Adaptive Data Analysis Lab.
Associate Professor: OGAWA Tomomi
For inquiries regarding laboratory information, student placement, or courses, please contact us at the email address below.
e-mail:
to.ogawa(at)mail.dendai.ac.jp
Adaptive Signal Processing/Blind Signal Processing Demo(GitHub)
Our laboratory focuses on signal processing and its theoretical foundations, with applications spanning data science and machine learning.
Digital Signal Processing (Adaptive and Blind Signal Processing)
Data Analysis and Data Science (Medical Data Analytics, Demand Forecasting)
Media Information Processing (Video Summarization, Image and Speech Processing, Noise Reduction)
Applied Machine Learning and Real-World Deployment (ICT-based Rehabilitation Systems, Anomaly Detection for Sewer Pipe Inspection)
Hyuga HAGIWARA, Tomomi OGAWA ,and Hiroki MATSUMOTO: A consideration of data augmentation methods for improving the accuracy of music removal from environmental sounds using machine learning, Proc. of 2025 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2025)
Hyuga Hagiwara, Yuka Saito, Tomomi OGAWA, and Hiroki Matsumoto: Data Augmentation Methods for Music Removal from Environmental Sounds Based on Machine Learning, Proc. of The 24th International Symposium on Communications and Information Technologies (ISCIT 2025)
Hyuga Hagiwara, Tomomi OGAWA, and Hiroki Matsumoto: A Consideration on Improvement of Music Removal Accuracy from Environmental Sounds, Proc. of 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2024)
Hyuga Hagiwara, Tomomi OGAWA, and Hiroki Matsumoto: Improvement of Music Removal Accuracy from Environmental Sounds, 2024 23rd International Symposium on Communications and Information Technologies (ISCIT)
Kiryu Mochizuki, Kohei Maeno, and Tomomi OGAWA: Granger Causality Analysis Between Feature Values Before and After the Change Point in Vehicle Sales Data, Proc. of 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)
Shun Ozawa and Tomomi OGAWA: Background Music Removal Using Deep Learning, Proc. of 2023 3rd IEEE International Conference on Software Engineering a/nd Artificial Intelligence (SEAI)
Taiki Yoshida and Tomomi OGAWA: Highlight Generation Method for Game Commentary using Acoustic Features, Proc. of 2023 3rd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI)
Ryutaro Igarashi and Tomomi OGAWA: Learning Method to Detect Anomalies in Sewer Pipe Images Using Object Detection, Proc. of 2023 3rd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI)
Address: Ishizaka, Hatoyama-machi, Hiki-gun, Saitama 350-0394
Division of Information Systems and Design, School of Science and Engineering, Tokyo Denki University