Proceedings ArticleDOI
Online learning for template-based multi-channel ego noise estimation
Gkhan Ince,Kazuhiro Nakadai,Keisuke Nakamura +2 more
- pp 3282-3287
TLDR
This paper presents a system that gives a robot the ability to diminish its own disturbing noise by utilizing template-based ego noise estimation, an algorithm previously developed by the authors.Abstract:
This paper presents a system that gives a robot the ability to diminish its own disturbing noise (i.e., ego noise) by utilizing template-based ego noise estimation, an algorithm previously developed by the authors. In pursuit of an autonomous, online and adaptive template learning system in this work, we specifically focus on eliminating the requirement of an offline training session performed in advance to build the essential templates, which represent the ego noise. The idea of discriminating ego noise from all other sound sources in the environment enables the robot to learn the templates online without requiring any prior information. Based on the directionality/diffuseness of the sound sources, the robot can easily decide whether the template should be discarded because it is corrupted by external noises, or it should be inserted into the database because the template consists of pure ego noise only. Furthermore, we aim to update the template database optimally by introducing an additional time-variant forgetting factor parameter, which provides a balance between adaptivity and stability of the learning process automatically. Moreover, we enhanced the single-channel noise estimation system to be compatible with the multi-channel robot audition framework so that ego noise can be eliminated from all signals stemming from multiple sound sources respectively. We demonstrate that the proposed system allows the robot to have the ability of online template learning as well as a high performance of noise estimation and suppression for multiple sound sources.read more
Citations
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Journal ArticleDOI
Acoustic Sensing From a Multi-Rotor Drone
Lin Wang,Andrea Cavallaro +1 more
TL;DR: A DOA-weighted spatial likelihood function that improves source localization performance by identifying noiseless sectors in the DOA circle is proposed and experimentally validate the performance of the proposed method with two array placements.
Journal ArticleDOI
Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles
Lin Wang,Andrea Cavallaro +1 more
TL;DR: In this article, the authors proposed to use the time-frequency processing approach, which formulates a spatial filter that can enhance a target direction based on local direction of arrival estimates at individual timefrequency bins.
Proceedings ArticleDOI
Ear in the sky: Ego-noise reduction for auditory micro aerial vehicles
Lin Wang,Andrea Cavallaro +1 more
TL;DR: The spectral and spatial characteristics of the ego-noise of a multirotor micro aerial vehicle (MAV) using audio signals captured with multiple onboard microphones are investigated and a noise model is derived that grounds the feasibility of microphone-array techniques for noise reduction.
Proceedings ArticleDOI
Time-frequency processing for sound source localization from a micro aerial vehicle
Lin Wang,Andrea Cavallaro +1 more
TL;DR: Experimental results with real-recorded MAV ego-noise show the superiority of the proposed time-frequency processing framework over the state of the art in performing source localization robustly.
Proceedings ArticleDOI
Tracking a moving sound source from a multi-rotor drone
TL;DR: A method to track from a multi-rotor drone a moving source, such as a human speaker or an emergency whistle, whose sound is mixed with the strong ego-noise generated by rotating motors and propellers is proposed.
References
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Explanation-Based Neural Network Learning: A Lifelong Learning Approach
TL;DR: The Explanation-Based Neural Network Learning (EBNN) as discussed by the authors is a machine learning algorithm that transfers knowledge across multiple learning tasks when faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one.
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Blind Source Separation With Parameter-Free Adaptive Step-Size Method for Robot Audition
TL;DR: The proposed adaptive step-size method for blind source separation suitable for robot audition systems has the following merits: low computational cost; no parameters to be adjusted manually; and no additional preprocessing requirements.
Proceedings ArticleDOI
Internal Noise Suppression for Speech Recognition by Small Robots
TL;DR: Two new methods are proposed that suppresses internal noise of the small robots by using the estimated noise spectrum dependent on the motion of the robot to prepare the noise spectrums for all motions.