Introduction

This introduction to the book Teacher Education for High Poverty Schools provides an international snapshot of important advances in both theory and practice related to the preparation of teachers for high poverty schools. This collection brings together some of the most significant researchers in the field of teacher education for high poverty schools and it is our hope that the book serves as testament to a diverse range of successful attempts to address key aspects within teacher education. Representing the US, Canada, Australia, Chile, South Africa and the UK, this collection profiles an assortment of theory, ideas and approaches from a range of countries.

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.