Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
A study on video browsing strategies
A study on video browsing strategies
HMM-based musical query retrieval
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Pitch Tracking and Melody Slope Matching for Song Retrieval
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Classification of audio signals using SVM and RBFNN
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Query by Singing (QBS) 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 QBS 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 QBS techniques are proposed, there is a dearth of work to analyze QBS through query excerption. Here, we present an analysis that works on QBS through query execrpt. To substantiate a series of experiments are conducted with the help of Mel-Frequency Cepstrum 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.