Mind, Body, World- Foundations of Cognitive Science, 2013a

connectionism is positioned to capture regularities that are difficult to express in
language or by using formal rules (Loy, 1991).
For example, human subjects can accurately classify short musical selections
into different genres or styles in a remarkably short period of time, within a quarter
of a second (Gjerdingen & Perrott, 2008). But it is difficult to see how one could provide
a classical account of this ability because of the difficulty in formally defining a
genre or style for a classical model. “It is not likely that musical styles can be isolated
successfully by simple heuristics and introspection, nor can they be readily modeled
as a rule-solving problem” (Loy, 1991, p. 31).
However, many different artificial neural networks have been developed to
classify music using categories that seem to defy precise, formal definitions. These
include networks that can classify musical patterns as belonging to the early works
of Mozart (Gjerdingen, 1990); classify selections as belonging to different genres
of Western music (Mostafa & Billor, 2009); detect patterns of movement between
notes in segments of music (Gjerdingen, 1994) in a fashion similar to a model of
apparent motion perception (Grossberg & Rudd, 1989, 1992); evaluate the affective
aesthetics of a melody (Coutinho & Cangelosi, 2009; Katz, 1995); and even predict
the possibility that a particular song has “hit potential” (Monterola et al., 2009).
Categories such as genre or hit potential are obviously vague. However, even
identifying a stimulus as being a particular song or melody may also be difficult
to define formally. This is because a melody can be transposed into different keys,
performed by different instruments or voices, or even embellished by adding improvisational
flourishes.
Again, melody recognition can be accomplished by artificial neural networks
that map, for instance, transposed versions of the same musical segment onto a
single output representation (Benuskova, 1995; Bharucha & Todd, 1989; Page, 1994;
Stevens & Latimer, 1992). Neural network melody recognition has implications for
other aspects of musical cognition, such as the representational format for musical
memories. For instance, self-organizing networks can represent the hierarchical
structure of a musical piece in an abstract enough fashion so that only the “gist” is
encoded, permitting the same memory to be linked to multiple auditory variations
(Large, Palmer, & Pollack, 1995). Auditory processing organizes information into
separate streams (Bregman, 1990); neural networks can accomplish this for musical
inputs by processing relationships amongst pitches (Grossberg, 1999).
The insights into musical representation that are being provided by artificial
neural networks have important implications beyond musical cognition. There is
now wide availability of music and multimedia materials in digital format. How
can such material be classified and searched? Artificial neural networks are proving
to be useful in addressing this problem, as well as for providing adaptive systems
for selecting music, or generating musical playlists, based on a user’s mood or past
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