10 Reasons to Work for Last.fm

I've moved to Vienna, and I'm about to leave Last.fm. Looking back, here are 10 reasons why I love to work for Last.fm:

1. Staff
My colleagues turn work into play and have created an environment that feels like a family. I've learned a lot from many of them. Some have become very close friends. All of them have inspired me with their passion, enthusiasm, motivation and curiosity for everything related to technology and music. Also they've always been very patient with me and always very helpful. I admire their intelligence, creativity, and how determined they are to make music more enjoyable.

2. Users
There is a constant flow of very valuable feedback from our users. Many are as passionate about Last.fm as we are. They have enabled us to run countless experiments (A/B tests and similar) - from which we learned more than we could have ever learned in a standard academic environment.

3. Data
If you love data mining and working with large data sets, you'll love working at Last.fm. There are a lot of fascinating things that can be learned about music from Last.fm's data. And there is a lot of fascinating things to learn about dealing with data at such a scale.

4. Technology
Last.fm has been on the front-line of some very interesting developments. Always using the tools that work best and developing new ones when needed. Always trying to push scalability limits and trying to make things work faster and better. Before joining Last.fm I didn't realize how fascinating technology can be that solves large scale problems. I've learned new programming languages, things like Hadoop, and many algorithms I had never heard of before. I caught a glimpse of the amazing technology (both software and hardware) behind serving tens of millions every month. If you love technology, you'll love working at Last.fm.

5. Openness
The data is easily available through APIs. Last.fm staffers constantly contribute to open source projects and parts of Last.fm are open source. Every staff member (even I) can post anything they want on the company blog. We are constantly attending events where we talk about the things we do and share our experiences. Also the communication within Last.fm is very open.

5. Fun
We have a table tennis table standing around in the office, a foosball table, there was always a skateboard around, and for a while we even turned one of the meeting rooms into a ball pit (and had meetings in there). There were remote controlled helicopters flying around in the office, XBoxes, we have colored bears telling us about the state of our code, highlight words on IRC include "pub", on IRC we also constantly share the newest coolest stuff we find on the Internet, servers have names like "badger", ...

6. Lunch Discussions & Techmosis
One of the things I've already started missing since I moved to Vienna are the daily (informal) lunch discussions. In particular the discussions in which Norman Casagrande was involved - he's like a living encyclopedia. We talked about why C++ templates are so useful, how to solve the problems in the middle east, inconsistencies in religious beliefs, objectivity as a concept, world history, politics, the daily show, ...

We also have something we call Techmosis, where we teach each other things. Usually Friday evenings we reserve a one hour slot to learn about something cool. Sometimes we'd also have non-staffers teach us. Today it will be a presentation on manga by Japanese manga artists. Generally I found the atmosphere of teaching each other extremely inspiring.

7. London
London is an amazing city. There is so much happening, there is more stuff to check out every day than I could absorb in a year. I'd love to move Last.fm to Vienna, but I can see why East London is the perfect place for Last.fm. As one of my colleagues says: it rubs off on you. If you haven't lived in London yet, you should definitely give it a try.

8. Free Stuff
My favorites are my Last.fm t-shirts (one of which I'm wearing right now) and the fruit. (I herewith admit that every week I probably eat about half of our weekly supplies all on my own.) Pizza lunches and the occasional pub visits with a company credit card were nice to have, too.

9. Rooftop Barbecues
We made sure our neighbors knew they lived (or worked) next to us. But I think the best part always was how the parties just seemed to spontaneously happen.

10. Music
Last on this list, but not least: If you love music, you'll have a hard time finding a better place to work than Last.fm where you are surrounded by music, and people who love music. You might also like the band practice room.

Wittgenstein Award: Gerhard Widmer (!!)

The Wittgenstein award is kind of like the Austrian version of the Nobel prize. It's worth €1.5M and the most prestigious research award I'm aware of. Yesterday Gerhard Widmer received the prize for his outstanding research work.

Gerhard is one of the hardest working I know, he has an amazing talent of communicating research to non-researchers, creating very productive work environments, and bringing the right people together. Also he has conducted, supervised, and inspired lots of great research. He also gave me my first (paid) job in MIR and supervised my PhD. I've learned a lot from him and I'm very grateful. So I'm particularly happy to see Gerhard received this award he well deserves.

