Note Onset Deviations as Musical Piece Signatures
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{"title"=>"Note Onset Deviations as Musical Piece Signatures", "type"=>"journal", "authors"=>[{"first_name"=>"Joan", "last_name"=>"Serrà", "scopus_author_id"=>"35749172500"}, {"first_name"=>"Tan Hakan", "last_name"=>"Özaslan", "scopus_author_id"=>"51161878200"}, {"first_name"=>"Josep Lluis", "last_name"=>"Arcos", "scopus_author_id"=>"56131974300"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"23935971", "doi"=>"10.1371/journal.pone.0069268", "issn"=>"19326203", "scopus"=>"2-s2.0-84881129270", "pui"=>"369507701", "sgr"=>"84881129270"}, "id"=>"9f22b1c5-5e7a-3a21-b191-0bf3ad20f6c3", "abstract"=>"A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.", "link"=>"http://www.mendeley.com/research/note-onset-deviations-musical-piece-signatures", "reader_count"=>14, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>4, "Student > Master"=>6}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>4, "Student > Master"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>1, "Agricultural and Biological Sciences"=>1, "Arts and Humanities"=>2, "Psychology"=>2, "Computer Science"=>4, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>4}, "Linguistics"=>{"Linguistics"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>2}}, "reader_count_by_country"=>{"France"=>2, "Spain"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1138176"], "description"=>"<p>Results obtained using a sequence length (for see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069268#pone.0069268.s005\" target=\"_blank\">Fig. S5</a>). The error bars correspond to the standard deviation and the shaded area denotes the range of all possible values (including minimum and maximum). The visual aids correspond to a power law of the form , where is a constant, is the number of compositions, and is the power law exponent. The upper one is plotted with and , and is associated with classification accuracies. The lower one is plotted with and , and corresponds to the random baseline. Notice that the exponent associated with classification accuracies is much smaller than the one for the random baseline, which suggests that the absolute difference between the two increases with the number of considered compositions and, therefore, with the size of the data set.</p>", "links"=>[], "tags"=>["Computer modeling", "Information technology", "databases", "Electrical engineering", "Computer engineering", "signal processing", "Audio signal processing", "data mining", "Statistical signal processing", "Applied mathematics", "Complex systems", "Mental health", "psychology", "behavior", "Human performance", "information science", "Information storage and retrieval", "Sociology", "culture", "Cultural resources", "classification"], "article_id"=>760616, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Sociology"], "users"=>["Joan Serrà", "Tan Hakan Özaslan", "Josep Lluis Arcos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069268.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_classification_accuracy_as_a_function_of_the_number_of_compositions_/760616", "title"=>"Average classification accuracy as a function of the number of compositions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-31 01:32:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1138174"], "description"=>"<p>These are (A), (B), and (C). The labels in the horizontal axes correspond to classification algorithms: Random (0), NN-E (1), NN-D (2), Tree (3), NB (4), LR (5), SVM-L (6), and SVM-R (7). In all plots, all accuracies are statistically significantly higher than the random baseline (, see MM).</p>", "links"=>[], "tags"=>["Computer modeling", "Information technology", "databases", "Electrical engineering", "Computer engineering", "signal processing", "Audio signal processing", "data mining", "Statistical signal processing", "Applied mathematics", "Complex systems", "Mental health", "psychology", "behavior", "Human performance", "information science", "Information storage and retrieval", "Sociology", "culture", "Cultural resources", "classification", "accuracies"], "article_id"=>760614, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Sociology"], "users"=>["Joan Serrà", "Tan Hakan Özaslan", "Josep Lluis Arcos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069268.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Box_plot_of_classification_accuracies_using_different_sequence_lengths_/760614", "title"=>"Box plot of classification accuracies using different sequence lengths.