2022/08/09 by Leah Chowenhill, Gaurav Satyanath, Chowenhill, Leah +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Animal Vocal Communication and Behavior #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Signal Processing (eess.SP) #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.00569
openalex publication_date 2022/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Few-shot learning is a type of classification through which predictions are made based on a limited number of samples for each class. This type of classification is sometimes referred to as a meta-learning problem, in which the model learns how to learn to identify rare cases. We seek to extract information from five exemplar vocalisations of mammals or birds and detect and classify these sounds in field recordings [2]. This task was provided in the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge of 2021. Rather than utilize deep learning, as is most commonly done, we formulated a novel solution using only machine learning methods. Various models were tested, and it was found that logistic regression outperformed both linear regression and template matching. However, all of these methods over-predicted the number of events in the field recordings.