Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics IJIIS: International Journal of Informatics and Information Systems
Kamonsantiroj, Suwatchai
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Similarity-preserving hash for content-based audio retrieval using unsupervised deep neural networks Panyapanuwat, Petcharat; Kamonsantiroj, Suwatchai; Pipanmaekaporn, Luepol
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (16.488 KB) | DOI: 10.11591/ijece.v11i1.pp879-891

Abstract

Due to its efficiency in storage and search speed, binary hashing has become an attractive approach for a large audio database search. However, most existing hashing-based methods focus on data-independent scheme where random linear projections or some arithmetic expression are used to construct hash functions. Hence, the binary codes do not preserve the similarity and may degrade the search performance. In this paper, an unsupervised similarity-preserving hashing method for content-based audio retrieval is proposed. Different from data-independent hashing methods, we develop a deep network to learn compact binary codes from multiple hierarchical layers of nonlinear and linear transformations such that the similarity between samples is preserved. The independence and balance properties are included and optimized in the objective function to improve the codes. Experimental results on the Extended Ballroom dataset with 8 genres of 3,000 musical excerpts show that our proposed method significantly outperforms state-of-the-art data-independent method in both effectiveness and efficiency.
A convolution neural network integrating climate variables and spatial-temporal properties to predict influenza trends Watmaha, Jaroonsak; Kamonsantiroj, Suwatchai; Pipanmaekaporn, Luepol
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6619

Abstract

The spread of influenza is contingent upon a multitude of outbreak-related factors, including viral mutation, climate conditions, acquisition of immunity, crowded environments, vaccine efficacy, social gatherings, and the health and age profiles of individuals in contact with infected individuals. An epidemic in the region impacted by spatial transmission risk from adjacent regions. A few influenzas epidemic models start highlighting the spatial correlations between influenza patients and geographically adjacent regions. The proposed model is based on the concept of climatic, immunization, and spatial correlations which are represented by a convolution neural network (CNN) for influenza epidemic forecasting. This study presents an integration of three determinants for predicting influenza outbreaks, multivariate climate data, spatial data on influenza vaccination, and spatial-temporal data of historical influenza patients. The performance of three comparison models, CNN, recurrent neural network (RNN), and long short-term memory (LSTM) was compared by the root mean squared error metric (RMSE). The findings revealed that the CNN model represents human interaction at intervals of 12, 16, 20, 24, and 28 weeks resulting in the best effectiveness of the lowest RMSE=0.00376 with learning rate=0.0001.
Chord Recognition in Music Using a Robust Pitch Class Profile (PCP) Feature and Support Vector Machines (SVM) Kamonsantiroj, Suwatchai; Wannatrong, Lita; Pipanmaekaporn, Luepol
International Journal of Informatics and Information Systems Vol 7, No 1: January 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i1.191

Abstract

Music is the most direct and effective means to express emotion, and the effective identification of music works can help us better understand the works and realize the correct interpretation of music. This paper takes robust PCP feature and SVM as the research object. Firstly, the related concepts of terms and a large number of robust CFO description chord spectrum methods used in audio analysis are introduced. Secondly, it expounds the correlation between SVM and speech tonality, designs the system of music chord recognition, tests the performance of the system, and focuses on the test in the direction of recognition rate. Finally, the test results show that the system greatly improves the recognition of music chords with the support of robustness feature optimization and SVM pattern.
Co-Authors Panyapanuwat, Petcharat Pipanmaekaporn, Luepol Wannatrong, Lita Watmaha, Jaroonsak