Naslov
Machine Learning Models for Music Genre Classification on AudioSet Dataset

Autori
Polanec, Maja

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, preddiplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
26.06

Godina
2023

Stranica
37

Mentor
Bagić Babac, Marina

Ključne riječi
music genre ; audio features ; machine learning ; AudioSet ; logistic regression ; decision tree ; random forest ; support vector machine ; XGBoost ; naïve Bayes

Sažetak
This thesis deals with the problem of classifying music genres using supervised machine learning. Genres classified are pop, rock, hip-hop, vocal, reggae, R&B and techno. AudioSet dataset is used for training and testing models. Audio features from both time and frequency domain are engineered and fed to 6 different models: logistic regression, decision tree, random forests, support vector machine, XGBoost and naïve Bayes. Model performances are evaluated using different evaluation metrics: accuracy, confusion matrix, F1-score and ROC-curve.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA