Multi-Label Classifier Chains for Bird Sound

Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species. However, few works have considered the multi-label structure in birdsong. We propose to use an ensemble of classifier chains combined with a histogram-of-segments representation for multi-label classification of birdsong. The proposed method is compared with binary relevance and three multi-instance multi-label learning (MIML) algorithms from prior work (which focus more on structure in the sound, and less on structure in the label sets). Experiments are conducted on two real-world birdsong datasets, and show that the proposed method usually outperforms binary relevance (using the same features and base-classifier), and is better in some cases and worse in others compared to the MIML algorithms.Comment: 6 pages, 1 figure, submission to ICML 2013 workshop on bioacoustics. Note: this is a minor revision- the blind submission format has been replaced with one that shows author names, and a few corrections have been mad

Multilabel Classification with R Package mlr

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of randomForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.Comment: 18 pages, 2 figures, to be published in R Journal; reference correcte

Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

In this paper we present our work on Task 1 Acoustic Scene Classi- fication and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.76 compared to the baseline of 0.91

Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their interactions with the environment and other organisms. However, traditional observation methods are labor- intensive. Most prior work on machine learning for bird song is not applicable to real-world acoustic monitoring, because it assumes recordings contain only a single species of bird, while recordings typically contain multiple simultaneously vocalizing birds. We propose to use the multi-instance multi-label (MIML) framework in machine learning for the species classification problem, where the dataset is viewed as a collection of bags of instances paired with sets of labels. Furthermore, we formalize MIML instance annotation, where the goal is to predict instance labels while learning only from bag label sets. We develop the first MIML representation for audio, and several new algorithms for MIML instance annotation based on support vector machines or classifier chains. The proposed methods classify either the set of species present in a recording, or individual calls, while learning only from recordings paired with a set of species. This form of training data requires less human effort to obtain than individually labeled calls. These methods are successfully applied to audio collected in the field which included multiple simultaneously vocalizing species. The proposed algorithms for MIML classification are general, and are also applied to object recognition in images

WASIS - Identificação bioacústica de espécies baseada em múltiplos algoritmos de extração de descritores e de classificação

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A identificação automática de animais por meio de seus sons é um dos meios para realizar pesquisa em bioacústica. Este domínio de pesquisa fornece, por exemplo, métodos para o monitoramento de espécies raras e ameaçadas, análises de mudanças em comunidades ecológicas, ou meios para o estudo da função social de vocalizações no contexto comportamental. Mecanismos de identificação são tipicamente executados em dois estágios: extração de descritores e classificação. Ambos estágios apresentam desafios, tanto em ciência da computação quanto na bioacústica. A escolha de algoritmos de extração de descritores e técnicas de classificação eficientes é um desafio em qualquer sistema de reconhecimento de áudio, especialmente no domínio da bioacústica. Dada a grande variedade de grupos de animais estudados, algoritmos são adaptados a grupos específicos. Técnicas de classificação de áudio também são sensíveis aos descritores extraídos e condições associadas às gravações. Como resultado, muitos sistemas computacionais para bioacústica não são expansíveis, limitando os tipos de experimentos de reconhecimento que possam ser conduzidos. Baseado neste cenário, esta dissertação propõe uma arquitetura de software que acomode múltiplos algoritmos de extração de descritores, fusão entre descritores e algoritmos de classificação para auxiliar cientistas e o grande público na identificação de animais através de seus sons. Esta arquitetura foi implementada no software WASIS, gratuitamente disponível na Internet. Diversos algoritmos foram implementados, servindo como base para um estudo comparativo que recomenda conjuntos de algoritmos de extração de descritores e de classificação para três grupos de animaisAbstract: Automatic identification of animal species based on their sounds is one of the means to conduct research in bioacoustics. This research domain provides, for instance, ways to monitor rare and endangered species, to analyze changes in ecological communities, or ways to study the social meaning of the animal calls in the behavior context. Identification mechanisms are typically executed in two stages: feature extraction and classification. Both stages present challenges, in computer science and in bioacoustics. The choice of effective feature extraction and classification algorithms is a challenge on any audio recognition system, especially in bioacoustics. Considering the wide variety of animal groups studied, algorithms are tailored to specific groups. Classification techniques are also sensitive to the extracted features, and conditions surrounding the recordings. As a results, most bioacoustic softwares are not extensible, therefore limiting the kinds of recognition experiments that can be conducted. Given this scenario, this dissertation proposes a software architecture that allows multiple feature extraction, feature fusion and classification algorithms to support scientists and the general public on the identification of animal species through their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web. A number of algorithms were implemented, serving as the basis for a comparative study that recommends sets of feature extraction and classification algorithms for three animal groupsMestradoCiência da ComputaçãoMestre em Ciência da Computação132849/2015-12013/02219-0CNPQFAPES

Numerical methods for fMRI data analysis

Brain imaging data are increasingly analyzed via a range of machine-learning methods. In this thesis, we discuss three specific contributions to the field of neuroimaging analysis methods: 1. To apply a recently-developed technique for identifying and viewing similarity structure in neuroimaging data, in which candidate representational structures are ranked; 2. Provide side-by-side analyses of neuroimaging data by a typical non-hierarchical (SVM) versus hierarchical (Decision Tree) machine-learning classification methods; and 3. To develop a novel programming environment for PyMVPA, a current popular analysis toolbox, such that users will be able to type a small number of packaged commands to carry out a range of standard analyses. We carried out our analysis with an fMRI data set generated using auditory stimuli. Tree and Ring were the best voted structural representations we obtained by applying the Kemp\u27s algorithm. Machine-learning classification resulted in accuracy values that were similar for both decision tree and SVM algorithms. Coding for different sound categories primarily occurred in the temporal lobes of the brain. We discovered a few non-temporal regions of the brain coding for these auditory sounds as well