Computational Invention of Cadences and Chord Progressions by Conceptual Chord-Blending
Authors: Manfred Eppe, Roberto Confalonieri, Ewen MacLean, Maximos Kaliakatsos, Emilios Cambouropoulos, Marco Schorlemmer, Mihai Codescu, Kai-Uwe Kühnberger
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we present various examples of our system at work. This is summarised in Tables 1 and 2, where we provide the input chord progressions, the chords that are blended, the prefixes,2 the colimit, the completion, and the resulting blend with its respective total generalisation cost. The two tables refer to the two different applications that we envisage for chord blending. We refer to these applications as cadence fusion and cross-fading. |
| Researcher Affiliation | Academia | 1IIIA-CSIC, Barcelona, Spain {meppe,confalonieri,marco}@iiia.csic.es 2University of Edinburgh, UK emaclea2@inf.ed.ac.uk 3University of Thessaloniki, Greece {emilios,maxk}@mus.auth.gr 4University of Magdeburg, Germany codescu@iws.cs.uni-magdeburg.de 5 University of Osnabr uck, Germany kkuenbe@uos.de 6 ICSI, Berkeley, USA eppe@icsi.berkeley.edu |
| Pseudocode | No | The paper describes the computational framework using prose, definitions, and equations, but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper describes the implementation using ASP and lists specific tools (clingo, HETS, darwin, eprover), but it does not state that the code for their blending system is open-source or publicly available. |
| Open Datasets | No | The paper refers to existing musicological concepts like 'cadences from earlier idioms' and 'The Real Book of jazz' for examples, but it does not provide concrete access information (links, DOIs, repositories, or formal citations) for a structured, publicly available dataset used for training or evaluation. |
| Dataset Splits | No | The paper does not specify any dataset split percentages, sample counts, or predefined splits for training, validation, or testing. The evaluation is presented with specific musicological examples rather than standard data partitioning. |
| Hardware Specification | No | The paper details the software components used for implementation but does not provide any specific hardware details (e.g., GPU/CPU models, processor types, or memory) used for running the experiments. |
| Software Dependencies | Yes | We use the ASP solver clingo v4 [Gebser et al., 2014] as main reasoning engine, which allows us not only to implement the search in an incremental manner, but also to use external programs via a Python interface. In our case, we need Python to call HETS [Mossakowski, 1998] as an external tool for computing the colimits for CASL specifications, and to invoke the theorem provers darwin [Baumgartner et al., 2007] and eprover [Schulz, 2013] for the consistency check. |
| Experiment Setup | Yes | To avoid this, we exploit axiom priorities, and only allow a limited number k of operators that violate the priority order of axioms within a path, i.e., that remove a high-priority axiom before a lower-priority axiom is removed. For most of our examples, a value of k = 2 turned out to be useful. |