2016/03/23 by Dan Stowell, Stowell, Dan, Veronica Morfi +3
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Environmental Science · #Animal Behavior and Reproduction #Animal Vocal Communication and Behavior #FOS: Computer and information sciences #Marine animal studies overview #Sound (cs.SD)
paper · pdf · doi:10.48550/arxiv.1603.07236
openalex publication_date 2016/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bird calls range from simple tones to rich dynamic multi-harmonic structures. The more complex calls are very poorly understood at present, such as those of the scientifically important corvid family (jackdaws, crows, ravens, etc.). Individual birds can recognise familiar individuals from calls, but where in the signal is this identity encoded? We studied the question by applying a combination of feature representations to a dataset of jackdaw calls, including linear predictive coding (LPC) and adaptive discrete Fourier transform (aDFT). We demonstrate through a classification paradigm that we can strongly outperform a standard spectrogram representation for identifying individuals, and we apply metric learning to determine which time-frequency regions contribute most strongly to robust individual identification. Computational methods can help to direct our search for understanding of these complex biological signals.