This tesis reports on research carried out and published during the last twenty years on different problems of Music Information Retrieval (MIR). We organize the text as a personal account and critical reflection along four hypothesized ages that have shaped the evolution of MIR. In the age of feature extractors, we present work on features to describe sounds and music, especially timbre and tonal aspects. In the age of semantic descriptors work on describing music with high-level concepts,

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... as mood, instruments, similarities, cover versions or genres, usually inferred with machine learning from annotated collections is reported. In the age of context-aware systems we report on user models for recommendation and for avatar generation, in addition to factors that influence music listening decisions. We finally discuss the possibility of a more recent age of creative systems where MIR features, classifiers, models and evaluation methodologies aid to enhance or expand music creation. The thesis is a compendium of different papers published in peer-reviewed journals and conferences: Herrera, P. & Bonada, J. (1998). Vibrato extraction and parameterization in the spectral modelling synthesis framework. Proceedings of the Digital Audio Effects Workshop (DAFX98), Barcelon, Spain. Herrera, P., Yeterian, A., Gouyon, F. (2002). Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques. In C. Anagnostopoulou et al. (Eds), "Music and Artificial Intelligence". Lecture Notes in Computer Science V. 2445. Berlin: Springer-Verlag. Herrera, P., Peeters, G., Dubnov, S. (2003). Automatic Classification of Musical Instrument Sounds. Journal of New Music Research. 32(1), pp. 3-21. Gómez, E. & Herrera, P. (2008). Comparative Analysis of Music Recordings from Western and Non-Western traditions by Automatic Tonal Feature Extraction. Empirical Musicology Review, 3(3), pp. 140-156. Bogdanov, D., Wack, N., Gómez, E., Gulati S., Herrera, P., Mayor, O., Roma, G., Salamon [...]