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        <datestamp>2026-09-30T14:17:48Z</datestamp>
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          <dc:title>Seven-Class Sarali Varisai Audio and Feature Dataset for Tonic-Invariant Pattern Classification</dc:title>
          <dc:creator>Sukumar Kotian (24527837)</dc:creator>
          <dc:creator>Dr Ambili P S (25144317)</dc:creator>
          <dc:subject>Music performance</dc:subject>
          <dc:subject>Music education</dc:subject>
          <dc:subject>Sarali Varisai</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This dataset supports the research study titled “A Tonic-Invariant Framework for Sarali Varisai Pattern Classification Using Legendre-Based Melodic Contour Representation.” It was created for the automatic classification of seven Sarali Varisai exercise types in Carnatic music.&lt;/p&gt;&lt;p dir="ltr"&gt;The dataset was collected by the authors and contains 276 vocal recordings distributed across seven classes, from Type 1 to Type 7. The recordings were captured using a Sony ICD-PX470 digital voice recorder in 16-bit, 44.1 kHz WAV format. Each class represents a distinct combination of ascending and descending swara sequences, note-cluster organization, repetition, and pause placement.&lt;/p&gt;&lt;p dir="ltr"&gt;The deposited files include anonymized audio recordings, class labels, anonymous participant identifiers, MFCC-based feature files, tonic-normalized Legendre polynomial features, participant-wise training and testing split information, model-performance results, and supporting documentation. The MFCC baseline uses 13 coefficients extracted from 10 equal temporal segments and summarized separately using mean, standard deviation, and median statistics. The Legendre representation models tonic-normalized melodic contours using polynomial degrees 4, 8, 12, 16, and 20.&lt;/p&gt;&lt;p dir="ltr"&gt;Recordings from an individual participant were assigned exclusively to either the training or testing subset to prevent singer-dependent information leakage. The dataset is intended to support research in Carnatic music analysis, music information retrieval, pitch-contour modeling, tonic-invariant audio classification, machine learning, and technology-assisted music education.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T14:17:48Z</dc:date>
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          <dc:type>Media</dc:type>
          <dc:identifier>10.6084/m9.figshare.33176456.v2</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
          <dc:rights>Open Access after 2027-02-06</dc:rights>
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