Analysis of Vowels in Sung Queries for a Music Information Retrieval System

Authors:
Maureen Mellody;Mark A. Bartsch;Gregory H. Wakefield
Affiliations:
Applied Physics Program, University of Michigan, Ann Arbor, Michigan, USA. maureen@stievater.com;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA. mbartsch@eecs.umich.edu;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA. ghw@eecs.umich.edu
Venue:
Journal of Intelligent Information Systems
Year:
2003

Citing 6
Cited 3

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Fundamentals of speech recognition

Fundamentals of speech recognition
Pitch analysis

Visual representations of speech signals
Query by humming: musical information retrieval in an audio database

Proceedings of the third ACM international conference on Multimedia
HMM-based musical query retrieval

Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Tune Retrieval in the Multimedia Library

Multimedia Tools and Applications

MusicStory: a personalized music video creator

Proceedings of the 13th annual ACM international conference on Multimedia
Music information retrieval from a singing voice using lyrics and melody information

EURASIP Journal on Applied Signal Processing
Finding `Lucy in Disguise': The Misheard Lyric Matching Problem

AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

A method for analyzing and categorizing the vowels of a sung query is described and analyzed. This query system uses a combination of spectral analysis and parametric clustering techniques to divide a single query into different vowel regions. The method is applied separately to each query, so no training or repeated measures are necessary. The vowel regions are then transformed into strings and string search methods are used to compare the results from various songs. We apply this method to a small pilot study consisting of 40 sung queries from each of 7 songs. Approximately 60% of the queries are correctly identified with their corresponding song, using only the vowel stream as the identifier.