In this paper we address the task of hierarchical bird species identification from audio recordings. We evaluate three types of approaches to deal with hierarchical classification problems: the flat classification app...
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(纸本)9781479906505
In this paper we address the task of hierarchical bird species identification from audio recordings. We evaluate three types of approaches to deal with hierarchical classification problems: the flat classification approach, the local-model per parent node classifier approach and the global-model hierarchical-classification approach. For the flat and local-model classification approach we employ the classic Naive Bayes algorithm. For the global-model approach we use the Global Model Naive Bayes (GMNB) algorithm. As in the classical Naive Bayes, the algorithm computes prior probabilities and likelihoods, but these computations take into account the hierarchical classification scenario: it assumes that any example which belongs to a given class will also belong to all its ancestor classes. In the current application, the employed class hierarchy is the standard scientific taxonomy of birds used in Biology. In order to deal with the bird songs we obtain features by computing several acoustic quantities from intervals of the audio signal. We conduct three experiments in order to compare the three different approaches to the hierarchical bird species identification problem. Our experimental results show that the use of the GMNB hierarchical classification algorithm outperforms both the flat and local-model approaches (Using the Hierarchical F-measure metric);hence the use of a global-model approach (such as the GMNB) can be a feasible way to improve the classification performance for problems with a large number of classes.
Particle Swarm Optimization (PSO) is based on the analysis of emergent behavior of bird flocks. Though it was originally designed for continuous optimization, PSO has provided good results in some recent works when ap...
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Particle Swarm Optimization (PSO) is based on the analysis of emergent behavior of bird flocks. Though it was originally designed for continuous optimization, PSO has provided good results in some recent works when applied to static and discrete optimization problems. In this paper, the particle encoding scheme is based on permutations and the PSO algorithm is adapted to solve a real-world application (cabs-customers allocation) of the dynamic task assignment problem. In the proposed approach, as the optimal solution may change during the optimization process, different strategies to detect and react to changes are tested. The results show that combinations of traditional techniques achieve good solutions in tested instances defined with different sizes and scales of changes.
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