Department Publications

In disaster-stricken environments, it’s vital to assess the damage quickly, analyse the stability of the environment, and allocate resources to the most vulnerable areas where victims might be present. These missions are difficult and dangerous to be conducted directly by humans. Using the complementary capabilities of both the ground and aerial robots, we investigate a collaborative approach of aerial and ground robots to address this problem. With an increased field of view, faster speed, and compact size, the aerial robot explores the area and creates a 3D feature-based map graph of the environment while providing a live video stream to the ground control station. Once the aerial robot finishes the exploration run, the ground control station processes the map and sends it to the ground robot. The ground robot, with its higher operation time, static stability, payload delivery and tele-conference capabilities, can then …

In this paper, we consider the problem of recovering speech from audio recordings of on-board microphones in a noisy drone platform. Enhancement of speech 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 (SdNR). We propose a solution by (i) developing a 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 the known statistics of motor current-specific drone noise. The theory developed is applicable to irregular microphone array embedded in a drone enabling realistic integration to most drones. We evaluate the proposed framework using two different drone acoustics datasets under extreme SdNR levels including− 30 dB. The experimental results confirm promising performance in terms of SdNR improvement, speech quality (PESQ), and intelligibility (STOI) and show a strong potential for speech enhancement applications using noisy drones.

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 (SDNR).  We propose a solution (i) by using a 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.  The theory developed is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We demonstrate the validity of the proposed framework through (i) experimental recordings from two …