Viva passed, corrections approved, blog barely updated...

The last couple months have proven me to be a terrible blogger, as I haven't posted at all.

Anyway, that aside, I'm pleased to announce that I have passed my viva with minor corrections (back on march 2nd) and as of about an hour ago, had my submitted corrections approved, which means I'm totally done!

Hoorah!

So before I run off for a bit of celebratory drinking, I thought I'd post the soft copy in the the series of tubes (here's the full pdf) and here is a brief chapter-by-chapter summary:

• Chapter 1: Introduction. We present the set of problems this thesis will address, through a discussion of relevant contexts, including changing patterns in music consumption and listening. The core terms are defined. Constraints imposed on this work are laid out along with our aims. Finally, we provide this outline to expose the structure of the document itself.
• Chapter 2: Playlists and Program Direction. We survey the state of the art in playlist tools and playlist generation. A framework for types of playlists is presented. We then give a brief history of playlist creation. This is followed by a discussion of music similarity, the current state of the art and how playlist generation depends on music similarity. The re- mainder of the chapter covers a representative survey of all things playlist. This includes commercially available tools to make and manage playlists, research into playlist generation and analysis of playlists from a selection of available playlist generators. Having reviewed existing tools and gen- eration methods, we aim to demonstrate that a better understanding of song-to-song relationships than currently exists is a necessary underpin- ning for a robust playlist generation system, and this motivates much of the work in this thesis.
• Chapter 3: Multimodal Social Network Analysis. We present an exten- sive analysis of a sample of a social network of musicians. First we analyse the network sample using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. We then compute content-based pairwise dissimilarity values using the musical data associated with the network sample, and the relationship between those content-based distances and distances from network the- ory are explored. Following this exploration, hybrid graphs and distance measures are constructed and used to examine the community structure of the artist network. We close the chapter by presenting the results of these investigations and consider the recommendation and discovery applications these hybrid measures improve.
• Chapter 4: Steerable Optimizing Self-Organized Radio. Using request radio shows as a base interactive model, we present the Steerable Opti- mizing Self-Organized Radio system as a prototypical music recommender system along side robust automatic playlist generation. This work builds directly on the hybrid models of similarity described in Chapter 3 through the creation of a web-based radio system that interacts with current lis- teners through the selection of periodic requests songs from a pool of nominees. We describe the interactive model behind the request system. The system itself is then described in detail. We detail the evaluation process, though note that the inability to rigorously compare playlists creates some difficulty for a complete study.
• Chapter 5: A Method to Describe and Compare Playlists. In this chapter we survey current means of evaluating playlists. We present a means of comparing playlists in a reduced dimensional space through the use of aggregated tag clouds and topic models. To evaluate the fitness of this measure, we perform prototypical retrieval tasks on playlists taken from radio station logs gathered from Radio Paradise and Yes.com, using tags from Last.fm with the result showing better than random performance when using the query playlist’s station as ground truth, while failing to do so when using time of day as ground truth. We then discuss possible applications for this measurement technique as well as ways it might be improved.
• Chapter 6: Conclusions. We discuss the findings of this thesis in their to- tality. After summarizing the conclusions we discuss possible future work and directions implied by these findings.

Enjoy!

(Also, if you find any deep hiding typos, I'd love to know about them. Not sending it to the printer/binder till Monday...)

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.