Enhanced robot audition by dynamic acoustic sensing in moving humanoids

Tourbabin, Vladimir; Barfuss, Hendrik; Rafaely, Boaz; Kellermann, Walter

doi:10.1109/icassp.2015.7179048

Cited by 9 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. While this approach has already been applied to ground robots [31], [32], its performance for MAV ego-noise, which is nonstationary and stronger, has not been reported yet. Reference-based methods use reference microphones installed close to the propellers to pick up motor noises and then cancel them out with an adaptive filter [13], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Cavallaro

2017

IEEE Sensors J.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Wang

Cavallaro

2017

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…Both ILD and IPD are known to be subject-dependent and frequency-dependent cues. This is captured by the so-called head related transfer functions (HRTFs) determined by the shape Audio-motor integration [8,9,20,30,31,33,34,38,41,48,55,60,61] Active audition [4,6,39,40,45,67]…”

Section: Audio-motor Integration In Psychophysics and Roboticsmentioning

confidence: 99%

“…beamforming. This idea was recently further exploited in [4,67] using a so-called robomorphic array, where microphones are placed on a robot's limbs. In [39], it is showed that turning the head of a binaural robotic system towards an estimated source direction enhances sound localization performance.…”

Section: Figure 21 Auditory-motor System Componentsmentioning

confidence: 99%

Audio-motor integration for robot audition

Deleforge

Schmidt

Kellermann

2019

Multimodal Behavior Analysis in the Wild

Self Cite

View full text Add to dashboard Cite

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

“…A number of authors have found it convenient to use a spherical harmonic (SH) description of the plane-wave density (PWD) of a sound field [22] because it is compact and straightforwardly accommodates rotation. In [23], a sound field is sampled at multiple time instants and positions by rotating and/or translating a spherical microphone array. Under the assumption that the sound field is stationary, successive observations can be combined, leading to a higher order SH description of the sound field than could be obtained using a stationary array.…”

Section: Introductionmentioning

confidence: 99%

Noise Covariance Matrix Estimation for Rotating Microphone Arrays

Moore

Xue

Naylor

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

The noise covariance matrix computed between the signals from a microphone array is used in the design of spatial filters and beamformers with applications in noise suppression and dereverberation. This paper specifically addresses the problem of estimating the covariance matrix associated with a noise field when the array is rotating during desired source activity, as is common in head-mounted arrays. We propose a parametric model which leads to an analytical expression for the microphone signal covariance as a function of the array orientation and array manifold. An algorithm for estimating the model parameters during noise-only segments is proposed and the performance shown to be improved, rather than degraded, by array rotation. The stored model parameters can then be used to update the covariance matrix to account for the effects of any array rotation that occurs when the desired source is active. The proposed method is evaluated in terms of the Frobenius norm of the error in the estimated covariance matrix and of the noise reduction performance of a minimum variance distortionless response beamformer. In simulation experiments the proposed method achieves 18 dB lower error in the estimated noise covariance matrix than a conventional recursive averaging approach and results in noise reduction which is within 0.05 dB of an oracle beamformer using the ground truth noise covariance matrix.

show abstract

Enhanced robot audition by dynamic acoustic sensing in moving humanoids

Cited by 9 publications

References 13 publications

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

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

Audio-motor integration for robot audition

Noise Covariance Matrix Estimation for Rotating Microphone Arrays

Contact Info

Product

Resources

About