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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.