Latent representations for traditional music analysis and generation


Autoria(s): Amerotti, Marco
Contribuinte(s)

Gabbrielli, Maurizio

Casini, Luca

Data(s)

13/07/2022

Resumo

We create and study a generative model for Irish traditional music based on Variational Autoencoders and analyze the learned latent space trying to find musically significant correlations in the latent codes' distributions in order to perform musical analysis on data. We train two kinds of models: one trained on a dataset of Irish folk melodies, one trained on bars extrapolated from the melodies dataset, each one in five variations of increasing size. We conduct the following experiments: we inspect the latent space of tunes and bars in relation to key, time signature, and estimated harmonic function of bars; we search for links between tunes in a particular style (i.e. "reels'") and their positioning in latent space relative to other tunes; we compute distances between embedded bars in a tune to gain insight into the model's understanding of the similarity between bars. Finally, we show and evaluate generative examples. We find that the learned latent space does not explicitly encode musical information and is thus unusable for musical analysis of data, while generative results are generally good and not strictly dependent on the musical coherence of the model's internal representation.

Formato

application/pdf

Identificador

http://amslaurea.unibo.it/26323/1/Latent_representation_for_traditional_music_analysis_and_generation.pdf

Amerotti, Marco (2022) Latent representations for traditional music analysis and generation. [Laurea], Università di Bologna, Corso di Studio in Informatica [L-DM270] <http://amslaurea.unibo.it/view/cds/CDS8009/>

Idioma(s)

en

Publicador

Alma Mater Studiorum - Università di Bologna

Relação

http://amslaurea.unibo.it/26323/

Direitos

cc_by_sa4

Palavras-Chave #music,latent space,autoencoder,variational autoencoder,AI,folk music,RNN,generation,musical analysis #Informatica [L-DM270]
Tipo

PeerReviewed

info:eu-repo/semantics/bachelorThesis