Sloboda and Parker (1985) proposed a new experimental paradigm for research on melodic memory in which participants are asked to listen to novel melodies and to sing back the parts they can recall from memory. In contrast to the many varieties of melodic recognition paradigms frequently used in memory research this sung recall paradigm can answer questions about how mental representations of a melody build up in memory over time, about the nature of memory errors, and about the interplay between different musical dimensions in memory. Although the paradigm has clear advantages with regard to ecological validity, Sloboda and Parker also note a number of difficulties inherent to the paradigm that mostly result from necessity to analyse ‘dirty musical data’ as sung by mostly untrained participants. This contribution reviews previous research done using the sung recall paradigm and proposes a computational approach for the analysis of dirty melodic data. This approach is applied to data from a new study using Sloboda and Parker’s paradigm. This chapter discusses how this new approach not only enables researchers to handle large amounts of data but also make use of concepts from computational music analysis and music information retrieval that introduce a new level of analytic precision and conceptual clarity and thus provide a new interface which connects Sloboda’s paradigm to rigorous quantitative data analysis
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