December 2014 Archives

Blog done moved

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At first I was hesitant because of its handling of latex, but Rolf of Mathcination pointed out this excellent tool: latex-to-wordpress. So, from now on I will be updating my new blog, High Noon GMT.

Now that I am cleaning out my office after spending half a decade here, I thought it would be interesting to compile the statistics of my peer reviewing during that time.

I have reviewed 33 papers submitted to conferences, such as ICASSP, EUSIPCO, ISMIR, and DAFx. The rate at which I recommended acceptance is over 51%!

I have reviewed at least 18 articles submitted to journals, such as IEEE Trans. Audio, Speech Lang. Proc., IEEE Sig. Process., IEEE Sig. Process. Letts., IEEE Trans. Multimedia, J. New Music, Research, and EURASIP J. Audio, Speech and Music Process. My acceptance rate is a surprising 11%. I wonder if I am being too harsh; but a brief review of the articles I have rejected tell me "no." Furthermore, looking at the recommendations of the other reviewers, I see that I am not the only one of the other two or three who recommends rejection. (Some are also "reject but encourage resubmission." :)

I have reviewed four external Master's students (all pass!).

I have examined two PhDs (all pass!).

I have reviewed one book proposal.

I have reviewed two grant proposals (one advising against and one in support).

leonid-sabaneyev-250x375.jpgAcousticBrainz aims to automatically analyze the world's music, in partnership with MusicBrainz and "provide music technology researchers and open source hackers with a massive database of information about music." This effort is crowd sourced neatness, which means people from all over the world are contributing data by having their computer crunch through their MusicBrainz-IDed music libraries and automatically uploading all the low-level features it extracts. AcousticBrainz has now analyzed over 1.2 million tracks, which makes it larger than the Million Song Dataset.

I construct today's review from low- and high-level data recently extracted from a particular music track AcousticBrainz. Can you guess what it is? What characteristics it has? ("Probabilities" are in parentheses.) The answer will be revealed below tomorrow.

This female (0.75) instrumental (0.97) tonal (0.94) track is not a danceable (0.98) Viennese Waltz (0.93). Its mood is not electronic (0.85) but definitely acoustic (1.0), definitely not party (1.0), and most certainly not happy (0.99) but not necessarily sad (0.52). Its genre is definitely electronic (1.0) jazz (0.94) ambient (0.94) classical (0.71). This track has a last.fm tag! "Genre: Jazz".

It is the Anouar Brahem Trio playing "Halfaouine"

leonid-sabaneyev-250x375.jpgAcousticBrainz aims to automatically analyze the world's music, in partnership with MusicBrainz and "provide music technology researchers and open source hackers with a massive database of information about music." This effort is crowd sourced neatness, which means people from all over the world are contributing data by having their computer crunch through their MusicBrainz-IDed music libraries and automatically uploading all the low-level features it extracts.

I construct today's review from low- and high-level data recently extracted from a particular music track AcousticBrainz. Can you guess what it is? What characteristics it has? ("Probabilities" are in parentheses.) The answer will be revealed below tomorrow.

This tonal (0.98) G minor track is instrumental (0.77), and not danceable (0.72) with a Tango rhythm (0.77). Its mood is acoustic (0.94), not aggressive (0.94), but not relaxed (0.67) and not party (0.72). It could be sad (0.62). Its genre is ambient (0.72) electronic (0.55) jazz (0.52).

Bert's Bossa Nova by Bert Kaempfert

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This page is an archive of entries from December 2014 listed from newest to oldest.

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