Global Musical Tempo Transformations using Case Based ... - OFAI

the Retrieve task relies heavily on the case base organization for effectivity
and efficiency of the task (although all tasks can potentially involve interaction
with the the case base). A common way of structuring the case base
is based on Schank’s dynamic memory model mentioned above [104, 70]).
In this approach the case base is represented as an indexical tree-structure
where the retrieval of cases takes place by traversing the tree from the root
to a leaf, at each node taking the branch that has input features corresponding
to those of the current problem. This approach has been rejected by
Thagard and Holyoak [114], mainly because of its lack of psychological plausibility.
In turn, they propose a theory for analogy that allows for making
inter-domain mappings (rather than intra-domain mappings, as is usual in
CBR). In their theory mapping is a result of semantic, structural and pragmatic
constraints [61].
3.3.2 The Retrieve Task
The Retrieve task consists in finding a matching case, given a problem (or
rather, an input description, specifying the problem situation and the requirements
that a solution should fulfill). Retrieval methods can range from
matching cases with only superficial similarities to the problem description,
to matching based on a semantic analysis of the input description. The latter
case typically requires a large amount of domain knowledge and is therefore
called Knowledge-Intensive CBR (KI-CBR, see section 3.4). The Retrieve
task in general can be divided into three subtasks: identify, match and select,
which will be described in turn.
Identify
The aim of this subtask is to identify relevant features of the input description.
In a “knowledge-poor” approach, the features of the input description
might be just the input description itself (or some part of it). A
“knowledge-intensive” approach might involve deriving abstract, or higher
level features from the input description, discarding noisy parts of the description,
and predicting unknown features of the input description, by using
domain-knowledge.
As an example of the Identify subtask we can see how this task is performed
in Tempo-Express [52], a CBR system for tempo-transformations
preserving musical expression that we have developed (see chapter 5). In
this system, the input problem description is formed by a representation of
a melody, a performance of that melody at a particular tempo (T i ), and
a desired tempo at which the melody is to be performed (T o ). The prob-
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