An Extensive Analysis of Query by Singing/Humming System Through Query Proportion

TL;DR

This paper analyzes Query by Singing/Humming systems focusing on query excerpt analysis, examining how database size impacts retrieval performance and precision using various audio features.

Contribution

It introduces an analysis method for QBSH based on query excerpts and evaluates the robustness of different audio features in this context.

Findings

Retrieval performance decreases with larger database size.

Precision diminishes as database size increases.

MFCC, LPC, and LPCC features show varying robustness in QBSH.

Abstract

Query by Singing/Humming (QBSH) is a Music Information Retrieval (MIR) system with small audio excerpt as query. The rising availability of digital music stipulates effective music retrieval methods. Further, MIR systems support content based searching for music and requires no musical acquaintance. Current work on QBSH focuses mainly on melody features such as pitch, rhythm, note etc., size of databases, response time, score matching and search algorithms. Even though a variety of QBSH techniques are proposed, there is a dearth of work to analyze QBSH through query excerption. Here, we present an analysis that works on QBSH through query excerpt. To substantiate a series of experiments are conducted with the help of Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC) and Linear Predictive Cepstral Coefficients (LPCC) to portray the robustness of the knowledge representation. Proposed experiments attempt to reveal that retrieval performance as well as precision diminishes in the snail phase with the growing database size.