![]() ![]() ![]() New York Times, Washington Post, CNN, Slate and Salon. After that, I attack feeds according to whatever mood I’m in or what kind of project I’m working on. I typically check a handful of the 200 some-odd feeds I follow. Some of the more regular ones I like to read are 10,000 Words, Chris Brogan, Nieman Lab, Romenesko, Dave Winer, Seth Godin, Poynter, Mental Floss, Neatorama, A List Apart, Mashable, The Next Web, Tech Crunch, Smashing Magazine, ReadWriteWeb, ProBlogger and Search Engine Land. On most days, I catch NPR on my local station, WFDD – out of Wake Forest University. I also check several news feeds via my feed reader. ![]() These include the New York Times, Washington Post, CNN, Sports Illustrated, Slate and Salon. I also follow several news outlets on Twitter, and often hear of breaking news there. I subscribe to news alerts via email from the New York Times as well. Currently, I do not subscribe to any magazines or newspapers. I also don’t watch much, if any, television news. I canceled cable a few months before I started graduate school, and haven’t looked back. I’m enjoying all the free time I have since I avoid programs I’m not really interested in much easier now. I do subscribe to NetFlix, and love all the documentaries there. Some recent ones I have watched include Tyson, Truman and Food, Inc. I’m also a frequent visitor to Interactive Narratives and MediaStorm, always looking for cool, multimedia stories. Lastly, I like to watch my share of Jon Stewart clips. I love to write, so therefore I read a lot. I recently read Googled, and The Future of the Internet (both for graduate school, among other titles). I also just finished What I Talk About When I Talk About Running. I’m currently reading Once a Runner and Bad Luck and Trouble. My favorite authors are Po Bronson and Paul Auster. I watch some television series, thanks to Netflix. I just finished The X-Files in its entirety. And you know what, I usually can’t make it a month without watching Strongbad answer some emails. My friends fill in other odds and ends via links on Twitter and Facebook. That’s my media diet.In this paper, the problem of the design of a simple and efficient music-speech discriminator for large audio data sets in which advanced music playing techniques are taught and voice and music are intrinsically interleaved is addressed. In the process, a number of features used in speech-music discrimination are defined and evaluated over the available data set. ![]()
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