Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners
Noise suppression in a high-level noise environment using a low-complexity method is challenging. This study proposes a low-complexity noise suppression algorithm for robot vacuum cleaner processors. We collected working noise from a robot vacuum cleaner along with speech signals and developed a met...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816400/ |
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| author | Seunghyeon Shin Minhan Kim Inkoo Jeon Ju-Man Song Yongjin Park Jungkwan Son Seokjin Lee |
| author_facet | Seunghyeon Shin Minhan Kim Inkoo Jeon Ju-Man Song Yongjin Park Jungkwan Son Seokjin Lee |
| author_sort | Seunghyeon Shin |
| collection | DOAJ |
| description | Noise suppression in a high-level noise environment using a low-complexity method is challenging. This study proposes a low-complexity noise suppression algorithm for robot vacuum cleaner processors. We collected working noise from a robot vacuum cleaner along with speech signals and developed a method to extract the desired speech signal while estimating the noise. Our approach estimates the noise in the existing signal and converts it into the desired signal. In addition, we designed a low-complexity neural network capable of operating on mobile processors. The evaluation results demonstrate that our method achieves a performance comparable to that of highly computational methods. Notably, our method maintains superior performance when the intensity of the desired signal is low, and its performance is less degraded than that of other methods. It exhibits less degradation than existing methods, and in contrast to other neural networks, it avoids generating incorrect signals. Furthermore, we simplified the neural network architecture reducing its size by approximately 25% with minimal performance loss. |
| format | Article |
| id | doaj-art-9c2cf1596cbc421594ff4677063fd6eb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9c2cf1596cbc421594ff4677063fd6eb2025-01-03T00:01:36ZengIEEEIEEE Access2169-35362025-01-011378980110.1109/ACCESS.2024.352293710816400Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum CleanersSeunghyeon Shin0https://orcid.org/0009-0007-7094-1710Minhan Kim1Inkoo Jeon2Ju-Man Song3https://orcid.org/0000-0002-9702-1590Yongjin Park4https://orcid.org/0000-0001-8872-1408Jungkwan Son5https://orcid.org/0000-0003-2622-9105Seokjin Lee6https://orcid.org/0000-0001-8220-192XSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of KoreaAdvanced Robotics Laboratory, LG Electronics, Seoul, Republic of KoreaAdvanced Robotics Laboratory, LG Electronics, Seoul, Republic of KoreaAdvanced Robotics Laboratory, LG Electronics, Seoul, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of KoreaNoise suppression in a high-level noise environment using a low-complexity method is challenging. This study proposes a low-complexity noise suppression algorithm for robot vacuum cleaner processors. We collected working noise from a robot vacuum cleaner along with speech signals and developed a method to extract the desired speech signal while estimating the noise. Our approach estimates the noise in the existing signal and converts it into the desired signal. In addition, we designed a low-complexity neural network capable of operating on mobile processors. The evaluation results demonstrate that our method achieves a performance comparable to that of highly computational methods. Notably, our method maintains superior performance when the intensity of the desired signal is low, and its performance is less degraded than that of other methods. It exhibits less degradation than existing methods, and in contrast to other neural networks, it avoids generating incorrect signals. Furthermore, we simplified the neural network architecture reducing its size by approximately 25% with minimal performance loss.https://ieeexplore.ieee.org/document/10816400/Source separationlow-complexitylow-SNRmachine learningmask estimationmono channel |
| spellingShingle | Seunghyeon Shin Minhan Kim Inkoo Jeon Ju-Man Song Yongjin Park Jungkwan Son Seokjin Lee Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners IEEE Access Source separation low-complexity low-SNR machine learning mask estimation mono channel |
| title | Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners |
| title_full | Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners |
| title_fullStr | Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners |
| title_full_unstemmed | Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners |
| title_short | Noise Suppression Method With Low-Complexity Noise Estimation Model and Heuristic Noise-Masking Algorithm for Real-Time Processing of Robot Vacuum Cleaners |
| title_sort | noise suppression method with low complexity noise estimation model and heuristic noise masking algorithm for real time processing of robot vacuum cleaners |
| topic | Source separation low-complexity low-SNR machine learning mask estimation mono channel |
| url | https://ieeexplore.ieee.org/document/10816400/ |
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