Center+for+Noise+and+Vibration+Control
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Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time
The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.
Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.
Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.
Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.
To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.
In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.
The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.
The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.
In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.
The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.
A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.
Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient's condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff."
This study was conducted in collaboration with SM Instruments Inc.
Profile: Yong-Hwa Park, Ph.D.
Associate Professor
yhpark@kaist.ac.kr
http://human.kaist.ac.kr/
Human-Machine Interaction Laboratory (HuMaN Lab.)
Department of Mechanical Engineering (ME)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr/en/
Daejeon 34141, Korea
Profile: Gyeong Tae Lee
PhD Candidate
hansaram@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Seong Hu Kim
PhD Candidate
tjdgnkim@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Hyeonuk Nam
PhD Candidate
frednam@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Young-Key Kim
CEO
sales@smins.co.kr
http://en.smins.co.kr/
SM Instruments Inc.
Daejeon 34109, Korea
(END)
2020.08.13 View 13751 -
Yang-Hann Kim named recipient of the Rossing Prize in Acoustics Education by the Acoustical Society of America
Courtesy of the Acoustical Society of America (ASA)
Press release issued by ASA on October 8, 2015:
Yang-Hann Kim named recipient of the Rossing Prize in Acoustics Education by the Acoustical Society of America
Melville (NY), 8 October 2015—Yang-Hann Kim, Professor at KAIST (Korea Advanced Institute of Science and Technology), Daejeon, has been named recipient of the Acoustical Society of America (ASA) Rossing Prize in Acoustics Education. The Rossing Prize is awarded to an individual who has made significant contributions toward furthering acoustics education through distinguished teaching, creation of educational materials, textbook writing and other activities. The Prize will be presented at the 170th meeting of the ASA on 4 November 2015 in Jacksonville, Florida.
“It is my great honor to receive the Rossing Prize, which has been given to outstanding scholar members of ASA since 2003. I never dreamed to be one of them.” said Kim. “I must express my deep respect and love to my friend Thomas Rossing: I have known him more than 20 years, always respect what he has done for teaching, writing books, and pioneering work in musical acoustics.”
Yang-Hann Kim is a Fellow of the Acoustical Society of America. He received a Ph.D. from the Massachusetts Institute of Technology. His main research interests in acoustics began with “sound visualization” resulted in the development of the “sound camera” which makes any sound visible instantly. Then he moved to “sound manipulation.” Using his manipulation technology, one can move any sound in space and time, positioning sound, and can create a private sound zone. Sound Visualization and Manipulation, (Wiley, 2013), summarizes these two fields. Dr. Kim’s textbook, Sound Propagation: An Impedance Based Approach (John Wiley and Sons, 2010), is well acknowledged by the associated professional communities as one of best acoustics textbooks. Using his teaching experience at KAIST, he created a YouTube lecture on acoustics and vibration which is also available in MOOC (Massive Open Online Course). He has also presented lectures to over 500 engineers and technicians for the past 30 years.
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The Acoustical Society of America (ASA) is the premier international scientific society in acoustics devoted to the science and technology of sound. Its 7000 members worldwide represent a broad spectrum of the study of acoustics. ASA publications include the Journal of the Acoustical Society of America—the world’s leading journal on acoustics, Acoustics Today magazine, books, and standards on acoustics. The Society also holds two major scientific meetings per year. For more information about the Society visit our website, www.acousticalsociety.org.
2015.10.06 View 8018 -
The Acoustical Society of America Names Yang Hann Kim of KAIST the Recipient of the 2015 Rossing Prize in Acoustics Education
The award, given to Dr. Kim in recognition of his contributions to the advancement of acoustics education, will be presented during the 170th Meeting of the Acoustical Society of America on November 2-6, 2015 in Jacksonville, Florida.
The Acoustical Society of America (ASA) announced today that Professor Yang Hann Kim of the Mechanical Engineering Department at the Korea Advanced Institute of Science and Technology (KAIST) was the 12th recipient of the Rossing Prize in Acoustics Education. Dr. Kim is the first recipient selected from a non-English-speaking nation.
The Rossing Prize in Acoustics Education was established in 2003 from a generous gift made to the ASA Foundation by Thomas D. Rossing to recognize an individual who has made significant contributions to the advancement of acoustics education through distinguished teaching, creation of educational materials, textbook writing, and other activities.
During 25 years of teaching and conducting research in acoustics, noise, and vibration at KAIST, Dr. Kim has advised 26 doctorates and published over 200 research papers in journals such as Journal of Acoustical Society of America, Journal of Sound and Vibration, and Journal of Mechanical Systems and Signal Processing. He also wrote two acoustics textbooks for university education, which has been widely read worldwide. The textbook titles are: Sound Propagation: An Impedance Based Approach (Wiley, July 2010) and with the co-author, Dr. Jung-Woo Choi, Sound Visualization and Manipulation (Wiley, September 2013).
Since 2009, Professor Kim has lectured an online course entitled “Introduction to Acoustics,” offering students and the general public throughout the world guidance to study acoustics through the basic concept of impedance, for example, on vibrations and waves.
Dr. Kim will receive the award during ASA’s 170th conference to be held on November 2-6, 2015 at the Hyatt Regency Jacksonville Riverfront Hotel in Jacksonville, Florida, USA.
For the list of previous recipients of the Rossing Prize in Acoustics Education,
see:http://acousticalsociety.org/funding_resources/prizes#rossing
2015.06.04 View 7838