Acoustic front-end optimization for bird species recognition

Abstract

The goal of this work was to explore the optimization of the feature extraction module (front-end) parameters to improve bird species recognition. We explored optimizing the spectral and temporal parameters of a Mel cepstrum feature-based front-end, starting from common parameter values used in speech processing experiments. These features were modeled using a Gaussian mixture model (GMM) system. We found an important improvement when increasing the spectral bandwidth and increasing the number of filter banks. We found no improvement when switching the filter bank distribution from the perceptually based Mel frequency scale to a linear frequency scale. In addition, no improvement was found when we either reduced or increased the time resolution. On the other hands, we found that the best time resolution is species dependent. We did find great improvements from a species-specific combination of different font-ends with different time resolutions relative to using the same front-end time resolution for all species.

Keywords: Bird species recognition, acoustic front-end, Gaussian mixture model.