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Drone Audition: Multi-microphone Signal Enhancement by dual-stage Wiener Filtering
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  • Wageesha Manamperi ,
  • Thushara Abhayapala ,
  • Prasanga Samarasinghe ,
  • Jihui (Aimee) Zhang
Wageesha Manamperi
Australian National University, Australian National University

Corresponding Author:[email protected]

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Thushara Abhayapala
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Prasanga Samarasinghe
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Jihui (Aimee) Zhang
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Abstract

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.