Characterizing Vocal Repertoires—Hard vs. Soft Classification Approaches
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{"title"=>"Characterizing vocal repertoires - Hard vs. Soft classification approaches", "type"=>"journal", "authors"=>[{"first_name"=>"Philip", "last_name"=>"Wadewitz", "scopus_author_id"=>"56607343500"}, {"first_name"=>"Kurt", "last_name"=>"Hammerschmidt", "scopus_author_id"=>"55859627900"}, {"first_name"=>"Demian", "last_name"=>"Battaglia", "scopus_author_id"=>"7006321888"}, {"first_name"=>"Annette", "last_name"=>"Witt", "scopus_author_id"=>"7103029949"}, {"first_name"=>"Fred", "last_name"=>"Wolf", "scopus_author_id"=>"7203026045"}, {"first_name"=>"Julia", "last_name"=>"Fischer", "scopus_author_id"=>"7404369347"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25915039", "doi"=>"10.1371/journal.pone.0125785", "sgr"=>"84928575886", "scopus"=>"2-s2.0-84928575886", "issn"=>"19326203", "pui"=>"604006362"}, "id"=>"b01153d8-a47a-3e58-bb5a-10b2dc359280", "abstract"=>"To understand the proximate and ultimate causes that shape acoustic communication in animals, objective characterizations of the vocal repertoire of a given species are critical, as they provide the foundation for comparative analyses among individuals, populations and taxa. Progress in this field has been hampered by a lack of standard in methodology, however. One problem is that researchers may settle on different variables to characterize the calls, which may impact on the classification of calls. More important, there is no agreement how to best characterize the overall structure of the repertoire in terms of the amount of gradation within and between call types. Here, we address these challenges by examining 912 calls recorded from wild chacma baboons (Papio ursinus). We extracted 118 acoustic variables from spectrograms, from which we constructed different sets of acoustic features, containing 9, 38, and 118 variables; as well 19 factors derived from principal component analysis. We compared and validated the resulting classifications of k-means and hierarchical clustering. Datasets with a higher number of acoustic features lead to better clustering results than datasets with only a few features. The use of factors in the cluster analysis resulted in an extremely poor resolution of emerging call types. Another important finding is that none of the applied clustering methods gave strong support to a specific cluster solution. Instead, the cluster analysis revealed that within distinct call types, subtypes may exist. Because hard clustering methods are not well suited to capture such gradation within call types, we applied a fuzzy clustering algorithm. We found that this algorithm provides a detailed and quantitative description of the gradation within and between chacma baboon call types. In conclusion, we suggest that fuzzy clustering should be used in future studies to analyze the graded structure of vocal repertoires. Moreover, the use of factor analyses to reduce the number of acoustic variables should be discouraged.", "link"=>"http://www.mendeley.com/research/characterizing-vocal-repertoires-hard-vs-soft-classification-approaches", "reader_count"=>62, "reader_count_by_academic_status"=>{"Researcher"=>12, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>12, "Other"=>3, "Student > Bachelor"=>11, "Professor"=>5}, "reader_count_by_user_role"=>{"Researcher"=>12, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>12, "Other"=>3, "Student > Bachelor"=>11, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Environmental Science"=>6, "Agricultural and Biological Sciences"=>41, "Neuroscience"=>4, "Physics and Astronomy"=>1, "Psychology"=>6, "Social Sciences"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Neuroscience"=>{"Neuroscience"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>41}, "Linguistics"=>{"Linguistics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>6}}, "reader_count_by_country"=>{"Netherlands"=>1, "Hungary"=>1, "United States"=>2, "Brazil"=>1, "United Kingdom"=>2, "Germany"=>2}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2040859"], "description"=>"<p>Sections with different colors indicate calls with different main type. Grunts and barks are more distinctly separated from other call types than screams and weaning calls.</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395232, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g007", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histogram_of_typicality_coefficients_/1395232", "title"=>"Histogram of typicality coefficients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040857"], "description"=>"<p>(A) Number of clusters in dependence on the fuzziness parameter μ. Partitions with more than five clusters exist only over very narrow ranges of μ values (red). (C-D) membership matrices for the identified clusters: Rows correspond to different fuzzy clusters and columns to individual calls. Membership values of single calls to each class are color coded (B). The scream-cluster is the first to emerge (cluster 1, C), followed by grunts (cluster 2, D). The scream-cluster splits into two clusters and the weaning-cluster emerges (cluster 1–2; cluster 4, E).