Pranjeevan Kulasingham, Christian Brodbeck, Alessandro Presacco, Stefanie E Kuchinsky, Samira Anderson, Jonathan Z Simon
Journal
Neuroimage , vol. 222 , pp. 117291 · 2020 · Cited by 75
Speech, Audio and Acoustic ProcessingBiosignal Processing
Abstract
Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from ∼100 Hz to several hundred Hz, phase-locking to the acoustic stimulus at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover case, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed responses in the high gamma range of 70–200 Hz to continuous speech using neural source-localized reverse correlation and the …
Wageesha Manamperi, Thushara D Abhayapala, Jihui Aimee Zhang, Prasanga N Samarasinghe
Conference
2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) , pp. 156-160 · 2020 · Cited by 8
Digital Signal ProcessingSpeech, Audio and Acoustic ProcessingUAVs and Aerial Robotics
Abstract
The potential of drone based services is enormous with applications ranging from consumer product delivery to health services. When meeting this demand, one of the major challenges we face is the noise produced by drones, which not only contributes to listener discomfort, but also hinders the device's ability to effectively communicate via audio. Thus, there exists a pressing need for understanding the characteristics of drone related noise, which can then be suppressed using suitable methods. This paper presents a preliminary study on modeling the relationship between input motor current and acoustic noise produced by a drone. An experimental study is conducted indoors for a drone under hovering manoeuvre with a single active motor and propeller. The drone noise was measured by a single on-board microphone. We identify multiple tones or harmonics in the drone noise spectrum that vary proportionally …