Audio & Educational Technology

…So with all this attention to technologies that ‘revolve’ around or have otherwise ‘attempted’ to incorporate some form of style-based automated composition into a technology product, what have I learned?  And, how does this relate to educational technology?  These are two questions that I started with–hoping that these questions would lead to deeper questions of how to pursue a path that could lead to a project–and ultimately help uncover research questions that could guide me through a dissertation.

Again, where does this leave me?  What have I learned about style-based auto-comp. as has been produced in industry?  The products produced are promising in that:

• There are several products that have recently come to market that incorporate generative music–that is music that changes dynamically over time.  Examples of these include:  iphone apps such as Bloom!, composition tools such as Noatikl, and generative audio in game environments such as Spore.
• Although these generative music applications are interesting, to what extent do they relate to style?  This is a more complicated answer.  I’ve read a good chunk of David Cope’s first book, ‘Computers and Musical Style’, and I’ve also gotten through the first part of another book of his, ‘Virtual Music…Computer Synthesis of Musical Style’.  According to Cope, musical style has a structure that be related to linguistics.  Cope’s SPEAC system (a hierarchical analysis consisting of a: statement, preparation,extension, antecedent, & consequent) gives the ability to analyze (and with his system) generate music in a way that can adapt to musical “styles”.  Along with Cope’s notion of augmented transition networks (ATN’s), which are related to–and to some degree dependent upon Chomsky’s notion of Transformational Grammar, Cope has been successful in designing a system (EMI : Experiments in Musical Intelligence) that can reproduce musical styles to the likes of the greats–Bach, Chopin, Rachmaninoff, and the like…

Musical Style…

“Styles” in Cope’s view are also described through attention to musical ’signatures’, ‘tension-resolution’ logic, ’semantic meshing’ (a series of nested musical contexts that range from local notes & measures to phrases, periods & large-scale sections), and final musical ‘recombination’ (which theoretically allows for recombination of discrete passages of music that can be recombined in ways that are still stylistically “viable”, according to Cope).

But these issues of style uncover other questions:  Cope’s work focuses on reproducing ’styles’ of different composers.  How can these systems support generation of music to match the style of someone interacting with a system–can someone listening to music participate in the construction of what they hear?  Well, Pandora makes an attempt at doing this.  Pandora doesn’t generate music, but it does try to give the listener what they want to hear.  Here’s my theory on this–which is based on connectionist modeling concepts described in Eric Clarke’s book, “Ways of Listening”, and described in the first chapter of the book–Perception, Ecology, and Music.

• Here, Clarke provides a background for connectionist modeling from McLelland’s: Parallel Distributed Processing (Rumelhart and McLelland 1986) and describes a theoretical experiment that describes the ability of a ‘network’ to learn (based on programming & guided input) the musical preferences of listeners (who are listening to discrete melodies) and be theoretically be able to predict and classify new melodies using the preferences on which the model is based.  (I’m oversimplifying Clarke’s example, sorry Clarke).  However, he describes this process of predictability as hinging on assignment of “connection weights between ‘input units’ (basic definition rules/conditions–in this case intervals between notes in each melody) and ‘output units’ (’like or dislike’ of the listener)
• My theory is that Pandora is based on exactly this system of analysis of ‘connection weights’ that use listener ‘voting’ to determine how well Pandora ‘understands’ listener preference–e.g. “style” and is therefore able to predict with a degree of probability whether the listener will like what comes up next.  If this is true–then can we say this way of matching listener ‘preference’ is related to the idea of musical ’style’?

Next question:

How do these ideas of generative music as it relates to style tie back to design of educational technology?