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Monthly Archives: August 2016
Ear in the sky: Ego-noise reduction for auditory micro aerial vehiclesEar in the sky: Ego-noise reduction for auditory micro aerial vehicles
- Lin Wang and Andrea Cavallaro. Ear in the sky: Ego-noise reduction for auditory micro aerial vehicles. In Proceedings of 13th IEEE International Conference on Advanced Signal and Video based Surveillance (AVSS), Colorado Springs, USA, 2016. doi:10.1109/AVSS.2016.7738063
[BibTeX] [Abstract]
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 P-rotor 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.
@InProceedings{2016-08-WANG, title = {{Ear in the sky: Ego-noise reduction for auditory micro aerial vehicles}}, author = {Lin Wang and Andrea Cavallaro}, booktitle = {{Proceedings of 13th IEEE International Conference on Advanced Signal and Video based Surveillance (AVSS)}}, address= {Colorado Springs, USA}, date = {2016-08-23/2016-08-26}, doi = {10.1109/AVSS.2016.7738063}, year = {2016}, abstract = {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 P-rotor 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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Autonomous robotic cameras for collaborative target localization
- Andrea Cavallaro. Autonomous robotic cameras for collaborative target localization. Invited talk at IEEE AVSS 2016 Workshop on Surveillance for Location-aware Data Protection, 2016.
[BibTeX] [Download handouts]@Misc{2016-08-CAVALLARO, author = {Andrea Cavallaro}, title = {{Autonomous robotic cameras for collaborative target localization}}, howpublished = {Invited talk at IEEE AVSS 2016 Workshop on Surveillance for Location-aware Data Protection}, date = {2016-08-23}, year = {2016}, address = {Colorado Springs, CO, USA}, handouts = {http://www.eecs.qmul.ac.uk/~andrea/dwnld/2016.08.23_ColoradoSprings_AutonomousRoboticCameras.pdf} }
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