Post-graduate theses
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Identifier |
000464520 |
Title |
Application of similarity learning and few-shot learning techniques for percussive sound recognition |
Alternative Title |
Εφαρμογή τεχνικών μάθησης ομοιότητας και μάθησης με λίγα παραδείγματα για αναγνώριση κρουστικών ήχων |
Author
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Συμιακάκης, Ανδρέας Κ.
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Thesis advisor
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Τσακαλίδης, Παναγιώτης
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Reviewer
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Στυλιανού, Γιάννης
Στεφανάκης, Νικόλαος
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Abstract |
The study of hyper-instruments or augmented musical instruments is a very fascinating topic in music technology. It involves the augmentation of the capabilities
of traditional musical instruments - as a source of music creation and performance
- by integrating technologies that, until recently, were mostly known in the context
of electronic musical instruments. Advances in computing and machine learning
(ML) have made it possible to develop hyper-instruments that do not require invasive, irreversible modifications on the instrument, or additional equipment such as
sensors or controls that musicians are unfamiliar with. However, training machine
learning models for such a purpose requires a large amount of unusual data that
can be time-consuming to acquire and that is not usually publicly available.
This Thesis studies the application of machine learning techniques in the context of a guitar augmentation system, assuming a user who interacts with the
system through percussive gestures produced on the body of the guitar. It studies
metric learning and few-shot learning techniques and discusses the value of these
techniques on the basis of numerous experiments with the aim of (i) finding the
optimal approach for training a siamese encoder using a large set of labelled sound
examples from different acoustic or classic guitars and (ii) finding the best way
to exploit the pre-trained siamese encoder for recognising a set of unknown samples from a new guitar, given a limited only support set from that new guitar.
Experimental results show that the proposed combination of ML techniques provides a viable approach in the context of few-shot learning, enabling a significant
reduction in the preparation time and effort required from the final user.
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Language |
English |
Issue date |
2024-07-26 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/0/1/1/metadata-dlib-1715239001-990128-4184.tkl
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Views |
1729 |