woooo!

I finally submitted my phd thesis. I'll post some bits of it over the next few weeks, and the whole thing after my viva. To start, here's the abstract:

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It is not hyperbole to note that a revolution has occurred in the way that we as a society distribute data and information. This revolution has come about through the confluence of Web-related technologies and the approaching- universal adoption of internet connectivity. Add to this mix the normalised use of lossy compression in digital music and the uptick in digital music download and streaming services; the result is an environment where nearly anyone can listen to nearly any piece of music nearly anywhere. This is in many respects the pinnacle in music access and availability. Yet, a listener is now faced with a dilemma of choice. Without being familiar with the ever-expanding millions of songs available, how does a listener know what to listen to? If a near-complete collection of recorded music is available what does one listen to next? While the world of music distribution underwent a revolution, the ubiquitous access and availability it created brought new problems in recommendation and discovery.

In this thesis, a solution to these problems of recommendation and discovery is presented. We begin with an introduction to the core concepts around the playlist (i.e. sequential ordering of musical works). Next, we examine the history of the playlist as a recommendation technique, starting from before the invention of audio recording and moving through to modern automatic methods. This leads to an awareness that the creation of suitable playlists requires a high degree of knowledge of the relation between songs in a collection (e.g. song similarity). To better inform our base of knowledge of the relationships between songs we explore the use of social network analysis in combination with content-based music information retrieval. In an effort to show the promise of this more complex relational space, a fully automatic interactive radio system is proposed, using audio-content and social network data as a backbone. The implementation of the system is detailed. The creation of this system presents another problem in the area of evaluation. To that end, a novel distance metric between playlists is specified and tested. This distance method is then applied as a mean of evaluation to our interactive radio system. We then conclude with a discussion of what has been shown and what future work remains.

IEEE-THEMES --shameless self promotion--

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...) :

Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

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.

1. 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.
2. Slides (and perhaps some video) will be made available at some point (probably just after the talk is given).
3. 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.

A bit about playlists and similarity

Sorry about the general radio silence of late. Many things going on, most of them interesting.

Lately I've been spending quite a bit of time considering various aspects of playlist generation and how they all fit together. Here are some of my lines of thought:

1. Evaluation of a playlist. How? Along which dimension? (Good v. Bad, Appropriate v. Offensive, Interesting v. Boring)
2. How do people in various functions create playlists? How does this process and its output compare to common (or state of the art) methods employed in automatic playlist construction. This is to say, are we doing it right? Are the correct questions even being asked?
3. What is the relationship between notions of music similarity (or pairwise relationship in the generic) and playlist construction?

While all these ideas are interrelated, for now I'm going to pick at point (3) a bit. I'm coming to believe this is central in understanding the other two points as well, at least to an extent. There are many ways to consider how two songs are related. In music informatics this similarity is almost always content-based, even if it isn't content derived. This can include methods based on timbral or harmonic features or most tags or similar labels (though these sometimes get away from content descriptors). This paints some kind of picture but leaves out something that can be critical to manual playlist construct as it is commonly understood (e.g. in radio or the creation of a 'mixtape'), socio-cultural context. In order to have the widest array of possible playlist constructions, it is necessary to have as complete an understanding of the relationship between member songs (not just neighbors...). Put another way, the complexity of your playlist is maximally bound by the complexity of your similarity measure.

M<=Cs

Where M is some not yet existant measure of the possible semantic complexity of a playlist and s is a similar measure of the semantic complexity of the similarity measure used in the construction of that playlist. C is our fudge factor constant. Now, obviously there are plenty of situations where complex structure isn't required. But if the goal is to make playlists for a wide range of functions and settings, it will be required some times.

In practice what this means is that you can make a bag of songs from a bag of features. However, imparting long form structure is at a minimum dependant on a much more complex understanding of the relationships (eg. sim) between songs (say from social networks or radio logs...)

Anyway, this is all a bit vague right now. I'm working on some better formalization, we'll see how that goes. Anyone have any thoughts?

back to playlisting

After a brief tangent into the wide world of mixing (of which I'll post some more in a bit from my proposed SMC paper in a bit) I'm back into playlist generation and related topics.  Along those lines it occurs to me that readers of my little blog may be interested in perusing my recently completed (Dec 2008 actually) M.Phil to PhD upgrade document.  For those of you unfamiliar with the British PhD system, PhD students start as a Master's of Philosophy by research student, then after about 24 months of independent research go through a process of summarizing and defending their work so far and what they intend to accomplish in the coming 18 - 24 months.  The outcome of this process is one of three things:

1. Your work is deemed interesting, rigorous and sufficiently likely to succeed in the next couple of years.  As a result, you upgrade to a PhD student and continue on with your research (this is what happened in my case)
2. You graduate at that point with a M.Phil.
3. You completely fail your upgrade process.

So that happened back in mid december.  Here's the abstract from my upgrade:

A framework is described to consider various real world playlist use cases.  Automatic playlist generation is introduced as a means to improve music recommendation.  Literature in related topics is discussed.

A sample of the Myspace artist network is examined to investigate the relationship between social connectivity and audio-based similarity.  Audio data from the Myspace artist pages is analyzed using well-established signal-based music information retrieval techniques.  In addition to showing that the Myspace artist network exhibits many of the properties common to social networks, it is seen that there is an ambiguous relationship between audio-based similarity and the social connectivity. Further the Myspace sample is examined with the pairwise relational connectivity measure Minimum cut/Maximum flow.  These values are then compared to a pairwise acoustic Earth Mover's Distance measure and the relationship is discussed.  A means of constructing playlists using the maximum flow value to exploit both the social and acoustic distances is realized.

Two playlist generation methods are proposed for development and experimentation.  The first is a direct extension of the myspace dataset analysis into a robust playlist system for interactive internet radio broadcast.  The second is content based system which uses expert constructed playlists to construct transition models which can then be used on new material.  This is followed by a discussion of evaluation needs and strategies. 

 If you're interested in reading the whole thing (comments welcome and encouraged) download the pdf.

On an unrelated note, I'll be a Yahoo Open Hackday 09 in Covent Garden in a couple weekends.  It's free and I believe there are still some tickets if anyone is interested.  It should be rad.

ah semantics

So I'm fairly certain this has been discussed before, but I'm going to pose the question again.  Music Information Retrieval or something else?  I prefer the term Music Informatics as I find it to be more generally encompassing of the sort of work that get labeled with the more well used term music information retrieval, but perhaps not?