TY - GEN
T1 - Noise reduction using independent vector analysis and noise cancellation for a hose-shaped Rescue Robot
AU - Ishimura, Masaru
AU - Makino, Shoji
AU - Yamada, Takeshi
AU - Ono, Nobutaka
AU - Saruwatari, Hiroshi
N1 - Funding Information:
This work was supported by the Japan Science and Technology Agency and Impulsing Paradigm Change through Disruptive Technologies Program (ImPACT) designed by the Council for Science, Technology and Innovation, and partly supported by SECOM Science and Technology Foundation. We would like to express our gratitude to Dr. Hioshi Okuno and Mr.Yoshiaki Bando for providing experimental data.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/19
Y1 - 2016/10/19
N2 - In this paper, we present noise reduction for a hose-shaped rescue robot. The robot is used for searching for disaster victims by capturing their voice with its microphone array. However, the ego noise generated by its vibration motors makes it difficult to distinguish human voices. To solve this problem, we propose a noise reduction method using a blind source separation technique based on independent vector analysis (IVA) and noise cancellation. Our method consists of two steps: (1) estimating a speech signal and an ego noise signal from observed multichannel signals using the IVA-based blind source separation technique, and (2) applying noise cancellation to the estimated speech signal using the estimated ego noise signal as a noise reference. The experimental evaluations show that this approach is effective for suppressing the ego noise.
AB - In this paper, we present noise reduction for a hose-shaped rescue robot. The robot is used for searching for disaster victims by capturing their voice with its microphone array. However, the ego noise generated by its vibration motors makes it difficult to distinguish human voices. To solve this problem, we propose a noise reduction method using a blind source separation technique based on independent vector analysis (IVA) and noise cancellation. Our method consists of two steps: (1) estimating a speech signal and an ego noise signal from observed multichannel signals using the IVA-based blind source separation technique, and (2) applying noise cancellation to the estimated speech signal using the estimated ego noise signal as a noise reference. The experimental evaluations show that this approach is effective for suppressing the ego noise.
KW - Independent vector analysis
KW - Noise cancellation
KW - Noise reduction
KW - Rescue robot
KW - Tough environment
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U2 - 10.1109/IWAENC.2016.7602912
DO - 10.1109/IWAENC.2016.7602912
M3 - Conference contribution
AN - SCOPUS:84997124581
T3 - 2016 International Workshop on Acoustic Signal Enhancement, IWAENC 2016
BT - 2016 International Workshop on Acoustic Signal Enhancement, IWAENC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Workshop on Acoustic Signal Enhancement, IWAENC 2016
Y2 - 13 September 2016 through 16 September 2016
ER -