Enhancing listening capability of humanoid robot by reduction of stationary ego‐noise

Wake, Naoki; Fukumoto, Masaaki; Takahashi, Hirokazu; Ikeuchi, Katsushi

doi:10.1002/tee.23008

Cited by 12 publications

(12 citation statements)

References 14 publications

(16 reference statements)

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“…Static noise removal is performed by the application of a SS filter as outlined in Ref. [6]. SS is a single-channel method that estimates and subtracts noise signals in a spectrogram, so that only the nonstationary mechanical noise remains as a final product of the preprocessing chain.…”

Section: Subtraction Of Labanotation-template Noisesmentioning

confidence: 99%

“…In this study, we compared the performance of the proposed LTS method with the conventional static noise SS, explained in our previous work [6], and the three widely used denoising convolutional neural network (CNN) architectures. These CNNs output a denoised speech signal by taking a noisy speech signal as input.…”

Section: Techniques Comparedmentioning

confidence: 99%

“…These factors discourage the use of conventional static noise reduction techniques such as spectral subtraction (SS) [2,3] and Wiener filtering [4,5]. We have previously reported the effectiveness of the SS filter, finite impulse response filter, adaptive multirate wideband, and enhanced variable rate codecs to improve robot speech recognition [6] in the presence of the robot's fan noise. However, these methods are likely to be less effective for nonstationary noise because they assume that the noise is stationary.…”

Section: Introductionmentioning

confidence: 99%

“…Note:The columns are the following: (i) is the WER of initial noisy recordings, (ii) only static noise is removed with the use of SS method[6], (iii) ego-noise is removed via LTS, (iv) naive CNN (Section 3.3), (v) after application of context-window CNN (Section 3.3), (vi) after application of IBMP (Section 3.3). The best results per an ASR service are highlighted in bold.…”

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confidence: 99%

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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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Section: Subtraction Of Labanotation-template Noisesmentioning

confidence: 99%

Section: Techniques Comparedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…Ego-noise is a source of problems in many robotic applications, as it corrupts audio recordings captured by microphones, as available in many Human-Robot Interaction (HRI) systems [2,3]. For this reason, ego-noise reduction is an active area of research that plays an important role in many autonomous systems, and has enabled applications such as speech recognition for HRI [4] or acoustic scene analysis [5].…”

Section: Introductionmentioning

confidence: 99%

Detecting Acoustic Reflectors Using A Robot’s Ego-Noise

Saqib

Deleforge

Jensen

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a method to estimate the proximity of an acoustic reflector, e.g., a wall, using ego-noise, i.e., the noise produced by the moving parts of a listening robot. This is achieved by estimating the times of arrival of acoustic echoes reflected from the surface. Simulated experiments show that the proposed nonintrusive approach is capable of accurately estimating the distance of a reflector up to 1 meter and outperforms a previously proposed intrusive approach under loud ego-noise conditions. The proposed method is helped by a probabilistic echo detector that estimates whether or not an acoustic reflector is within a short range of the robotic platform. This preliminary investigation paves the way towards a new kind of collision avoidance system that would purely rely on audio sensors rather than conventional proximity sensors.

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ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application

Wake,

Kanehira,

Sasabuchi

et al. 2023

IEEE Access

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This paper introduces a novel method for translating natural-language instructions into executable robot actions using OpenAI's ChatGPT in a few-shot setting. We propose customizable input prompts for ChatGPT that can easily integrate with robot execution systems or visual recognition programs, adapt to various environments, and create multi-step task plans while mitigating the impact of token limit imposed on ChatGPT. In our approach, ChatGPT receives both instructions and textual environmental data, and outputs a task plan and an updated environment. These environmental data are reused in subsequent task planning, thus eliminating the extensive record-keeping of prior task plans within the prompts of ChatGPT. Experimental results demonstrated the effectiveness of these prompts across various domestic environments, such as manipulations in front of a shelf, a fridge, and a drawer. The conversational capability of ChatGPT allows users to adjust the output via natural-language feedback. Additionally, a quantitative evaluation using VirtualHome showed that our results are comparable to previous studies. Specifically, 36% of task planning met both executability and correctness, and the rate approached 100% after several rounds of feedback. Our experiments revealed that ChatGPT can reasonably plan tasks and estimate postoperation environments without actual experience in object manipulation. Despite the allure of ChatGPTbased task planning in robotics, a standardized methodology remains elusive, making our work a substantial contribution. These prompts can serve as customizable templates, offering practical resources for the robotics research community. Our prompts and source code are open source and publicly available at https://github.com/microsoft/ChatGPT-Robot-Manipulation-Prompts.

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Enhancing listening capability of humanoid robot by reduction of stationary ego‐noise

Cited by 12 publications

References 14 publications

Robot Ego‐Noise Suppression with Labanotation‐Template Subtraction

Robot Ego‐Noise Suppression with Labanotation‐Template Subtraction

Detecting Acoustic Reflectors Using A Robot’s Ego-Noise

ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application

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