TY - GEN
T1 - The Identification and Evaluation for Animal and Other Sounds
T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
AU - Li, Qingqing
AU - Wu, Qiong
AU - Yang, Jiajia
AU - Yu, Yiyang
AU - Wu, Fengxia
AU - Wang, Wu
AU - Takahashi, Satoshi
AU - Ejima, Yoshimichi
AU - Wu, Jinglong
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported by JAPAN SOCIETY FOR THE PROMOTION OF SCIENCE (JSPS) KAKENHI grant numbers 16K18052, 17K18855, 18H05009, 18K12149, and 18H01411, a Grant-in-Aid for Strategic Research Promotion from Okayama University. We would like to express our gratitude to the participants who participated in our research.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Sound identification may affect sound processing and naming, and then influence the performance in hazard identification and memory behavior. In addition, previous studies have shown that there are many factors that can affect the recognition of sound, including ecological frequencies, typical sounds, and some acoustic properties of sounds [6]. However, there are no data directly explore the impact of the length of presentation time on sound recognition, so we will collect the Naming and evaluation data which is from different category (animal, music, artificial) of 30 familiar sounds by controlling five time periods (150ms, 200ms, 250ms, 300ms, 350ms). For all categories of sound materials, as the presentation time increases, the accuracy of recognition increases significantly. These normative data may be helpful in guiding sound identification in future artificial intelligence studies and help clarify the relationships between sound naming and other variables, including hazard identification.
AB - Sound identification may affect sound processing and naming, and then influence the performance in hazard identification and memory behavior. In addition, previous studies have shown that there are many factors that can affect the recognition of sound, including ecological frequencies, typical sounds, and some acoustic properties of sounds [6]. However, there are no data directly explore the impact of the length of presentation time on sound recognition, so we will collect the Naming and evaluation data which is from different category (animal, music, artificial) of 30 familiar sounds by controlling five time periods (150ms, 200ms, 250ms, 300ms, 350ms). For all categories of sound materials, as the presentation time increases, the accuracy of recognition increases significantly. These normative data may be helpful in guiding sound identification in future artificial intelligence studies and help clarify the relationships between sound naming and other variables, including hazard identification.
KW - Category
KW - Evaluation
KW - Presentation time
KW - Sounds-Naming
UR - http://www.scopus.com/inward/record.url?scp=85072404793&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072404793&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2019.8816333
DO - 10.1109/ICMA.2019.8816333
M3 - Conference contribution
AN - SCOPUS:85072404793
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 874
EP - 879
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 August 2019 through 7 August 2019
ER -