Online learning for template-based multi-channel ego noise estimation

İnce, Gökhan; Nakadai, Kazuhiro; Nakamura, Keisuke

doi:10.1109/iros.2012.6385824

Cited by 11 publications

(9 citation statements)

References 12 publications

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“…Templatebased methods build a noise template database from which the spectrum [23] or the correlation matrix [28] of the ego-noise can be estimated corresponding to the motor rotation speed and the MAV behaviour. The estimated ego-noise information can be used to design single-channel spectral filters [23] or multichannel adaptive beamformer [29], [30] for ego-noise reduction, and can also be applied to noise-robust source localization [28]. To avoid using monitoring sensors, nonnegative matrix factorization can be employed to learn noise bases from pre-recorded training data and then to estimate the noise spectrum online from the noisy recording.…”

Section: Introductionmentioning

confidence: 99%

Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles

Wang

Cavallaro

2017

IEEE Sensors J.

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When a micro aerial vehicle (MAV) captures sounds emitted by a ground or aerial source, its motors and propellers are much closer to the microphone(s) than the sound source, thus leading to extremely low signal-to-noise ratios (SNR), e.g.-15 dB. While microphone-array techniques have been investigated intensively, their application to MAV-based ego-noise reduction has been rarely reported in the literature. To fill this gap, we implement and compare three types of microphonearray algorithms to enhance the target sound captured by an MAV. These algorithms include a newly emerged technique, time-frequency spatial filtering, and two well-known techniques, beamforming and blind source separation. In particular, based on the observation that the target sound and the ego-noise usually have concentrated energy at sparsely isolated timefrequency bins, we propose 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 time-frequency bins. By exploiting the time-frequency sparsity of the acoustic signal, this spatial filter works robustly for sound enhancement in the presence of strong ego-noise. We analyze in details the three techniques and conduct a comparative evaluation with real-recorded MAV sounds. Experimental results show the superiority of blind source separation and timefrequency filtering in low-SNR scenarios.

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Section: Introductionmentioning

confidence: 99%

Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles

Wang

Cavallaro

2017

IEEE Sensors J.

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“…However, applying these methods to a moving MAV is challenging because the acoustic mixing network changes dynamically. Template-based approaches [15,16] construct noise correlation matrices as a function of the behavior of the robot and use them to design an adaptive beamformer. This can be seen as a multi-channel extension of the singlechannel template [10], which only considers the spectral amplitude.…”

Section: Related Workmentioning

confidence: 99%

Ear in the sky: Ego-noise reduction for auditory micro aerial vehicles

Wang

Cavallaro

2016

2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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We investigate the spectral and spatial characteristics of the ego-noise of a multirotor micro aerial vehicle (MAV) using audio signals captured with multiple onboard microphones and derive a noise model that grounds the feasibility of microphone-array techniques for noise reduction. The spectral analysis suggests that the ego-noise consists of narrowband harmonic noise and broadband noise, whose spectra vary dynamically with the motor rotation speed. The spatial analysis suggests that the ego-noise of a Protor MAV can be modeled as P directional noises plus one diffuse noise. Moreover, because of the fixed positions of the microphones and motors, we can assume that the acoustic mixing network of the ego-noise is stationary. We validate the proposed noise model and the stationary mixing assumption by applying blind source separation to multi-channel recordings from both a static and a moving MAV and quantify the signal-to-noise ratio improvement. Moreover, we make all the audio recordings publicly available.

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“…By doing so, spectral subtraction can be applied on the audio spectrum of each individual sound source (e.g., music, speech) using its corresponding ego noise spectrum. The details of this block can be found in our complementary paper [16]. In addition, a power threshold filter was applied atop of this ego noise suppression scheme for handling unpredictable robot noises (e.g., jittering).…”

Section: A Preprocessing and Speech Processingmentioning

confidence: 99%

“…In order to increase the disturbing effects of the robot's ego noise, the dance motions were designed to simultaneously move 6 joints: the shoulders pitch and yaw, and the elbows pitch (see Fig. 2(a)); each with a rotational variation in the range of [10][11][12][13][14][15][16][17][18][19][20] • to maximize the number of transitions. During recordings the dance motions were continuously generated into a full dance sequence by using a uniform number of periodic repetitions of the 3 dances.…”

Section: Periodic Dance Motionsmentioning

confidence: 99%

Live assessment of beat tracking for robot audition

Oliveira

İnce

Nakamura

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper we propose the integration of an online audio beat tracking system into the general framework of robot audition, to enable its application in musically-interactive robotic scenarios. To this purpose, we introduced a staterecovery mechanism into our beat tracking algorithm, for handling continuous musical stimuli, and applied different multi-channel preprocessing algorithms (e.g., beamforming, ego noise suppression) to enhance noisy auditory signals lively captured in a real environment. We assessed and compared the robustness of our audio beat tracker through a set of experimental setups, under different live acoustic conditions of incremental complexity. These included the presence of continuous musical stimuli, built of a set of concatenated musical pieces; the presence of noises of different natures (e.g., robot motion, speech); and the simultaneous processing of different audio sources on-the-fly, for music and speech. We successfully tackled all these challenging acoustic conditions and improved the beat tracking accuracy and reaction time to music transitions while simultaneously achieving robust automatic speech recognition.

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Online learning for template-based multi-channel ego noise estimation

Cited by 11 publications

References 12 publications

Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles

Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles

Ear in the sky: Ego-noise reduction for auditory micro aerial vehicles

Live assessment of beat tracking for robot audition

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