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Learning Parameter Spaces in Neural Modeling of Audio Circuits

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Wright, Alec
dc.contributor.author Mikkonen, Otto
dc.date.accessioned 2022-12-18T18:02:01Z
dc.date.available 2022-12-18T18:02:01Z
dc.date.issued 2022-12-12
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/118304
dc.description.abstract This thesis studies black-box virtual analog modeling formulated as a machine learning sequence modeling task within the category of supervised learning problems. The focus is on learning scenarios where the modeling targets have multiple user controls, and the aim of the thesis is to evaluate how the properties of the training datasets affect the generalization of the learning algorithm. To study the problem, three nonlinear analogue sound processors were modeled using a recurrent neural network consisting of a Gated Recurrent Unit and a fully-connected output layer. For each target device, two groups of datasets, seven in total, were constructed, using SPICE simulations of the targets. The difference between the datasets is in the density of the sampling grid used for setting the user controls of the targets, as well as in the number of input/output pairs corresponding to each distinct value of each of the controls. For the targets considered during the study, the sparsest sampling grid using only three possible values for each of the user controls was found inadequate for the models to generalize over the testsets used for evaluation. Increasing the sampling density was seen improving the model performance in most cases, with some targets also portraying clear advantages with increasing the number of input/output pairs corresponding to each distinct value of the user controls. According to the study, a sampling grid with five points would appear as a good baseline for training neural networks on targets with multiple user controls when no further investigations in the sampling density can be afforded. For future work, the experiments could be extended to include global scaling of the dataset size while keeping the constraints for sampling the parameter spaces, as well as combining the data generation and training procedures to a single loop, allowing for potentially infinite variety within the datasets. en
dc.format.extent 60
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Learning Parameter Spaces in Neural Modeling of Audio Circuits en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.subject.keyword virtual analog modeling en
dc.subject.keyword black-box modeling en
dc.subject.keyword machine learning en
dc.subject.keyword deep learning en
dc.subject.keyword sequence modeling en
dc.subject.keyword recurrent neural networks en
dc.identifier.urn URN:NBN:fi:aalto-202212187046
dc.programme.major Acoustics and Audio Technology fi
dc.programme.mcode ELEC3030 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Välimäki, Vesa
dc.programme CCIS - Master’s Programme in Computer, Communication and Information Sciences (TS2013) fi
dc.location P1 fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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