A comparison of backpropagation and statistical classifiers for bird identification

Abstract

We compare neural networks and statistical methods used to identify birds by their songs. Six birds native to Manitoba were chosen which exhibited overlapping characteristics in terms of frequency content, song components and length of songs. Songs from multiple individuals in each species were employed. These songs were analyzed using backpropagation learning in two-layer perceptrons, as well as methods from multivariate statistics including quadratic discriminant analysis. Preprocessing methods included linear predictive coding and windowed Fourier transforms. Generalization performance ranged from 82% to 93% correct identification, with the lower figures corresponding to smaller networks that employed more preprocessing for dimensionality reduction. Computational requirements were significantly reduced in the later case.