[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120640-en":3,"doc-seo-120640-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120640,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Classification of Dog Barks - a Machine Learning Approach","The study analyzes context-specific and individual-specific acoustic features of dog barks using a new machine-learning algorithm. A sound pool of over 6,000 barks recorded in six communicative situations across multiple identified dogs serves as training data. The algorithm learns which acoustic features, measured from different contexts and individuals, can separate barks into distinct classes. After selecting the best feature set, performance is validated on a classification task using unknown barks, yielding recognition rates above chance (43% for recorded situations and 52% for individual identity).","Anim Cogn  \nDOI 10. 1007/s10071-007-0129-9  \nORIGINAL PAPER  \nClassiWcation of dog barks: a machine learning approach  \nCsaba Molnár · Frédéric Kaplan · Pierre Roy ·  \nFrançois Pachet · Péter Pongrácz · Antal Dóka ·  \nÁdám Miklósi  \nReceived: 6 July 2006 / Revised: 23 November 2007 / Accepted: 13 December 2007  \n© Springer-Verlag 2008  \nAbstract In this study we analyzed the possible contextspeciWc and individual-speciWc features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six diVerent communicative situations was used as the sound sample. The algorithm’s task was to learn which acoustic features of the barks, which were recorded in diVerent contexts and from diVerent individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identiWed contexts by identiWed dogs. After the best feature set had been obtained (with which the highest identiWcation rate was achieved), the eYciency of the algorithm was tested in a classiWcation task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an eYciency of 43% and with an eYciency of 52% of the barking individuals. These Wndings suggest that dog  \nWe cite several conference proceedings because in computer science the conference proceedings are more important than in biology. For engineers, it is essential to show their colleagues that their product (e.g. software) is actually working, so in this Weld of science the main forums for scientiWc discussion are the conferences.  \nC. Molnár (&) · P. Pongrácz · A. Dóka · Á . Miklósi Department of Ethology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary [e-mail: molcsa@gmail.com](e-mail: molcsa@gmail.com)  \nF. Kaplan  \nEcole Polytechnique Fédérale de Lausanne,  \nCRAFT, CE 1 628 Station 1, 1015 Lausanne, Switzerland  \nP. Roy · F. Pachet  \nSony Computer Science Laboratory, Paris, 6 rue Amyot, 75005 Paris, France  \nbarks have context-speciWc and individual-speciWc acoustic features. In our opinion, this machine learning method may provide an eYcient tool for analyzing acoustic data in various behavioral studies.  \nKeywords Acoustic communication · Dog barks · Machine learning · Genetic programming  \nIntroduction  \nIn this paper, we report the results of the Wrst acoustic analysis and classiWcation of companion dog barks using machine learning algorithms. Earlier we found that humans have the ability to categorize various barks and associate them with appropriate emotional content by merely listening to them (Pongrácz et al. 2005) . Humans with diVerent dog experience levels showed similar trends in categorization of the possible inner state of the given barking dog. In another study we have shown that human perception of the motivational state in dogs is inXuenced by acoustic parameters in the barks (Pongrácz et al. 2006) . In contrast, humans showed only modest accuracy in discriminating between individual dogs by only hearing their barks (Molnár et al. 2006) .  \nIn behavioral research, especially when data collection (for example acoustic signal analysis) is automated, the size of the data set is often extremely large. A promising approach to handle the resulting information overload is to automate the process of knowledge extraction using data mining techniques, thereby extracting novel information and relationships between biological features (Fielding 1999; Hatzivassiloglou et al. 2001). Machine learning techniques permit the building of models for a given classiWcation task. Such models take the form of a mathematical  \nfunction that can assign a given class (or label) to an unknown example. The machine is Wrst trained on a set of labeled examples and then tested on a second set for which it must predict the labels. During the training phase, parameters","cbCaigH3rWChH7S9","https://ap.wps.com/l/cbCaigH3rWChH7S9","pdf",678318,1,12,"English","en",105,"# Abstract\n## Methods and Data\n## Classification Performance\n## Implications for Behavioral Studies\n# Introduction\n## Background in Human Perception\n## Machine Learning and Data Mining in Behavioral Research\n## Related Applications in Biology and Bioacoustics","[{\"question\":\"What data was used to train the machine-learning algorithm?\",\"answer\":\"The algorithm was trained on a pool of more than 6,000 dog barks recorded in six different communicative situations from multiple identified dogs.\"},{\"question\":\"How does the algorithm perform classification?\",\"answer\":\"It analyzes acoustic features of barks emitted in previously identified contexts by identified dogs, then uses the learned feature set to categorize unknown barks.\"},{\"question\":\"What recognition accuracy results were reported?\",\"answer\":\"Recognition rates were reported as above chance, with 43% efficiency for categorizing the recorded situation and 52% efficiency for identifying the barking individuals.\"}]","Classification of Dog Barks - 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