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The Deep Learning Revolution in MIR: The Pros and Cons, the Needs and the Challenges Read chapter Read first chapter

2021 | OriginalPaper | Chapter

MUSICNTWRK: Data Tools for Music Theory, Analysis and Composition

Author : Marco Buongiorno Nardelli

Published in: Perception, Representations, Image, Sound, Music

Publisher: Springer International Publishing

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Abstract

We present the API for MUSICNTWRK, a python library for pitch class set and rhythmic sequences classification and manipulation, the generation of networks in generalized music and sound spaces, deep learning algorithms for timbre recognition, and the sonification of arbitrary data. The software is freely available under GPL 3.0 and can be downloaded at www.​musicntwrk.​com or installed as a PyPi project (pip install musicntwrk).

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previous chapter Deploying Prerecorded Audio Description for Musical Theater Using Live Performance Tracking
next chapter Feasibility Study of Deep Frequency Modulation Synthesis
Footnotes
1
A full treatment of the mathematical properties of VL operators will be the subject of a forthcoming publication.
 
2
This step might be unnecessary if running on a cloud service like Google Colaboratory.
 
Literature
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2.
Buongiorno Nardelli, M.: materialssoundmusic: a computer-aided data-driven composition environment for the sonification and dramatization of scientific data streams. In: International Computer Music Conference Proceedings, vol. 356 (2015)
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4.
Buongiorno Nardelli, M.: Beautiful data: reflections for a sonification and post-sonification aesthetics, in leonardo gallery: scientific delirium madness 4.0. Leonardo 51(3), 227–238 (2018)
5.
Buongiorno Nardelli, M., Aramaki, M., Ystad, S., Kronland-Martinet, R.: 14th International Symposium on Computer Music Multidisciplinary Research CNMR: Oct 2019, Marseille, France (2019)
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9.
Piczak, K.J.: Environmental sound classification with convolutional neural networks. In: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6 (2015)
Metadata
Title
MUSICNTWRK: Data Tools for Music Theory, Analysis and Composition
Author
Marco Buongiorno Nardelli
Copyright Year
2021
Book
Perception, Representations, Image, Sound, Music

Print ISBN: 978-3-030-70209-0

Electronic ISBN: 978-3-030-70210-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-70210-6_14