Bio

My research focuses on animal sound and machine learning (AI). I develop methods to understand animal behaviour and monitor animal populations, using modern computational methods to uncover new evidence from audio data.

Collaboration

My position is a joint appointment with Naturalis Biodiversity Centre, via JADS. We are building collaborations for large-scale monitoring of biodiversity using multimedia and deep learning.

Recent publications

  1. The potential for acoustic individual identification in mammals

    Linhart, P., Mahamoud-Issa, M., Stowell, D., & Blumstein, D. T. (2022). The potential for acoustic individual identification in mammals. Mammalian Biology.
  2. Computational bioacoustics with deep learning: a review and roadmap

    Stowell, D. (2022). Computational bioacoustics with deep learning: a review and roadmap. PEERJ, [13152].
  3. Few-shot bioacoustic event detection: A new task at the DCASE 2021 ch…

    Morfi, V., Nolasco, I., Lostanlen, V., Singh, S., Strandburg-Peshkin, A., Gill, L. F., Pamuła, H., Benvent, D., & Stowell, D. (2021). Few-shot bioacoustic event detection: A new task at the DCASE 2021 challenge. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Morfi_52.pdf
  4. Acoustic traits of bat-pollinated flowers compared to flowers of othe…

    Simon, R., Yovel, Y. (Ed.), Bakunowski, K., Reyes-Vasques, A. E., Tschapka, M., Knörnschild, M., Steckel, J., & Stowell, D. (2021). Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks. PLOS Computational Biology, 17(12), [1009706].
  5. Deep perceptual embeddings for unlabelled animal sound events

    Stowell, D. (2021). Deep perceptual embeddings for unlabelled animal sound events. Journal of the Acoustical Society of America, 150(1), 2-11.

Find an expert or expertise