We are interested in what you think about a potential session proposal for the 2014 EuroMAC in Belgium. What might be a good umbrella concept for the event? What might be an attractive format? Who might be good speakers? What would you like to contribute, if it happens? What topics/methodologies might it include (think beyond rhythm)?
Please post a short pitch for an “umbrella” concept (and imagined format) as a reply to this post; each pitch should be posted as a separate reply so that replies to the pitch can be threaded. Also post individual contributions ideas for self as a separate reply. For optimal usefulness, initial postings should be done by Monday, November 11. There are no strings attached!
Here’s the link for the lab workshops. It looks like the one we’ll want is next Friday morning, the 15th. The link to register is at the top of the page.
Hi everyone. I’m sorry about the delayed post, I’m still furiously catching up from Charlotte, as I’m sure are many of you.
Pleases read TOUISSANT, G et. al. 2011 “Computational models of symbolic rhythm similarity: Correlation with human Judgments” pages 380-402 & 418-424 only (pdf 1-23 & 39-45). (Feel free to skip experiments 2 and 3; I will summarize them in class).
In preparation for class, I would like everyone to ruminate on the relationships between different mathematical measures of rhythm similarity (symbolic) and human judgment of rhythms (heard) as similar. Specifically, what is the perceptual difference between swap and edit distances? Why do you believe edit distances performed better? Are there any rhythmic circumstances in which you might expect swap distances to better correlate to perception than edit distances? Do you have any thoughts on how to “change the edit distance so that it is impervious to counter examples” (421)? Might it be beneficial to reconstruct “distance” in a different way than the minimum number of required mutations? If so, how would we proceed?
My aim in these questions is to foster a discussion on: symbolic metrics, what different distances “mean,” experimental design, representation of results, and potential follow-up studies.