I'm going to be presenting work at
IEEE-THEMES, a workshop collocated with
ICASSP, on March 15th in Dallas, TX. The talk is associated with an article to be published in the august issue of Select Topics in Signal Processing, which is a special issue on signal processing and social networks. Here's the title/abstract (note: link is to a preprint, camera-ready isn't due till after the talk so paper may well change a touch...) :
Abstract—This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relation- ship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are presented and considered in the light of recommendation and discovery applications with these hybrid measures as their basis.
The paper mostly covers content that has been discussed elsewhere (much of it with
Kurt Jacobson) refactored for a broader audience and with wider narratives in mind. That said there are some notable new findings in the paper as well. We have run another acoustic dissimilarity measure across the entire set (the
2009 MIREX entry in
audio music similarity using
marsyas) which for the most part confirms our earlier findings (that acoustic similarity and social similarity [mostly] aren't linearly correlated and that community genre labeling becomes more homogeneous [again, mostly] when using the audio sim as a weight). Additionally, we have broadened our comparison metrics to include an examination of the mutual information between the different dissimilarity sets. This also basically confirms our earlier findings, though mutual information provides a very satisfying level of nuance that is not possible from simply testing (using Pearsons) for linear correlation, especially given that our data is quite far from a normal distribution. So, if you're planning to be at ICASSP, I'd highly recommend IEEE-THEMES (the rest of the program looks to be very interesting as well...) and if you aren't going to be in Dallas, there are a few options for you.
- If you're in London right now, you can come to Goldsmiths today at 4pm to rm 144 in the main building, where I'll be giving a trail run of the talk.
- Slides (and perhaps some video) will be made available at some point (probably just after the talk is given).
- IEEE is running a pay-to-watch live stream of THEMES, so there's that as well.
Generally, if you're going to be in Dallas fr0m March 15-19, much discussion can happen in person. Also, between now and then I'll be doing some traveling (tomorrow till 6 March I'll be at UIUC, then from there till the 14th of March I'll be in San Diego) so if any readers are interested in some in person discussion and our locations overlap, let me know and perhaps something can be arranged.
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