AES Store

Journal Forum

Audibility of a CD-Standard A/DA/A Loop Inserted into High-Resolution Audio Playback - September 2007
10 comments

Reflecting on Reflections - June 2014
1 comment

Quiet Thoughts on a Deafening Problem - May 2014
1 comment

Access Journal Forum

AES E-Library

Segmentation of Musical Signals Using Hidden Markov Models.

In this paper, we present a segmentation algorithm for acoustic musical signals, using a hidden Markov model. Through unsupervised learning, we discover regions in the music that present steady statistical properties: textures. We investigate different front-ends for the system, and compare their performances. We then show that the obtained segmentation often translates a structure explained by musicology: chorus and verse, different instrumental sections, etc. Finally, we discuss the necessity of the HMM and conclude that an efficient segmentation of music is more than a static clustering and should make use of the dynamics of the data.

Authors:
Affiliation:
AES Convention: Paper Number:
Publication Date:
Subject:

Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $20 for non-members, $5 for AES members and is free for E-Library subscribers.

Learn more about the AES E-Library

E-Library Location:

Start a discussion about this paper!


 
Facebook   Twitter   LinkedIn   Google+   YouTube   RSS News Feeds  
AES - Audio Engineering Society