</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395230, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fuzzy_partitions_with_decreasing_fuzziness_956_values_are_visualized_as_membership_matrices_/1395230", "title"=>"Fuzzy partitions with decreasing fuzziness (μ values) are visualized as membership matrices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040854"], "description"=>"<p>The x-axis represents groups of calls, and the y-axis represents average Euclidian within-cluster linkage distance. (A) Set consisting of 118 features. High-frequency (cluster 1) and low-frequency (cluster 2) were segregated into two first-order clusters. High frequency calls further subdivide into more tonal (cluster 1.1) and relatively noisier (cluster 1.2) calls. Low frequency calls subdivide into short and very low-frequency grunt-calls (cluster 2.2), moderate-frequency and harmonic weaning-calls (cluster 2.1.1), and more noisy, short bark-calls (cluster 2.1.2). (B) Set consisting of 38 features. (C) Set consisting of 9 features. (D) Set consisting of 19 factors determined by factor analysis.</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395227, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unsupervised_Ward_8217_s_clustering_of_912_chacma_baboon_calls_based_on_different_frequency_dependent_and_temporal_feature_setups_/1395227", "title"=>"Unsupervised Ward’s clustering of 912 chacma baboon calls based on different frequency dependent and temporal feature setups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040853"], "description"=>"<p>Shown are call types that have been described in the literature. (A) Male bark [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125785#pone.0125785.ref026\" target=\"_blank\">26</a>]. (B) Grunt [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125785#pone.0125785.ref027\" target=\"_blank\">27</a>]. (C) Female bark [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125785#pone.0125785.ref022\" target=\"_blank\">22</a>]. (D) Noisy scream [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125785#pone.0125785.ref025\" target=\"_blank\">25</a>]. (E) Weaning call [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125785#pone.0125785.ref025\" target=\"_blank\">25</a>]. (F) Tonal scream [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125785#pone.0125785.ref025\" target=\"_blank\">25</a>].</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395226, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g001", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spectrograms_of_calls_in_the_used_dataset_/1395226", "title"=>"Spectrograms of calls in the used dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040861", "https://ndownloader.figshare.com/files/2040862", "https://ndownloader.figshare.com/files/2040863", "https://ndownloader.figshare.com/files/2040864", "https://ndownloader.figshare.com/files/2040865", "https://ndownloader.figshare.com/files/2040866", "https://ndownloader.figshare.com/files/2040867", "https://ndownloader.figshare.com/files/2040868", "https://ndownloader.figshare.com/files/2040869", "https://ndownloader.figshare.com/files/2040870"], "description"=>"<div><p>To understand the proximate and ultimate causes that shape acoustic communication in animals, objective characterizations of the vocal repertoire of a given species are critical, as they provide the foundation for comparative analyses among individuals, populations and taxa. Progress in this field has been hampered by a lack of standard in methodology, however. One problem is that researchers may settle on different variables to characterize the calls, which may impact on the classification of calls. More important, there is no agreement how to best characterize the overall structure of the repertoire in terms of the amount of gradation within and between call types. Here, we address these challenges by examining 912 calls recorded from wild chacma baboons (<i>Papio ursinus</i>). We extracted 118 acoustic variables from spectrograms, from which we constructed different sets of acoustic features, containing 9, 38, and 118 variables; as well 19 factors derived from principal component analysis. We compared and validated the resulting classifications of k-means and hierarchical clustering. Datasets with a higher number of acoustic features lead to better clustering results than datasets with