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A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance.
Author:	Sommer, Nathan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	102 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Notes:	Advisor: Ralescu, Anca.
Contained By:	Dissertations Abstracts International82-03B.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28108067
ISBN:	9798662545571

A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance.
Sommer, Nathan.

A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 102 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2020.

This item must not be sold to any third party vendors.

Musicians who perform with electronic synthesizers often adjust synthesis parameters during live performance to achieve more expressive sounds. Enabling the performer to teach a computer to make these adjustments automatically during performance frees the performer from this responsibility, while maintaining an expressive sound in line with the performer’s musical vision. We have created a machine learning system called Larasynth that can be trained by a musician to make these parameter adjustments in real-time during live performances. Larasynth is trained using examples in the form of MIDI files created by the user. Learning is achieved using Long Short-Term Memory (LSTM) recurrent neural networks. To accomplish this, we have devised a set of features which capture the state of the synthesizer controller at regular intervals and are used to make regular predictions of parameter values using an LSTM network. To achieve sufficient generalization during training, transformations are applied to the training data set before each training epoch to simulate variations that may occur during performance. We have also created a new lightweight LSTM library suitable for small networks under real-time constraints. In this thesis we present details behind Larasynth’s implementation and use, and experiments that were performed to demonstrate Larasynth’s ability to learn behaviors based on different musical situations.

ISBN: 9798662545571Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Machine learning

A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance.
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520 $a Musicians who perform with electronic synthesizers often adjust synthesis parameters during live performance to achieve more expressive sounds. Enabling the performer to teach a computer to make these adjustments automatically during performance frees the performer from this responsibility, while maintaining an expressive sound in line with the performer’s musical vision. We have created a machine learning system called Larasynth that can be trained by a musician to make these parameter adjustments in real-time during live performances. Larasynth is trained using examples in the form of MIDI files created by the user. Learning is achieved using Long Short-Term Memory (LSTM) recurrent neural networks. To accomplish this, we have devised a set of features which capture the state of the synthesizer controller at regular intervals and are used to make regular predictions of parameter values using an LSTM network. To achieve sufficient generalization during training, transformations are applied to the training data set before each training epoch to simulate variations that may occur during performance. We have also created a new lightweight LSTM library suitable for small networks under real-time constraints. In this thesis we present details behind Larasynth’s implementation and use, and experiments that were performed to demonstrate Larasynth’s ability to learn behaviors based on different musical situations.
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