Combining Acoustic and Multilevel Visual Features for Music Genre Classification

Abstract

Most music genre classification approaches extract acoustic features from frames to capture timbre information, leading to the common framework of bag-of-frames analysis. However, time-frequency analysis is also vital for modeling music genres. This article proposes multilevel visual features for extracting spectrogram textures and their temporal variations. A confidence-based late fusion is proposed for combining the acoustic and visual features. The experimental results indicated that the proposed method achieved an accuracy improvement of approximately 14% and 2% in the world's largest benchmark dataset (MASD) and Unique dataset, respectively. In particular, the proposed approach won the Music Information Retrieval Evaluation eXchange (MIREX) music genre classification contests from 2011 to 2013, demonstrating the feasibility and necessity of combining acoustic and visual features for classifying music genres.