a piece), measure number (within a piece), beat number (within a measure), note value (relative to the beat), and slur information (whether the note is slurred to the next note) of each note in every recording.



  • It offers metadata files that contain information about the composer name, work name (including opus number), movement name (including tempo marking), performer name (including instrument), recording date (if available), recording location (if available), recording engineer (if available), recording license (Creative Commons or Public Domain), score source (if available), score license (Creative Commons or Public Domain), score alignment method (dynamic time warping or manual), score alignment verification (by trained musicians or not), score alignment error rate (estimated or not), and label format version.



MusicNet applications and challenges




MusicNet can be used for various music-related tasks that require machine learning models to learn features of music from scratch. Some of these tasks are:


  • Identify the notes performed at specific times in a recording. This task involves predicting the pitch class, instrument id, note id, measure number, beat number, note value, and slur information of each note given its onset time and offset time in a recording.



  • Classify the instruments that perform in a recording. This task involves predicting the instrument id of each note given its onset time and offset time in a recording.



  • Classify the composer of a recording. This task involves predicting the composer name of a recording given its audio signal.



Ident Identify the onset times of notes in a recording. This task involves predicting the onset time of each note given its audio signal.


Predict the next note in a sequence of notes. This task involves predicting the pitch class, instrument id, note id, measure number, beat number, note value, and slur information of the next note given a sequence of previous notes.


These tasks are challenging because they require models to learn complex and high-dimensional representations of music from raw audio signals, and to deal with issues such as noise, polyphony, tempo variation, articulation, expression, and style. MusicNet provides a benchmark dataset for evaluating the performance of different models on these tasks, and for comparing them with human performance.


How to download MusicNet and use it for your own projects




If you are interested in using MusicNet for your own music research projects, you can download it from its official website or from its GitHub repository. The dataset is available in two formats: WAV files and HDF5 files. The WAV files contain the raw audio signals of the recordings, while the HDF5 files contain the labels and metadata of the recordings. The HDF5 files are organized into three groups: train, test, and validation. Each group contains a set of recordings and their corresponding labels and metadata. The train group contains 320 recordings, the test group contains 10 recordings, and the validation group contains 10 recordings.


Download options and formats




You can choose to download either the WAV files or the HDF5 files, or both. The WAV files are compressed into ZIP files, while the HDF5 files are compressed into TAR files. The total size of the WAV files is about 22 GB, while the total size of the HDF5 files is about 1 GB. You can download them from the following links:


FormatLink


WAV files


HDF5 files


You can also download individual recordings or subsets of recordings by using the download script provided in the GitHub repository. The script allows you to specify the format, group, composer, instrument, or recording id of the recordings you want to download. For example, if you want to download only the WAV files of Mozart's recordings in the train group, you can run the following command:


python download.py --format wav --group train --composer Mozart


Data loaders and tools




To facilitate the use of MusicNet for your own projects, you can use the data loaders and tools provided in the GitHub repository. The data loaders allow you to load and process the MusicNet data in Python or PyTorch. The tools allow you to visualize and play back the MusicNet data in Jupyter notebooks. For example, if you want to load and plot a recording from MusicNet using PyTorch, you can run the following code:


import torch from musicnet import MusicNet # Load MusicNet data root = '/path/to/musicnet' dataset = MusicNet(root=root) # Get a recording by id rec_id = 1727 # Mozart's Clarinet Quintet in A major x, y = dataset[rec_id] # Plot the audio signal and labels dataset.plot(x,y)


Examples and tutorials




If you want to see some examples and tutorials on how to use MusicNet for different music-related tasks, you can check out the notebooks provided in the GitHub repository. The notebooks demonstrate how to use MusicNet for tasks such as note identification, instrument recognition, composer classification, onset detection, and next-note prediction. They also show how to use different machine learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and variational autoencoders (VAEs) for these tasks. For example, if you want to see how to use a CNN for note identification on MusicNet, you can open this notebook:


Conclusion and FAQs




In this article, we have introduced MusicNet, a large-scale dataset for music rese


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