In This Section
Perceptual Effects of Dynamic Range Compression in Popular Music Recordings - January 2014
Accurate Calculation of Radiation and Diffraction from Loudspeaker Enclosures at Low Frequency - June 2013
New Measurement Techniques for Portable Listening Devices: Technical Report - October 2013
AES Journal Forum
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.
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