Deep Learning Representations for Music Similarity

Abstract

This thesis investigates the use of deep learning for feature extraction from music datasets and the evaluation of song similarities. The primary focus is on using deep learning models to extract audio features, including CNNs and CAEs, and traditional statistical features, and evaluating song similarities using various distance metrics and normalization techniques.