In an intelligent editing environment, the semantic music structure can be used as beneficial assistance during the post production process. In this paper we propose a new approach to extract both low and high level hierarchical structure from vocal tracks of multi-track master recordings. Contrary to most segmentation methods for polyphonic audio, we utilize extra information available when analyzing a single audio track. A sequence of symbols is derived using a hierarchical decomposition method involving onset detection, pitch tracking and timbre modelling to capture phonetic similarity. Results show that the applied model well captures similarity of short voice segments.
Authors:
Fazekas, György; Sandler, Mark
Affiliation:
Queen Mary University of London
AES Convention:
123 (October 2007)
Paper Number:
7249
Publication Date:
October 1, 2007
Subject:
Signal Processing Applied To Music
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.