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-31 01:32:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1138179"], "description"=>"<p>These are NB (A) and SVM-R (B). The color code indicates average accuracy per composition (the higher, the darker). Compositions 7, 8, and 10 seem to be generally well-classified. For NB, compositions 2 and 3 attract many of the confusions while, for SVM-R, composition 1 takes that role.</p>", "links"=>[], "tags"=>["Computer modeling", "Information technology", "databases", "Electrical engineering", "Computer engineering", "signal processing", "Audio signal processing", "data mining", "Statistical signal processing", "Applied mathematics", "Complex systems", "Mental health", "psychology", "behavior", "Human performance", "information science", "Information storage and retrieval", "Sociology", "culture", "Cultural resources", "matrices"], "article_id"=>760619, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Sociology"], "users"=>["Joan Serrà", "Tan Hakan Özaslan", "Josep Lluis Arcos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069268.g005", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Confusion_matrices_for_two_different_classifiers_/760619", "title"=>"Confusion matrices for two different classifiers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-31 01:32:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1138172"], "description"=>"<p>The error bars correspond to the standard deviation and the shaded area denotes the range of all possible values (including minimum and maximum). The visual aid corresponds to a straight line of the form , where is the intercept, is the slope of the straight, and is the sequence length. In the plot and .</p>", "links"=>[], "tags"=>["Computer modeling", "Information technology", "databases", "Electrical engineering", "Computer engineering", "signal processing", "Audio signal processing", "data mining", "Statistical signal processing", "Applied mathematics", "Complex systems", "Mental health", "psychology", "behavior", "Human performance", "information science", "Information storage and retrieval", "Sociology", "culture", "Cultural resources", "onset", "deviation"], "article_id"=>760612, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Sociology"], "users"=>["Joan Serrà", "Tan Hakan Özaslan", "Josep Lluis Arcos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069268.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_accuracy_as_a_function_of_the_length_of_the_onset_deviation_sequence_/760612", "title"=>"Classification accuracy as a function of the length of the onset deviation sequence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-31 01:32:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1138186", "https://ndownloader.figshare.com/files/1138188", "https://ndownloader.figshare.com/files/1138190", "https://ndownloader.figshare.com/files/1138195", "https://ndownloader.figshare.com/files/1138196", "https://ndownloader.figshare.com/files/1138197", "https://ndownloader.figshare.com/files/1138199", "https://ndownloader.figshare.com/files/1138201", "https://ndownloader.figshare.com/files/1138203", "https://ndownloader.figshare.com/files/1138205", "https://ndownloader.figshare.com/files/1138212"], "description"=>"<div><p>A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.</p></div>", "links"=>[], "tags"=>["Computer modeling", "Information technology", "databases", "Electrical engineering", "Computer engineering", "signal processing", "Audio signal processing", "data mining", "Statistical signal processing", "Applied mathematics", "Complex systems", "Mental health", "psychology", "behavior", "Human performance", "information science", "Information storage and retrieval", "Sociology", "culture", "Cultural resources", "onset", "deviations"], "article_id"=>760626, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Engineering", "Sociology"], "users"=>["Joan Serrà", "Tan Hakan Özaslan", "Josep Lluis Arcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0069268.s001", "https://dx.doi.org/10.1371/journal.pone.0069268.s002", "https://dx.doi.org/10.1371/journal.pone.0069268.s003", "https://dx.doi.org/10.1371/journal.pone.0069268.s004", "https://dx.doi.org/10.1371/journal.pone.0069268.s005", "https://dx.doi.org/10.1371/journal.pone.0069268.s006", "https://dx.doi.org/10.1371/journal.pone.0069268.s007", "https://dx.doi.org/10.1371/journal.pone.0069268.s008", "https://dx.doi.org/10.1371/journal.pone.0069268.s009", "https://dx.doi.org/10.1371/journal.pone.0069268.s010", "https://dx.doi.org/10.1371/journal.pone.0069268.s011"], "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Note_Onset_Deviations_as_Musical_Piece_Signatures_/760626", "title"=>"Note Onset Deviations as Musical Piece Signatures", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-07-31 01:32:18"}

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Relative Metric

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