Sunday 19 April 2009

MIR PhD Thesis: Luís Gustavo Martins

Luís Gustavo Martins recently completed his PhD thesis titled A computational Framework for Sound Segregation in Music Signals.

From the abstract: "[...] This dissertation proposes a flexible and extensible Computational Auditory Scene Analysis framework for modeling perceptual grouping in music listening. The goal of the proposed framework is to partition a monaural acoustical mixture into a perceptually motivated topological description of the sound scene (similar to the way a naive listener would perceive it) instead of attempting to accurately separate the mixture into its original and physical sources. [...]"

Luis is probably best known in the MIR community for his contributions to Marsyas (the awesome open source software framework for audio processing with specific emphasis on MIR applications).

If you know of any other dissertations missing in the list of MIR PhDs please let me know.

Wednesday 15 April 2009

Visual Listening Charts (Part 2)

Here's a follow-up to my previous attempt to visualize my listening history differently.

Basically, I've been looking for a fun project that would give me plenty of reasons to play with matplotlib.

My preliminary conclusion is that matplotlib is awesome. They couldn't have made it much easier to use for someone already familiar with Matlab.

Ayla Nereo (65 plays)Gilberto Gil (66 plays)Barney Kessel (66 plays)Le Volume Courbe (67 plays)Nick Drake (68 plays)Glenn Gould (69 plays)Kimya Dawson (83 plays)Essie Jain (88 plays)Ani DiFranco (93 plays)Jack Johnson (118 plays)

Monday 6 April 2009

Recent MIR PhDs

I'm slowly catching up. The following dissertations were added to the list of MIR PhDs:

"Real Time Automatic Harmonisation" by Giordano Cabra. I couldn't find a link to the thesis, but I found a video of the defense. I'd love to see more defense videos (preferably in English).

"Modeling musical anticipation: From the time of music to the music of time" by Arshia Cont.

"Music Recommendation and Discovery in the Long Tail" by Oscar Celma. I highly recommend it!

"From Sparse Models to Timbre Learning: New Methods for Musical Source Separation" by Juan Jose Burred.

UPDATE: I totally missed Yves' announcement on the Music-IR list on Friday. I've added him now too:

"A Distributed Music Information System" by Yves Raimond.

UPDATE 2: Almost forgot that Kris finished recently too:

"Novel Techniques for Audio Music Classification and Search" by Kris West.

Please send me any I might have missed - thanks!

Sunday 5 April 2009

Since ISMIR I've been listening to...

I'm currently a bit fascinated with different ways of representing my listening history.

I really like LastGraph which was inspired by Lee Byron. The graphs show how my listening preferences (and in particular how often I listen to my favorite artists) change over time.

I also like these visualizations by Martin. They show how artists move up or down over time in my chart.

Recently I tried to visualize my top artist chart using artist images, and readjusting their sizes so they correspond to how often I've listened to each artist respectively. Here's what I've been listening to since my last blogpost at ISMIR 2008:

Kaki King (22 plays)Ani DiFranco (22 plays)Cake (22 plays)Yo-Yo Ma (23 plays)Nick Drake (24 plays)Brian Eno (24 plays)Robert Lockwood Jr. (24 plays)Davy Graham (24 plays)Kimya Dawson (25 plays)Antônio Carlos Jobim (25 plays)Radiohead (25 plays)Barney Kessel (28 plays)Baden Powell (30 plays)Art Tatum (30 plays)Pablo Casals (43 plays)Herb Ellis (51 plays)Julian Bream (53 plays)John Fahey (58 plays)Essie Jain (74 plays)Jack Johnson (79 plays)

I think this montage says it all: I like black and white music from solo artists playing guitars.

C++ Software Engineer, Data & Recommendations

If you are interested in data structures, algorithms, and scalability, you might also be interested in joining the data and recommendations team at

You'd be working with Norman Casagrande, Mark Levy, me and other highly motivated colleagues trying to solve lots of fun challenges in music information retrieval.