only a few features. The use of factors in the cluster analysis resulted in an extremely poor resolution of emerging call types. Another important finding is that none of the applied clustering methods gave strong support to a specific cluster solution. Instead, the cluster analysis revealed that within distinct call types, subtypes may exist. Because hard clustering methods are not well suited to capture such gradation within call types, we applied a fuzzy clustering algorithm. We found that this algorithm provides a detailed and quantitative description of the gradation within and between chacma baboon call types. In conclusion, we suggest that fuzzy clustering should be used in future studies to analyze the graded structure of vocal repertoires. Moreover, the use of factor analyses to reduce the number of acoustic variables should be discouraged.</p></div>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395234, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0125785.s001", "https://dx.doi.org/10.1371/journal.pone.0125785.s002", "https://dx.doi.org/10.1371/journal.pone.0125785.s003", "https://dx.doi.org/10.1371/journal.pone.0125785.s004", "https://dx.doi.org/10.1371/journal.pone.0125785.s005", "https://dx.doi.org/10.1371/journal.pone.0125785.s006", "https://dx.doi.org/10.1371/journal.pone.0125785.s007", "https://dx.doi.org/10.1371/journal.pone.0125785.s008", "https://dx.doi.org/10.1371/journal.pone.0125785.s009", "https://dx.doi.org/10.1371/journal.pone.0125785.s010"], "stats"=>{"downloads"=>21, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterizing_Vocal_Repertoires_8212_Hard_vs_Soft_Classification_Approaches_/1395234", "title"=>"Characterizing Vocal Repertoires—Hard vs. Soft Classification Approaches", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040856"], "description"=>"<p>NMI values have been calculated for k = 5 clusters.</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395229, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g004", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_of_the_algorithm_performance_normalized_mutual_information_between_the_human_made_reference_classification_and_K_means_purple_and_Ward_8217_s_orange_clustering_for_the_three_feature_sets_/1395229", "title"=>"Sensitivity of the algorithm performance (normalized mutual information) between the human-made reference classification and K-means (purple), and Ward’s (orange) clustering for the three feature sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040855"], "description"=>"<p>The 9 feature set (green) shows generally higher silhouette width. For the 2-cluster-solution, all but the set based on factors (yellow) show globally the highest value. Excluding the 2-cluster-solution (not to be retained because of its lack of detail), no solution is markedly superior over all others, although plateau values of average silhouette width are already obtained for cluster numbers as small as k = 5 (apart from factor-based clustering).</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395228, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g003", "stats"=>{"downloads"=>1, "page_views"=>59, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_the_average_silhouette_width_for_K_means_clustering_for_k_2_to_20_clusters_for_all_4_feature_sets_/1395228", "title"=>"Comparison between the average silhouette width for K-means clustering for k = 2 to 20 clusters for all 4 feature sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/2040858"], "description"=>"<p>Two-dimensional projections of memberships of calls belonging to the grunt (red), scream 1 (green), scream 2 (pink), weaning (yellow), and bark (blue) cluster. Every call is represented once (by closest and second closest cluster). Diagonal lines in the panels represent identical memberships. Spectrograms represent transitions from most typical call of cluster A to most typical call of cluster B with hybrids close to the joint cluster borders. Sound examples can be found in the supporting information.</p>", "links"=>[], "tags"=>["chacma", "classification", "gradation", "factor", "algorithm", "method", "Soft Classification Approaches", "repertoire", "variable", "analyses", "type", "Cluster analysis"], "article_id"=>1395231, "categories"=>["Biological Sciences"], "users"=>["Philip Wadewitz", "Kurt Hammerschmidt", "Demian Battaglia", "Annette Witt", "Fred Wolf", "Julia Fischer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125785.g006", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pairwise_comparisons_of_cluster_segregations_/1395231", "title"=>"Pairwise comparisons of cluster segregations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-27 02:58:23"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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