MongoDB Clustering using K-means for Real-Time Song Recognition

TitleMongoDB Clustering using K-means for Real-Time Song Recognition
Publication TypeConference Proceedings
Year of Conference2019
AuthorsSahbudin, M.. A. B., M.. Scarpa, and S.. Serrano
Conference Name2019 International Conference on Computing, Networking and Communications (ICNC)
Date Published02/2019
PublisherIEEE publisher
Conference Location18-21 Feb. 2019, Honolulu, Hawaii, USA
ISBN Number978-1-5386-9223-3
Accession Number18600816
Keywordsdata analysis, data storage framework, information retrieval, information retrieval technique, K-means clustering, MongoDB clustering, MUSIC, pattern clustering, real-time song recognition, storage management

Recently, the increased competition in song recognition has led to the necessity to identify songs within very huge databases compared to previous years. Therefore, information retrieval technique requires a more efficient and scalable data storage framework. In this work, we propose an approach exploiting K-means clustering and describe strategies for improving accuracy and speed. In collaboration with an audio expert company providing us with 2.4 billion fingerprints data, we evaluated the performance of the proposed clustering and recognition algorithm.

Refereed DesignationRefereed