AES Store

Journal Forum

Perceptual Effects of Dynamic Range Compression in Popular Music Recordings - January 2014
4 comments

Accurate Calculation of Radiation and Diffraction from Loudspeaker Enclosures at Low Frequency - June 2013
9 comments

New Measurement Techniques for Portable Listening Devices: Technical Report - October 2013
1 comment

Access Journal Forum

AES E-Library

System for Automatic Singing Voice Recognition

A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing.

Authors:
Affiliation:
JAES Volume 56 Issue 9 pp. 710-723; September 2008
Publication Date:

Click to purchase paper or login as an AES member. If your company or school subscribes to the AES Journal then you can look for this paper in the institutional version of the Online Journal. 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