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[abstFig src='/00290001/19.jpg' width='300' text='Human-voice enhancement system for a hose-shaped robot' ] This paper presents the design and implementation of a two-stage human-voice enhancement system for a hose-shaped rescue robot. When a microphone-equipped hose-shaped robot is used to search for a victim under a collapsed building, human-voice enhancement is crucial because the sound captured by a microphone array is contaminated by the ego-noise of the robot. For achieving both low latency and high quality, our system combines online and offline human-voice enhancement, providingan overview first and then details on demand. The online enhancement is used for searching for a victim in real time, while the offline one facilitates scrutiny by listening to highly enhanced human voices. Our online enhancement is based on an online robust principal component analysis, and our offline enhancement is based on an independent low-rank matrix analysis. The two enhancement methods are integrated with Robot Operating System (ROS). Experimental results showed that both the online and offline enhancement methods outperformed conventional methods.

In this paper, we present an ego noise reduction method for a hose-shaped rescue robot, developed for search and rescue operations in large-scale disasters. It is used to search for victims in disaster sites by capturing their voices with its microphone array. However, ego noises are mixed with voices, and it is difficult to differentiate them from a call for help from a disaster victim. To solve this problem, we here propose a two-step noise reduction method involving the following: (1) the estimation of both speech and ego noise signals from observed multichannel signals by multichannel nonnegative matrix factorization (NMF) with the rank-1 spatial constraint, and (2) the application of multichannel noise cancellation to the estimated speech signal using reference signals. Our evaluations show that this approach is effective for suppressing ego noise.

In this paper, we present an ego noise reduction method for a hose-shaped rescue robot developed for search and rescue operations in large-scale disasters such as a massive earthquake. It can enter narrow and dark places covered with rubble in a disaster site and is used to search for disaster victims by capturing their voices with its microphone array. However, ego noises, such as vibration or fricative sounds, are mixed with the voices, and it is difficult to differentiate them from a call for help from a disaster victim. To solve this problem, we here propose a two-step noise reduction method as follows: (1) the estimation of both speech and ego noise signals from an observed multichannel signal by multichannel nonnegative matrix factorization (NMF) with the rank-1 spatial constraint, which was proposed by Kitamura et al., and (2) the application of noise cancellation to the estimated speech signal using the noise reference. Our evaluations show that this approach is effective for suppressing ego noise.

Rescue robots have been developed for search and rescue operations in times of large-scale disasters. Such a robot is used to search for survivors in disaster sites by capturing their voices with its microphone array. However, since the robot has many vibration motors, ego noise is mixed with voices, and it is difficult to differentiate the ego noise from a call for help from a disaster survivor. In our previous works, an ego noise reduction technique that combines a method of blind source separation called independent low-rank matrix analysis and postprocessing for noise cancellation was proposed. In the practical use of this robot, to determine the precise location of survivors, the direction of the observed voice should be estimated after the ego noise reduction process. To achieve this objective, in this study, a new hose-shaped rescue robot with microphone arrays was developed. Moreover, we adapt postfilter called MOSIE to our previous noise reduction method to listen to stereo sound because this robot can record stereo sound. By performing in a simulated disaster site, we confirm that the operator can perceive the direction of a survivor's location by applying a speech enhancement technique combining independent low-rank matrix analysis, noise cancellation, and postfiltering to the observed multichannel noisy signals.