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

to select the best matching case or to rank the cases according to their
relevancy. This method can be integrated with the initial match method,
but it can also be treated as a separate method. The Select task usually
involves a more detailed matching of the problem description of the cases to
the input description. Ideally, the method should prefer those cases whose
solutions offer the best starting point for generating the solution for the
input problem. This can be done by assessing how relevant the differences
between input description and problem description of the matched case are,
for example by weighting the description features or the discriminatory power
of the features.
To continue the example from the previous subsection, we consider the selection,
or rather ranking task as performed in Tempo-Express. The ranking
is performed upon a number of matching cases returned by the match task.
For each case the performance available at the input tempo T i is compared to
the input performance by using a similarity measure for performances. The
cases are ranked according to these similarities. The performance at T o of
the best matching cases is taken as the input for the Reuse task.
3.3.3 The Reuse Task
The generation of the solution for the input problem based on the retrieved
case(s) is defined as the Reuse task (also called adaptation). Since Reuse is
generally considered a rather difficult task, that involves thorough knowledge
of the problem domain, it is sometimes circumvented by increasing the size
of the case base until most problems are represented in the case base. In that
case, a solution for a new problem can be transferred literally, that is without
adaptation, from the most similar (or preferably, identical) case in the case
base to the new problem. Obviously, this approach is not feasible in most
real life situations, since the number and variety of cases is too large. For
these situations Reuse is a way of making problem solving tractable in two
opposite ways. Firstly, it reduces the need for a huge case base that covers all
conceivable problems and their solutions, since parts of the problem space
can be covered by interpolating (through Reuse) from solutions of nearby
problems. Secondly, it serves to reduce the computational effort needed to
construct a solution (when compared to non-CBR approaches), by offering
ways to use existing solutions to similar problems as a starting point, instead
of starting from scratch.
In the Reuse task, the central question is whether (parts of) the retrieved
solution(s) can be reused in the new solution, and if so, whether those solutions
need to be adapted to fit the requirements of the input description.
There are several frameworks for building a new solution based on retrieved
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