Categories of results generated during M4S

The results of the M4S project fall into five major categories:

  1. Development of features for the automatic analysis of melodies and music in symbolic formats.
  2. Psychological tests concerning the cognitive validity and adequacy of analytic features and similarity algorithms.
  3. Development of the technical and conceptual tools to derive melodic information for a large collection (i.e. a corpus) of melodies.
  4. The usage of melody features as predictors in models of human memory for melodies and other application models.
  5. Development of general methodologies for music (cognition) research

1. Feature development

The features developed and used in M4S are implemented in the open source program FANTASTIC and are documented in the corresponding software documentation (see section Software and Documentation). The features implemented in FANTASTIC comprise features of different types:

2. Feature testing

Over the course of M4S we have conducted several studies that tested the cognitive adequacy of algorithmically derived melodic features in various psychological experiments. The results of these studies have been reported at several conferences and journal papers are currently in the publishing process. Among the features that have been tested intensively are:

3. Developing tools for using music corpora

One of the original ideas of M4S is the approach to incorporate human background knowledge about music into models of music cognition. As an approximation to human knowledge about western popular music we use a large corpus of commercial popular songs and describe it in terms of the statistical distributions and regularities of melodic and generally musical features in it. We then use this statistical information about western pop music in general to assist modelling cognition in specific tasks and individual melodies. Beyond cognitive modelling the statistical description of a corpus of music can also be interesting in persepctive of stylistic categorisation, music information retrieval, analytic musicology. The three steps for dealing information with a music corpus that we took in M4S are:

  1. We acquired a large collection of very accurate MIDI-transcriptions of 14,063 pop songs ranging from the 1950s to 2006 and encompassing most pop music styles from Geerdes MIDI Music and curated it to become a coherent corpus of pop music
  2. We devised AMuSE (in collaboration with the Andrew W. Mellon funded MeTAMuSE project), a database system for musical knowledge representation and reasoning.
  3. We developed concepts and methods to apply corpus-based music knowledge to cognitive modelling and musicology, drawing parallels and differences to approaches in corpus-based linguistics (Müllensiefen et al., 2008).

4. Using melodic features and corpus knowledge in models of music cognition and other applications

There are several areas where we successfully applied and tested the tools and knowledge gained during M4S:

5. General methodologies for music cognition research

Apart from results that are in direct connection with the targets of the project we also developed concepts and methodologies that are relevant for a wider circle of researchers in music cognition and musicology.