Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition

We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone ph...

Full description

Saved in:
Bibliographic Details
Published in:2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2661 - 2665
Main Authors: Raissi, Tina, Tibo, Alessandro, Bientinesi, Paolo
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2018
Subjects:
autoencoder
Feature extraction
feature extraction and selection
genre classification
information gain
Mel frequency cepstral coefficient
Music
Musical signal
Pipelines
Task analysis
Training
ISSN:2379-190X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461807