An Auditory Model Based Transcriber of Vocal Queries

Abstract

In this paper a new auditory model-based transcriber of melodic queries produced by a human voice is presented. The newly presented system is tested systematically, together with some other state-of-the-art systems, on three types of vocal queries: singing with syllables, singing with words and whistling. The experimental results show that the new system can transcribe these queries with an accuracy between 76% (whistling) to 85% (singing with syllables), and that it clearly outperforms the other systems included in the test on all three query modes.