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Beyond Error Tolerance: Finding Thematic Similarities in Music Digital Libraries Tamar Berman J. Stephen Downie Bart Berman GSLIS University of Illinois at Urbana-Champaign GSLIS University of Illinois at Urbana-Champaign Independent Researcher www.notesonfranzschubert.com INTRODUCTION The objective: given a theme description, retrieve relevant phrases from a music database. These phrases will be thematically similar to each other The inspiration: Barlow and Morgenstern’s Dictionary of Musical Themes The challenge: the relevant phrases may be quite different from each other in musical details such as melody and rhythm. They may not have identical harmonies The solution: 1. Describe the theme as a sequence of melody and harmony events that must be presented in a given order and completed within a given time frame 2. Create an index for the music database which describes changes in harmony over time. Use this index to perform the retrieval EXAMPLE First theme in Allegro of Mozart’s Clarinet Concerto in A, K622 Taken from the Barlow and Morgenstern dictionary A later presentation of the theme (measures 32-33) Would have been retrieved only by harmony events METHOD OF INDEXING Possible search keys: 1. Melody sequence: {E C# D F# E D C# C# D B D B A G#} 2. Transposed melody sequence, as in B&M: GEFAGFEEFDFDCB 3. Rhythm: {Half, Dotted Quarter, Eighth, Eighth, Eighth, Eighth, Eighth, Quarter} 4. Exact harmony: {I I IV I ii ii I V7} 5. Harmony events: First event: A, C#, E with E as top voice Second event: A, C# with C# as top voice Third event: A, C# The music in the database in transformed into an equally-spaced time series of 12-dimensional vectors. This time series serves as an index to the database, and is used by the retrieval queries Each element in the time series, called a harmonic window, describes the pitch content of the time interval contained within the window For example, a harmonic window which starts 5 seconds into the piece and ends 6 seconds into the piece describes, for each pitch class, its role within that time frame: top voice, bass, middle or absent The series is constructed on the basis of two parameters: 1. Window length: size (in seconds) of the time interval described by each harmonic window 2. Onset interval: time (in seconds) between window onsets (“sampling rate”) TESTING AND PERFORMANCE The method was tested on midi sequences of music by Mozart Simple query retrieval achieved up to 88% precision Complex query retrieval achieved up to 100% precision First presentation of the theme (measures 1-4) Would have been successfully retrieved by any of the search keys Special Thanks to: The Andrew W. Mellon Foundation and the National Science Foundation