Supervisor
Vikas Tomer
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This research focuses on Music Genre Classification (MGC) using Convolutional Neural Networks (CNNs) and various datasets, including raw audio files (WAV) and extracted features such as Mel Spectrograms (MS), Mel-Frequency Cepstral Coefficients (MFCC), and Chroma Features (CF). The study employs Explanatory Sequential Mixed Methods (ESMM), combining qualitative research and experimental analysis to explore different model inputs and their performance. Several CNN-based models, including 2D CNN, 2D CNN-LSTM, 1D CNN, and 1D CNN-LSTM, were tested. However, the models generally underperformed, with most achieving accuracy of 10% or lower, and the best model (raw audio 1D CNN) reaching only 20%. The research discusses troubleshooting, model limitations, and future recommendations, including potential reasons for the low performance compared to related works.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Grace, Sabhdh, "Assessment of the Impact of Various Feature Extraction Techniques on the Effectiveness of Music Genre Classification in Neural Network Models." (2024). ICT. 57.
https://arc.cct.ie/ict/57