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

Sommer, Nathan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592168963826025

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
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.
Anca Ralescu, Ph.D. (Committee Chair)
Yizong Cheng, Ph.D. (Committee Member)
Chia Han, Ph.D. (Committee Member)
Mara Helmuth, D.M.A. (Committee Member)
Ali Minai, Ph.D. (Committee Member)
99 p.
Computer Science
machine learning; neural networks; computer music; supervised learning; interactive machine learning; audio synthesizers

Recommended Citations

Citations

  • Sommer, N. (2020). A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592168963826025

    APA Style (7th edition)

  • Sommer, Nathan. A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592168963826025.

    MLA Style (8th edition)

  • Sommer, Nathan. "A Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592168963826025

    Chicago Manual of Style (17th edition)

ucin1592168963826025
208
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.