Tag Archives: music recommendation

Paper on “My musical avatar”

We feel ourselves identified with the type of music we like and we sometimes use music to define our personality. I guess one of the questions I ask to any new person I know is “whad kind of music do you listen to?”.

During the last few years, I have been taking part in a research project, where the main goal is to visualize one’s musical preferences, “The Musical Avatar“. The idea behind is to use computational tools to automatically describe your music (in audio format) in terms of melody, instrumentation, rhythm, etc and use this information to build an iconic representation of one’s musical preferences and to recommend you new music. All the system is only based on content description, i.e. on the signal itself and not on information about the music (context) as found on web sites, etc. And it works! 🙂

We finally published a paper describing the technology behind and its scientific evaluation   at Information Processing & Management journal. This is the complete reference:

Dmitry Bogdanov, Martín Haro, Ferdinand Fuhrmann, Anna Xambó, Emilia Gómez, Perfecto Herrera Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing & Management
Volume 49, Issue 1, pp. 13-33, January 2013

There is much to improve, but you can see my musical avatar below. Can you guess how my favorite music sounds like? You can of course build yours from your last-FM profile here.

Emilia's musical avatar

My automatically generated musical avatar

Highlights

► We propose preference elicitation technique based on explicit preference examples. ► We study audio-based approaches to music recommendation and preference visualization. ► Approaches based on semantics inferred from audio surpass low-level timbre methods. ► Such approaches are close to metadata-based system being suitable for music discovery. ► Proposed visualization captures the core musical preferences of the participants.

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Book on Multimodal Music Processing

I’m very happy to have coauthored a chapter in this book on Multimodal Music Processing, as a result of a seminar that Meinard Muëller, Masataka Goto and Simon Dixon organized last year.

I contributed to a chapter about user modeling and personalization, which I think it’s a key aspect of future MIR systems. Searches, descriptors, similarity measures and classification algorithms should be adapted to different user needs, in order to provide powerful and informative services of recommendation and retrieval.

I hope you will find it interesting!

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