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

被引：0
|
作者：
Bin Sahbudin, Murtadha Arif [1 ]
Scarpa, Marco [1 ]
Serrano, Salvatore [1 ]
机构：
[1] Univ Messina, Dept Engn, I-98166 Messina, Italy
来源：
2019 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC) | 2019年
关键词：
D O I：
10.1109/iccnc.2019.8685489
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：350 / 354
页数：5
相关论文
共 50 条
  • [1] Image compression in real-time multiprocessor systems using divisive K-means clustering
    Fradkin, D
    Muchnik, IB
    Streltsov, S
    [J]. INTERNATIONAL CONFERENCE ON INTEGRATION OF KNOWLEDGE INTENSIVE MULTI-AGENT SYSTEMS: KIMAS'03: MODELING, EXPLORATION, AND ENGINEERING, 2003, : 506 - 511
  • [2] An FPGA implementation of real-time K-means clustering for color images
    Saegusa, Takashi
    Maruyama, Tsutomu
    [J]. JOURNAL OF REAL-TIME IMAGE PROCESSING, 2007, 2 (04) : 309 - 318
  • [3] Real-time K-means clustering for color images on reconfigurable hardware
    Maruyama, Tsutomu
    [J]. 18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2006, : 816 - 819
  • [4] An FPGA implementation of real-time K-means clustering for color images
    Takashi Saegusa
    Tsutomu Maruyama
    [J]. Journal of Real-Time Image Processing, 2007, 2 : 309 - 318
  • [5] Real-time segmentation of color images based on the k-means clustering on FPGA
    Saegusa, Takashi
    Maruyama, Tsutomu
    [J]. ICFPT 2007: INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY, PROCEEDINGS, 2007, : 329 - 332
  • [6] Implementation of Real-Time Skin Segmentation Based on K-Means Clustering Method
    De, Souranil
    Rakshit, Soumik
    Biswas, Abhik
    Saha, Srinjoy
    Datta, Sujoy
    [J]. COMPUTATIONAL VISION AND BIO-INSPIRED COMPUTING, 2020, 1108 : 964 - 973
  • [7] Real Time Floor Sitting Posture Monitoring using K-Means Clustering
    Soenandi, Iwan Aang
    Ginting, Meriastuti
    Harsono, Budi
    [J]. PROCEEDINGS OF THE 2019 2ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND INFORMATION MANAGEMENT (ICSIM 2019) / 2019 2ND INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (ICBDSC 2019), 2019, : 194 - 198
  • [8] Hardware-driven adaptive k-means clustering for real-time video imaging
    Maliatski, B
    Yadid-Pecht, O
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2005, 15 (01) : 164 - 166
  • [9] A New Real-Time FPGA-Based Implementation of K-Means Clustering for Images
    Deng, Tiantai
    Crookes, Danny
    Siddiqui, Fahad
    Woods, Roger
    [J]. INTELLIGENT COMPUTING AND INTERNET OF THINGS, PT II, 2018, 924 : 468 - 477
  • [10] Unsupervised Embrace Pose Recognition using K-Means Clustering
    Kleawsirikul, Nutnaree
    Mitake, Hironori
    Hasegawa, Shoichi
    [J]. 2017 26TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN), 2017, : 883 - 890
12345