http://www.mdpi.com/journal/algorithms/special_issues/sound-localization/ Submission All papers should be submitted to algorithms@mdpi.com. To be published continuously until the deadline and papers will be listed together at the special issue website. Submitted papers should not have been published nor be under consideration for publication elsewhere. All papers are refereed through a peer-review process. A guide for authors is available on the Instructions for Authors page. Algorithms is an international peer-reviewed quarterly journal published by MDPI. Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first three volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections) for papers submitted before 31 December 2010. http://www.mdpi.com/1999-4893/2/4/1410/ Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks. http://www.mdpi.com/1999-4893/2/4/1410/ Wed, 18 Nov 2009 00:00:00 CET Algorithms 2009-11-18 2 4 Article 1410 1428 1999-4893 A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models 2009-11-18 doi: 10.3390/a2041410 Yao Ren Michael T. Johnson Patrick J. Clemins Michael Darre Sharon Stuart Glaeser Tomasz S. Osiejuk Ebenezer Out-Nyarko http://www.mdpi.com/1999-4893/2/3/1232/ With the aim of classifying sperm whales, this report compares two methods that can use Gaussian functions, a radial basis function network, and support vector machines which were trained with two different approaches known as C-SVM and ν-SVM. The methods were tested on data recordings from seven different male sperm whales, six containing single click trains and the seventh containing a complete dive. Both types of classifiers could distinguish between the clicks of the seven different whales, but the SVM seemed to have better generalisation towards unknown data, at the cost of needing more information and slower performance. http://www.mdpi.com/1999-4893/2/3/1232/ Tue, 22 Sep 2009 00:00:00 CEST Algorithms 2009-09-22 2 3 Article 1232 1247 1999-4893 Classification of Sperm Whale Clicks (Physeter Macrocephalus) with Gaussian-Kernel-Based Networks 2009-09-22 doi: 10.3390/a2031232 Mike Van der Schaar Eric Delory Michel André