Ego noise suppression of a robot using template subtraction

İnce, Gökhan; Nakadai, Kazuhiro; Rodemann, Tobias; Hasegawa, Yuji; Tsujino, Hiroshi; Imura, Jun-ichi

doi:10.1109/iros.2009.5354651

Cited by 43 publications

(46 citation statements)

References 9 publications

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“…Thus, we seek to reduce the dimensionality of the problem. Joint acceleration was reported to be unimportant for spectrum estimation of audible joint noise with a Honda ASIMO [10]. Assuming a smooth robotic motion controller, it is reasonable for us to assume that joint acceleration also has little effect on ego-vibrations.…”

Section: Spectral Subtractionmentioning

confidence: 99%

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Spectral Subtraction of Robot Motion Noise for Improved Event Detection in Tactile Acceleration Signals

McMahan

Kuchenbecker

2012

Haptics: Perception, Devices, Mobility, and Communication

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New robots for teleoperation and autonomous manipulation are increasingly being equipped with highbandwidth accelerometers for measuring the transient vibrational cues that occur during con-tact with objects. Unfortunately, the robot's own internal mechanisms often generate significant high-frequency accelerations, which we term ego-vibrations. This paper presents an approach to characterizing and removing these signals from acceleration measurements. We adapt the audio processing technique of spectral subtraction over short time windows to remove the noise that is estimated to occur at the robot's present joint velocities. Implementation for the wrist roll and gripper joints on a Willow Garage PR2 robot demonstrates that spectral subtraction significantly increases signal-to-noise ratio, which should improve vibrotactile event detection in both teleoperation and autonomous robotics. Abstract. New robots for teleoperation and autonomous manipulation are increasingly being equipped with high-bandwidth accelerometers for measuring the transient vibrational cues that occur during contact with objects. Unfortunately, the robot's own internal mechanisms often generate significant high-frequency accelerations, which we term ego-vibrations. This paper presents an approach to characterizing and removing these signals from acceleration measurements. We adapt the audio processing technique of spectral subtraction over short time windows to remove the noise that is estimated to occur at the robot's present joint velocities. Implementation for the wrist roll and gripper joints on a Willow Garage PR2 robot demonstrates that spectral subtraction significantly increases signal-to-noise ratio, which should improve vibrotactile event detection in both teleoperation and autonomous robotics.

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Section: Spectral Subtractionmentioning

confidence: 99%

“…The basic idea of spectral subtraction is that noise in signals can be removed by transforming to the frequency domain and subtracting out an estimate of the noise spectrum. Additionally, Ince et al have successfully applied this technique to the similar problem of audible robot motion noise in robot audition [10,11].…”

Section: Introductionmentioning

confidence: 99%

Spectral Subtraction of Robot Motion Noise for Improved Event Detection in Tactile Acceleration Signals

McMahan

Kuchenbecker

2012

Haptics: Perception, Devices, Mobility, and Communication

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“…The trained network had to predict the noise spectrum from angular velocities of the joints of the robot. Ince et al [1] proposed to use parameterized template subtraction which incorporates tunable parameters to cope with noise template representations that do not match to the instantaneous noise due to the deviations in the noise spectra. However, these methods suffer from the distorting effects of musical noise [5], a phenomenon that occurs when noise estimation fails.…”

Section: A Comparison To Related Workmentioning

confidence: 99%

“…Secondly, they utilized blockwise templates which cannot cope with dynamic changes of the motion trajectories in time. Our approach overcomes the former problem by introducing an SNR-based weighting of mask generation, whereas the latter drawback is tackled by a parameterized template prediction method as in [1].…”

Section: A Comparison To Related Workmentioning

confidence: 99%

“…One special type of ego noise, which is observed while the robot is executing some motions using its motors, is called egomotion noise. As explained and quantified in [1], it causes drastic reduction in both speech recognition and sound localization accuracies. This type of noise, however, is so far either ignored or circumvented by a close-talk microphone, because the problem is rather challenging due to the complex characteristics of this particular noise.…”

Section: Introductionmentioning

confidence: 99%

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Multi-talker speech recognition under ego-motion noise using Missing Feature Theory

İnce

Nakadai

Rodemann

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a system that gives a mobile robot the ability to recognize target speaker's speech, even if the robot performs an action and there are multiple speakers talking in the room. Associated problems to this system are twofold: (1) While the robot is moving, the joints inevitably generate ego-motion noise due to its motors. (2) Recognizing target speech against other interfering speech signals is a difficult task. Since typical solutions to (1) and (2), motor noise suppression and sound source separation, both introduce distortion to the processed signals, the performance of automatic speech recognition (ASR) deteriorates. Instead of removing the ego-motion noise with conventional noise suppression methods, in this work, we investigate methods to eliminate the unreliable parts of the audio features that are contaminated by the ego-motion noise. For this purpose, we model masks that filter unreliable speech features based on the ratio of speech and motor noise energies. We analyze the performance of the proposed technique under various test conditions by comparing it to the performance of existing Missing Feature Theory-based ASR implementations. Finally, we propose an integration framework for two different masks that are designed to eliminate ego noise and to filter the leakage energy of interfering sound sources. We demonstrate that the proposed methods achieve a high ASR accuracy.

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Robot Ego‐Noise Suppression with Labanotation‐Template Subtraction

Jaroslavceva

Wake

Sasabuchi

et al. 2021

IEEJ Transactions Elec Engng

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In this study, we aim to improve automatic-speech-recognition (ASR) accuracy in the presence of robot ego-noise toward a better human-robot interaction. Although several noise reduction methods have been proposed to increase ASR accuracy or signal-to-noise ratio (SNR) by predicting ego-noises through a short-time motion-template subtraction or a neural network, these methods showed poor performance in some practical use cases, such as attenuating long-term motion-associated ego-noise. Based on the motion-template subtraction method, we address the problem of creating ego-noise templates associated with a wide variety of robot motions. For representing robot motions, we employ a dance notation referred to as Labanotation. The rationales behind our approach are: (i) Labanotation allows quantizing infinite motion patterns using a finite number of Labanotation combinations; (ii) Labanotation-based motion description is hardware-independent; and (iii) long-time noise templates facilitate the localization of noise templates in a speech-with-noise signal compared to short-time templates. The effectiveness of the Labanotation-template subtraction (LTS) method was tested for five commercial ASRs in terms of ASR accuracy, SNR, and source-to-distortion ratio. We show that LTS leads to a reasonable performance, comparable to the other methods. The contribution of this study is (i) to propose to use Labanotation to reasonably collect noise templates, (ii) to demonstrate the practical effectiveness of LTS as well as examples of Labanotations for household actions.

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Ego noise suppression of a robot using template subtraction

Cited by 43 publications

References 9 publications

Spectral Subtraction of Robot Motion Noise for Improved Event Detection in Tactile Acceleration Signals

Spectral Subtraction of Robot Motion Noise for Improved Event Detection in Tactile Acceleration Signals

Multi-talker speech recognition under ego-motion noise using Missing Feature Theory

Robot Ego‐Noise Suppression with Labanotation‐Template Subtraction

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