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 Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple sources and multiple microphones, shows promising performance when applied to speech denoising and speaker separation in a noisy reverberant room. This work utilizes the ReTM to propose a novel framework for drone noise suppression. Difficulties in noise cancellation for drone audition applications arise due to its self-generated noise that causes an adverse noisy environment of low Signal-to-Drone Noise Ratio (SDNR) levels. In this paper, we divide the drone on-board microphones into two multichannel groups to approximately estimate the drone noise from one group to the other with known drone noise ReTM for denoising. We demonstrate the ReTM spatial mapping ability in both indoor and outdoor experiments using hovering drone on-board microphone recordings with low average …

Multichannel Wiener filter (MWF), commonly used as a spatial filter for noise reduction, exhibits promising performance due to its robustness in multi-channel setups. Additionally, the preservation of spatial cues at the MWF is usually a favorable property that contributes to sound source localization. Previous works have shown that the MWF can be used in drone noise suppression for acoustic sensing tasks in drone audition. In this paper, we analyze the preservation of spatial cues at the MWF from on-board microphone recordings on a drone at very low signal-to-drone noise ratio (SDNR) levels. The analysis builds upon the relative transfer function (ReTF), and explores the spatial information of the desired sound sources such as human speakers and birds through experimental recordings from simulated and real-life environments. We also demonstrate the effectiveness of the preservation of spatial cues at the MWF in an acoustic localization application

Making drones talk is the next step in Human-Unmanned Aerial Vehicle interactions by installing an on-board loudspeaker array. However, due to the drone being loud, it creates an adverse noisy environment of very low signal-to-drone noise ratio (SDNR) levels (less than -10 dB) making it difficult to project a perceptual soundfield to a desired location on the ground. In this paper, we present a preliminary study on developing a sparsity-based interior response model of a commercial loudspeaker that can be used to modify the speech signals before playback through the loudspeaker. We incorporate the ground reflections into the incident response model of the real loudspeaker to simulate the outdoor environment. We validate the proposed model using soundfield recording over a larger region for low frequencies up to 114 Hz.

This thesis explores the acoustic signal processing on drones which can enhance the drone audition functions for search and rescue missions, wildlife monitoring, and video recording for media and filming industries. With the recent emergence of drone-based applications and services, microphone arrays are beginning to be mounted to drones. However, audio capturing using drone-mounted microphone arrays leads to very low signal-to-drone noise ratio (SDNR) recordings (below negative 10 dB) due to the drone-generated motor and propeller noise signals. Hence, this causes a highly adverse noisy environment and degrades the quality and intelligibility of the recorded audio signals. Therefore, there is a growing interest in deriving new signal processing algorithms for the future deployment of the drone audition. To this end, we develop novel methods for spatial soundfield modelling, and spatial audio processing using a microphone array embedded into the body of drones for sound source localisation and audio signal enhancement. To a lesser extent, drone noise characterisation is also studied. We propose modelling of the drone noise harmonics as current to noise transfer function by taking the significant harmonics of the drone noise signature. The model is derived from the experimental measurements of a single motor propeller combination, and validated using extensive error analysis. A cross-correlation-based DOA estimation method with drone noise angular spectrum subtraction is derived for acoustic source localisation. Our approach is to exploit the underlying spatial characteristics of drone noise to mitigate the noise emission by …

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple sources and multiple microphones, shows promising performance when applied to speech denoising and speaker separation in a noisy reverberant room. This work utilizes the ReTM to propose a novel framework for drone noise suppression. Difficulties in noise cancellation for drone audition applications arise due to its self-generated noise that causes an adverse noisy environment of low Signal-to-Drone Noise Ratio (SDNR) levels. In this paper, we divide the drone on-board microphones into two multichannel groups to approximately estimate the drone noise from one group to the other with known drone noise ReTM for denoising. We demonstrate the ReTM spatial mapping ability in both indoor and outdoor experiments using hovering drone on-board microphone recordings with low average magnitude spectrum error. Finally, we validate the method in a real-life environment for source signal enhancement over different SDNR conditions and offer both improved speech intelligibility and signal-to-distortion ratio.