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    Automatic Segmentation, Learning and Retrieval of Melodies Using A Self-Organizing Neural Network 

    Steven Harford (Johns Hopkins University, 2003-10-26)
    We introduce a neural network, known as SONNET-MAP, capable of automatic segmentation, learning and retrieval of melodies. SONNET-MAP is a synthesis of Nigrin’s SONNET (Self-Organizing Neural NETwork) architecture and an ...
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    Automatic Music Transcription from Multiphonic MIDI Signals 

    Haruto Takeda; Takuya Nishimoto; Shigeki Sagayama (Johns Hopkins University, 2003-10-26)
    For automatically transcribing human-performed polyphonic music recorded in the MIDI format, rhythm and tempo are decomposed through probabilistic modeling using Viterbi search in HMM for recognizing the rhythm and EM ...
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    How People Describe Their Music Information Needs: A Grounded Theory Analysis Of Music Queries 

    David Bainbridge; Sally Jo Cunningham; J. Stephen Downie (Johns Hopkins University, 2003-10-26)
    How do users of music information retrieval (MIR) systems express their needs? Using a Wizard of Oz approach to system evaluation, combined with a grounded theory analysis of 502 real-world music queries posted to Google ...
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    Determining Context-Defining Windows: Pitch Spelling using the Spiral Array 

    Elaine Chew; Yun-Ching Chen (Johns Hopkins University, 2003-10-26)
    This paper presents algorithms for pitch spelling using the Spiral Array model. Accurate pitch selling, assigning contextually consistent letter names to pitch numbers (for example, MIDI), is a critical component of music ...
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    Discovering Musical Pattern through Perceptive Heuristics. 

    Olivier Lartillot (Johns Hopkins University, 2003-10-26)
    This paper defends the view that the intricate difficulties challenging the emerging domain of Musical Pattern Discovery, which is dedicated to the automation of motivic analysis, will be overcome only through a thorough ...
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    Automatic Mood Detection from Acoustic Music Data 

    Dan Liu; Lie Lu; Hong-Jiang Zhang (Johns Hopkins University, 2003-10-26)
    Music mood describes the inherent emotional meaning of a music clip. It is helpful in music understanding and music search and some music-related applications. In this paper, a hierarchical framework is presented to automate ...
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    Was Parsons right? An experiment in usability of music representations for melody-based music retrieval 

    Alexandra Uitdenbogerd; Yaw Wah Yap (Johns Hopkins University, 2003-10-26)
    In 1975 Parsons developed his dictionary of musical themes based on a simple contour representation. The motivation was that people with little training in music would be able to identify pieces of music. We decided to ...
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    Effects of song familiarity, singing training and recent song exposure on the singing of melodies 

    Steffen Pauws (Johns Hopkins University, 2003-10-26)
    Findings of a singing experiment are presented in which trained and untrained singers sang melodies of familiar and less familiar Beatles songs from memory and after listening to the original song on CD. Results showed ...

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    AuthorAlexandra Uitdenbogerd (1)Dan Liu (1)David Bainbridge (1)Elaine Chew (1)Haruto Takeda (1)Hong-Jiang Zhang (1)J. Stephen Downie (1)Lie Lu (1)Olivier Lartillot (1)Sally Jo Cunningham (1)... View MoreSubject
    Perception and Cognition (8)
    Music Analysis (4)IR Systems and Algorithms (3)General Interest (1)... View MoreDate Issued2003 (8)Has File(s)Yes (8)

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