@inproceedings{0e1c131964094eada1add4a2b04cb6de,
title = "Knock\&Tap: Classification and Localization of Knock and Tap Gestures using Deep Sound Transfer Learning",
abstract = "Gesture interaction is considered one of the promising approaches to control smart devices. In this paper, we present Knock\&Tap, an audio-based approach that can perform gesture classification and gesture localization using deep transfer learning. Knock\&Tap consists of a single 4-microphone array to record the sound of the user's knocking and tapping gestures and a wood/glass panel for knocking and tapping. Knock\&Tap can be used in a situation or environment where vision-based gesture recognition is impossible due to the lighting condition or camera installation issue. Various experiments were conducted to validate the feasibility of Knock\&Tap with 7 gesture types on both wood and glass panels. Our experimental results show that Knock\&Tap predicts the gesture type and location with an accuracy of up to 97.24\% and 92.05\%, respectively.",
keywords = "Audio classification, Gesture recognition, Transfer learning",
author = "Jeong, \{Jae Yeop\} and Kim, \{Jung Hwa\} and Yoon, \{Ha Yeong\} and Jeong, \{Jin Woo\}",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 23rd ACM International Conference on Multimodal Interaction, ICMI 2021 ; Conference date: 18-10-2021 Through 22-10-2021",
year = "2021",
month = oct,
day = "18",
doi = "10.1145/3461615.3485428",
language = "English",
series = "ICMI 2021 Companion - Companion Publication of the 2021 International Conference on Multimodal Interaction",
publisher = "Association for Computing Machinery, Inc",
pages = "1--6",
booktitle = "ICMI 2021 Companion - Companion Publication of the 2021 International Conference on Multimodal Interaction",
}