Unmanned aerial vehicles (UAVs) have recently gained huge popularity across a wide range of applications, including filming, search and rescue, and surveillance. Such applications take advantage of capturing visual information (i.e. video and imagery) that are otherwise impossible without making use of UAVs. On the other hand, audio signals are also one that should not be overlooked. It is common to encounter environments that are often remote and harsh, which can easily render visual information unusable. This is not the case with audio. However, audio recording using UAVs have shown to be challenging due to the high noise levels radiated from the UAV rotors. This significantly affects the quality of the audio signals to aid with any application.

The problem has been tackled by the Acoustics Research Centre (ARC), UoA, for which a UAV system with quiet rotors, equipped with an array of microphones and a signal processing algorithm, was developed to effectively record desired audio in-flight while removing the UAV rotor noise. Recently, a method based on machine learning was used to explore possibilities of predicting UAV rotor noise with a hybrid of microphone and non-acoustical information. However, a common problem with such data-driven system is the lack of transparency between the inputs and the result it produces. To this end, studies have been made to unravel these ambiguities with the help of analytical modelling. This project will focus on incorporating these analytical findings to optimise the current signal processing algorithm.

Publications/conference presentations relevant to this research

Journal articles/Book chapters

• B. Yen, Y. Li, and Y. Hioka. Rotor noise-aware noise covariance matrix estimation for unmanned aerial vehicle audition. IEEE/ACM Transactions on Audio, Speech and Language Processing, vol.31, pp. 2491 – 2506, 2023. Publisher link
• B. Yen, Y. Hioka, G. Schmid, and B. Mace.  Multi-sensory sound source enhancement for unmanned aerial vehicle recordings. Applied Acoustics, 189:108590, February 2022. Publisher link
• Y. Hioka, B. Yen, R. McKay, M. Kingan, Clean audio recording using unmanned aerial vehicles, in A. Koubaa and A.T. Azar Eds, Unmanned Aerial Systems – Theoretical Foundation and Applications, Academic Press, 175-202, 2021. Publisher link
• B. Yen and Y. Hioka. Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles. EURASIP Journal on Audio, Speech, and Music Processing, 2020:13, 2020. Publisher link
• Y. Hioka, M. Kingan, G. Schmid, R. McKay, and K. Stol. Design of an unmanned aerial vehicle mounted system for quiet audio recording. Applied Acoustics, 155:423–427, December 2019. Publisher link

Conference proceedings

• Y. Li, B. Yen, and Y. Hioka. Improvement of Rotor Noise Reduction for Unmanned Aerial Vehicle Audition by Rotor Noise PSD Informed Beamformer Design. Quiet Drones 2022, June 2022. (Presentation video)
• Y. Li, B. Yen, and Y. Hioka. Performance evaluation on multi-channel Wiener filter based speech enhancement for unmanned aerial vehicles recordings. Internoise 2021, August 2021. (Presentation video)
• B. Yen, Y. Hioka and B. Mace. Source enhancement for unmanned aerial vehicle recording using multi-sensory information. In Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2020 (APSIPA2020), December 2020. Conference proceedings link
• B. Yen, Y. Hioka, and B. Mace. Improving power spectral density estimation of unmanned aerial vehicle rotor noise by learning from non-acoustic information. In 16th International Workshop on Acoustic Signal Enhancement (IWAENC), 545–549, September 2018. Conference proceedings link
• B. Yen, Y. Hioka , and B. Mace. Estimating power spectral density of unmanned aerial vehicle rotor noise using multisensory information. In 26th European Signal Processing Conference (EUSIPCO 2018), 2434–2438, September 2018. Conference proceedings link
• Y. Hioka, M. Kingan, G. Schmid, and K.A. Stol. Speech enhancement using a microphone array mounted on an unmanned aerial vehicle. In 15th International Workshop on Acoustic Signal Enhancement (IWAENC), September 2016. Conference proceedings link
  

 Video demo of audio recording UAV