Department Publications

In this paper, we consider the problem of recovering desired sound source signals from on-board microphone recordings on a noisy drone. Enhancement of source signal degraded by drone noise is considered to be a difficult task due to the strong noise generated from its motors and propellers causing an extremely low signal-to-drone noise ratio (S D ‾ NR). We propose a solution (i) by combining the widely known multichannel Wiener filter (MWF) to remove drone noise from microphone recordings, and (ii) further reduction of residual noise using a Gaussian mixture model (GMM) based dual-stage parametric Wiener filter (WF). The method exploits known statistics of motor current-specific drone noise. This combination of techniques to the specific context of signal enhancement for drone audition is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We …

The ability to accurately localize vehicles is essential for driver assistance applications. Existing modern localization systems, while sophisticated, often come with considerable costs and limitations in accessibility. Recognizing the need for a more accessible solution, there is a growing demand for user-friendly and cost-effective alternatives. One such alternative lies in leveraging mobile devices for localization. However, relying solely on Global Positioning System (GPS), Wi-Fi, or cellular networks, the most commonly used technologies in mobile device localization, often suffer from issues with accuracy. Addressing these drawbacks is crucial to ensuring its effectiveness and widespread adoption. Map matching is a commonly pursued and well-researched approach taken to rectify these erroneous location data. This paper presents the implementation of a hybrid of two map-matching implementations on a mobile …