ISMIR 2009 is over – but it will not be soon forgotten.  It was a wonderful event, with seemingly flawless execution.  Some of my favorite things about the conference this year:

1. The proceedings – distributed on a USB stick hidden in a pen that has a laser! And the battery for the laser recharges when you plug the USB stick into your computer.  How awesome is that!?  (The printed version is very nice too, but it doesn’t have a laser).
2. The hotel – very luxurious while at the same time, very affordable.  I had a wonderful view of Kobe, two very comfortable beds and a toilet with more controls than the dashboard on my first car.
3. The presentation room – very comfortable with tables for those sitting towards the front, great audio and video and plenty of power and wireless for all.
4. The banquet – held in the most beautiful room in the world with very exciting Taiko drumming as entertainment.
5. The details – it seems like the organizing team paid attention to every little detail and request – they had taped numbers on the floor so that the 30 folks giving their 30 second pitches during poster madness would know just where to stand, to the signs on the coffeepots telling you that the coffee was being made, to the signs on the train to the conference center welcoming us to ISMIR 2009.  It seems like no detail was left to chance.
6. The food – our stomachs were kept quite happy – with sweet breads and pastries every morning,  bento boxes for lunch, and coffee, juices, waters, and the  mysterious beverage ‘black’ that I didn’t dare to try. My absolute favorite meal was the box lunch during the tutorial day – it was a box with a string – when you are ready to eat you give the string a sharp tug – wait a few minutes for the magic to do its job and then you open the box and eat a piping hot bowl of noodles and vegetables.  Almost as cool as the laser-augmented proceedings.
7. The city – Kobe is a really interesting city – I spent a few days walking around and was fascinated by it all. I really felt like I was walking around in the future.  It was extremely clean, the people will very polite, friendly and always willing to help.  Going into some parts of town was sensory overload, the colors, sounds, smells, the sights were overwhelming – it was really fun.
8. the Keynote – music making robots – what more is there to say.
9. The Program – the quality of papers was very high – there was some outstanding posters and oral presentations.  Much thanks to George and Keiji for organizing the reviews to create a great program. (More on my favorite posters and papers in an upcoming post)
10. f(mir) – The student-organized workshop looked at what MIR research would look like in 10, 20 or even 50 years (basically after I’m dead and gone). The presentations in this workshop were quite provactive – well done students!

I write this post as I sit in the airport in Osaka waiting for my flight home.  I’m tired, but very energized to explore the many new ideas that I encountered at the conference. It was a great week.  I want to extend my personal thanks to Professor Fujinaga and Professor Goto and the rest of the conference committee for putting together a wonderful week.

Masataka and Ichiro at the conference table

Oral Session 5 – Tags

Session Chair: Paul Lamere

I’m the session chair for this session, so I can’t keep notes. So instead I offer the abstracts.

TAG INTEGRATED MULTI-LABEL MUSIC STYLE CLASSIFICATION WITH HYPERGRAPH

Fei Wang, Xin Wang, Bo Shao, Tao Li    Mitsunori Ogihara

Abstract: Automatic music style classification is an important, but challenging problem in music information retrieval. It has a number of applications, such as indexing of and search- ing in musical databases. Traditional music style classifi- cation approaches usually assume that each piece of music has a unique style and they make use of the music con- tents to construct a classifier for classifying each piece into its unique style. However, in reality, a piece may match more than one, even several different styles. Also, in this modern Web 2.0 era, it is easy to get a hold of additional, indirect information (e.g., music tags) about music. This paper proposes a multi-label music style classification ap- proach, called Hypergraph integrated Support Vector Ma- chine (HiSVM), which can integrate both music contents and music tags for automatic music style classification. Experimental results based on a real world data set are pre- sented to demonstrate the effectiveness of the method.

EASY AS CBA: A SIMPLE PROBABILISTIC MODEL FOR TAGGING MUSIC

Matthew D. Hoffman, David M. Blei, Perry R. Cook

ABSTRACT Many songs in large music databases are not labeled with semantic tags that could help users sort out the songs they want to listen to from those they do not. If the words that apply to a song can be predicted from audio, then those predictions can be used both to automatically annotate a song with tags, allowing users to get a sense of what qualities characterize a song at a glance. Automatic tag prediction can also drive retrieval by allowing users to search for the songs most strongly characterized by a particular word. We present a probabilistic model that learns to predict the probability that a word applies to a song from audio. Our model is simple to implement, fast to train, predicts tags for new songs quickly, and achieves state-of-the-art performance on annotation and retrieval tasks.

USING ARTIST SIMILARITY TO PROPAGATE SEMANTIC INFORMATION

Joon Hee Kim, Brian Tomasik, Douglas Turnbull

ABSTRACT Tags are useful text-based labels that encode semantic information about music (instrumentation, genres, emotions, geographic origins). While there are a number of ways to collect and generate tags, there is generally a data sparsity problem in which very few songs and artists have been accurately annotated with a sufficiently large set of relevant tags. We explore the idea of tag propagation to help alleviate the data sparsity problem. Tag propagation, originally proposed by Sordo et al., involves annotating a novel artist with tags that have been frequently associated with other similar artists. In this paper, we explore four approaches for computing artists similarity based on dif- ferent sources of music information (user preference data, social tags, web documents, and audio content). We com- pare these approaches in terms of their ability to accurately propagate three different types of tags (genres, acoustic de- scriptors, social tags). We find that the approach based on collaborative filtering performs best. This is somewhat surprising considering that it is the only approach that is not explicitly based on notions of semantic similarity. We also find that tag propagation based on content-based mu- sic analysis results in relatively poor performance.

MUSIC MOOD REPRESENTATIONS FROM SOCIAL TAGS

Cyril Laurier, Mohamed Sordo, Joan Serra, Perfecto Herrera

ABSTRACT This paper presents findings about mood representations. We aim to analyze how do people tag music by mood, to create representations based on this data and to study the agreement between experts and a large community. For this purpose, we create a semantic mood space from last.fm tags using Latent Semantic Analysis. With an unsuper- vised clustering approach, we derive from this space an ideal categorical representation. We compare our commu- nity based semantic space with expert representations from Hevner and the clusters from the MIREX Audio Mood Classification task. Using dimensional reduction with a Self-Organizing Map, we obtain a 2D representation that we compare with the dimensional model from Russell. We present as well a tree diagram of the mood tags obtained with a hierarchical clustering approach. All these results show a consistency between the community and the ex- perts as well as some limitations of current expert models. This study demonstrates a particular relevancy of the basic emotions model with four mood clusters that can be sum- marized as: happy, sad, angry and tender. This outcome can help to create better ground truth and to provide more realistic mood classification algorithms. Furthermore, this method can be applied to other types of representations to build better computational models.

EVALUATION OF ALGORITHMS USING GAMES: THE CASE OF MUSIC TAGGING

Edith Law, Kris West, Michael Mandel, Mert Bay, J. Stephen Downie

Abstract Search by keyword is an extremely popular method for retrieving music. To support this, novel algorithms that automatically tag music are being developed. The conventional way to evaluate audio tagging algorithms is to com- pute measures of agreement between the output and the ground truth set. In this work, we introduce a new method for evaluating audio tagging algorithms on a large scale by collecting set-level judgments from players of a human computation game called TagATune. We present the de- sign and preliminary results of an experiment comparing five algorithms using this new evaluation metric, and con- trast the results with those obtained by applying several conventional agreement-based evaluation metrics.