izpis_h1_title_alt

Transkripcija klavirske glasbe z globokim učenjem
Jug, Jan (Author), Marolt, Matija (Mentor) More about this mentor... This link opens in a new window

URLURL - Presentation file, Visit http://eprints.fri.uni-lj.si/3044/ This link opens in a new window

Abstract
Transkripcija glasbe je zahteven postopek simboličnega zapisa glasbenega posnetka. Cilj tega diplomskega dela je bila preučitev transkripcije klavirske glasbe z metodami globokega učenja, za kar so bili implementirani in preizkušeni trije modeli globokih nevronskih mrež: večnivojski perceptron, konvolucijska nevronska mreža in globoka verjetnostna mreža. Z modelom globoke verjetnostne mreže je bilo preizkušeno nenadzorovano predučenje, katerega namen je izluščenje glasbenih značilnosti iz zvočnega signala. Učenje modelov in preverjanje končne uspešnosti transkripcije je bilo izvedeno na zbirki za transkripcijo klavirske glasbe MAPS. Izvedena je bila tudi primerjava predpriprave podatkov s transformacijama hitre Fourierove transformacije in transformacije s konstantnim Q. Končni rezultati so pokazali, da je globoko učenje s pravim učnim načrtom lahko močno orodje za transkripcijo glasbe.

Language:Unknown
Keywords:avtomatična transkripcija glasbe, globoke nevronske mreže, klavirska glasba, globoko učenje, večnivojski perceptron, konvolucijska nevronska mreža, globoka verjetnostna mreža, hitra Fourierova transformacija, transformacija s konstantnim Q
Work type:Not categorized (r6)
Organization:FRI - Faculty of computer and information science
Year:2015
COBISS.SI-ID:1536477635 Link is opened in a new window
Views:508
Downloads:110
Metadata:XML RDF-CHPDL DC-XML DC-RDF
 
Average score:(0 votes)
Your score:Voting is allowed only to logged in users.
:
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Secondary language

Language:Unknown
Title:Transcription of piano music with deep learning
Abstract:
Transcription of music is a complex process of transcribing an audio recording into a symbolic notation. The goal of this thesis was to examine transcription of piano music with deep learning, for which three models of deep neural networks were implemented: multilayer perceptron, convolutional neural network and deep belief network. Through the use of deep belief network, unsupervised pretraining for automatic extraction of musical features from audio signals was also tested. Learning of these models and evaluation of transcription was performed with MAPS database for piano music transcription. A comparison between Fast Fourier Transform and Constant Q Transform for data pre-processing was also carried out. Final results show that deep learning with an appropriate learning schedule is potentially a powerful tool for automatic transcription of music.

Keywords:automatic music transcription, deep neural networks, piano music, deep learning, multilayer perceptron, convolutional neural network, deep belief network, Fast Fourier Transform, Constant Q Transform

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back