My favorite daily newspaper wrote about it. And here's a quick, shortened, and far from accurate translation of what they wrote:

Frustrations and Fortunes with Ludwig van Beethoven

Among Gerhard Widmer's favorite music you find Beethoven's piano sonatas. He doesn't have a preferred interpret. For some phrases he prefers Friedrich Gulda, for others Alfred Brendel, or other pianists.

It is also Beethoven who is to blame that Gerhard did not pursue a career as musician and instead became a internationally renowned researcher on algorithms to study music. Work for which he was now awarded with the Wittgenstein prize.

Gerhard was an early talent but gave up his career as pianist after frustrations with Beethoven's sonatas as a teenager. Instead he took a quick dip in Jazz and more or less randomly ended up studying computer science.

He received an MSc in Vienna and at the University of Wisconsin. It was also in Wisconsin that Gerhard briefly returned to the Jazz piano. Back in Vienna he completed his PhD in computer science which finalized his career as researcher.

Gerhard's research started with analyzing the performances of a single artist and as a side effect he tried to teach computers to interpret music. Later his group started focusing on developing algorithms that enable organizing and retrieving content from very large music collections. The work of his group can also be found in the newest devices by Bang & Olufsen.

Music has always been more than just a research subject to Gerhard. And he says that the scientific analysis of music does not take away any of its magic - instead it makes the music even more beautiful when you start to understand its structure.

Updated MIR PhD Theses List

I've updated the list of MIR PhD theses to include the recent work by Antti Eronen on Signal Processing Methods for Audio Classification and Music Content Analysis. Antti covers a broad range of fascinating audio content processing topics such as: instrument classification, classification of ambient environments/backgrounds (libraries, cars, ...), chorus detection, and meter analysis.

Seems like I'm the list is still far from complete. As the following two theses indicate. If you know of any more that I'm missing please let me know. (Thank you Arijit Biswas for your help!)

One that I missed is the work of Michael J. Bruderer on Perception and Modeling of Segment Boundaries in Popular Music. Which describes an interesting series of experiments in which Michael explores which cues listeners use when segmenting music and how they can be modeled.

Another older one I've added is the work of Olivier Gillet Transcription des signaux percussifs. Application à l'analyse de scènes musicales audiovisuelles. Olivier joined Google after his PhD where he worked on the optimization of Google's ad product and on technologies now being used by Google China Music. Olivier is now joining us at Last.fm's to work on fun next generation MIR technologies.

I would also like to mention the Master thesis Implementing a scalable music recommender by Erik Bernhardsson. I really like how Spotify has given him access to their data and let him publish the results. Seems like Spotify was also very happy with his work as they have hired him.

Bang & Olufsen and OFAI

This is probably very old news to some, but I'm a bit behind in blogging.

Yet another really cool product my former colleagues at OFAI have been working on over the last years: integration of content-based playlist generation technologies into the most awesome music hardware out there.

Read more about it here (English) and here (German).

If you can't read German, the perhaps most interest bits: retail price per unit €4.765, 500 pre-orders before the device was available, OFAI and B&O will continue the collaboration, size of the features representing each song: 3.2KB.

FM4 Soundpark and OFAI

My former colleagues at OFAI have been experimenting with audio content based music recommendation for FM4 Soundpark for a while now. (I blogged about it here.) Now they (and in particular Martin Gasser) have taken it a few steps further and implemented a 3D exploration interface using the islands of music metaphor.

It's all in Java and requires special privileges because it needs access to low level graphics card functions. But if you get passed those hurdles here's what you'd see:



(Navigation: space bar, A,W,S,D and mouse)

There is more information about this and other recommendation tools they have developed for Soundpark here (in German).

Last.fm Artist Connections

Playing with Last.fm similar artist data, trying to connect one artist to another through similar artists:



The Beatles -> John Lennon -> Sean Lennon -> Joseph Arthur -> Howie Day -> Teddy Geiger -> Jonas Brothers



ABBA -> Cher -> Madonna -> Róisín Murphy -> Björk -> Radiohead



Metallica -> Iron Maiden -> Paul Di'Anno -> Numbers From The Beast -> Scott Lavender -> Ark Sano -> Frédéric Chopin -> Wolfgang Amadeus Mozart



Brian Eno -> Roxy Music -> Bryan Ferry -> Eurythmics -> Annie Lennox -> Madonna -> Britney Spears

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.

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.

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!

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:



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 Last.fm.

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.