MIRages: an account of music audio extractors, semantic description and context-awareness, in the three ages of MIR

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, such 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.Aquesta tesi informa sobre recerca realitzada i publicada durant els últims vint anys en diferents problemes de Recuperació d'Informació Musical (MIR). Organitzem el text com a visió personal i reflexió crítica i utilitzant quatre hipotètiques edats que han configurat l'evolució del MIR. A l'edat dels extractors de característiques, presentem treballs sobre trets per a descriure sons i música, especialment timbre i aspectes tonals. A l'edat dels descriptors semàntics es treballa en la descripció de música amb conceptes d'alt nivell, com l'estat d'ànim, els instruments, les similituds, les versions musicals o els gèneres, generalment deduïts amb l'aprenentatge automàtic a partir de col·leccions anotades. En l'era dels sistemes sensibles al context, informem sobre models d'usuaris amb l’objectiu de fer recomanacions musicals i generació d'avatars, a més de factors que influeixen en les decisions d'escoltar música. S’esmenta, finalmente, una posible i més recent edat dels sistemes creatius on els descriptors, classificadors, models i metodologies d'avaluació de MIR ajuden a potenciar o ampliar la creació musical.  Programa de doctorat en Tecnologies de la Informació i les Comunicacion

MIRages: an account of music audio extractors, semantic description and context-awareness, in the three ages of MIR

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, such 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.Aquesta tesi informa sobre recerca realitzada i publicada durant els últims vint anys en diferents problemes de Recuperació d'Informació Musical (MIR). Organitzem el text com a visió personal i reflexió crítica i utilitzant quatre hipotètiques edats que han configurat l'evolució del MIR. A l'edat dels extractors de característiques, presentem treballs sobre trets per a descriure sons i música, especialment timbre i aspectes tonals. A l'edat dels descriptors semàntics es treballa en la descripció de música amb conceptes d'alt nivell, com l'estat d'ànim, els instruments, les similituds, les versions musicals o els gèneres, generalment deduïts amb l'aprenentatge automàtic a partir de col·leccions anotades. En l'era dels sistemes sensibles al context, informem sobre models d'usuaris amb l’objectiu de fer recomanacions musicals i generació d'avatars, a més de factors que influeixen en les decisions d'escoltar música. S’esmenta, finalmente, una posible i més recent edat dels sistemes creatius on els descriptors, classificadors, models i metodologies d'avaluació de MIR ajuden a potenciar o ampliar la creació musical.

Música y persuasión

Disponible versió en català: http://hdl.handle.net/10230/4491

Música i persuasió

Disponible versió en castellà: http://hdl.handle.net/10230/4490

Taking advantage of editorial metadata to recommend music

Comunicació presentada al 9th International Symposium on Computer Music Modeling and Retrieval, celebrat del 19 al 22 de juny de 2012 a Londres, Regne Unit.In this work we propose a novel approach to music recommendation based exclusively on editorial metadata. To this end, we propose to use a public database of music releases Discogs.com, which contains extensive information about artists, their releases and record labels. We rely on an explicit set of music tracks provided by the user as evidence of his/her music preferences to construct a user profile suitable for distance-based music recommendation. We evaluate the proposed method against two purely metadata-based approaches and one approach partially based on audio content in a listening experiment with 27 participants. The results of subjective evaluation show that the proposed method is competitive to the state-of-the-art recommenders based on commercial metadata, while being easily implemented relying only on open public data.The authors would like to thank all participants involved in the evaluation. This research has been partially supported by the FI Grant of Generalitat de Catalunya (AGAUR) and the Classical Planet (TSI-070100- 2009-407, MITYC), DRIMS (TIN2009-14247-C02-01, MICINN), and MIRES (EC-FP7 ICT-2011.1.5 Networked Media and Search Systems, grant agreement No. 287711) projects

Music and speech in early development: automatic analysis and classification of prosodic features from two Portuguese variants

In the present study we aim to capture rhythmic and melodic patterning in speech and singing directed to infants. We address this issue by exploring the acoustic features that best predict different classification problems. We built a database composed by infant-directed speech from two Portuguese variants (European vs Brazilian Portuguese) and infant-directed singing from the two cultures, comprising 977 tokens. Machine learning experiments were conducted in order to automatically discriminate between language variants for speech, vocal songs and between interaction contexts. Descriptors related with rhythm exhibited strong predictive ability for both speech and singing language variants’ discrimination tasks, presenting different rhythmic patterning for each variant. Common features could be used by a classifier to discriminate speech and singing, indicating that the processing of speech and singing may share the analysis of the same stimulus properties. With respect to discriminating interaction contexts, pitch-related descriptors showed better performance. We conclude that prosodic cues present in the surrounding sonic environment of an infant are rich sources of information not only to make distinctions between different communicative contexts through melodic cues, but also to provide specific cues about the rhythmic identity of their mother tongue. These prosodic differences may lead to further research on their influence in the development of the infant’s musical representations

How much metadata do we need in music recommendation?: a subjective evaluation using preference sets

Comunicació presentada a: 2th International Society for Music Information Retrieval Conference (ISMIR 2011) celebrat del 24 al 28 d'octubre de 2011 a Miami, EEUU.In this work we consider distance-based approaches to music recommendation, relying on an explicit set of music tracks provided by the user as evidence of his/her music preferences. Firstly, we propose a purely content-based approach, working on low-level (timbral, temporal, and tonal) and inferred high-level semantic descriptions of music. Secondly, we consider its simple refinement by adding a minimum amount of genre metadata. We compare the proposed approaches with one content-based and three metadata-based baselines. As such, we consider content-based approach working on inferred semantic descriptors, a tag-based recommender exploiting artist tags, a commercial black-box recommender partially employing collaborative filtering information, and a simple genre-based random recommender. We conduct a listening experiment with 19 participants. The obtained results reveal that although the low-level/semantic content-based approach does not achieve the performance of the baseline working exclusively on the inferred semantic descriptors, the proposed refinement provides significant improvement in the listeners’ satisfaction comparable with metadata-based approaches, and surpasses these approaches by the number of novel relevant recommendations. We conclude that the proposed content-based approach refined by simple genre metadata is suited for music discovery not only in the long-tail but also within popular music items.This research has been partially funded by the FI Grant of Generalitat de Catalunya (AGAUR) and the Buscamedia (CEN-20091026), Classical Planet (TSI-070100-2009-407, MITYC), and DRIMS (TIN2009-14247- C02-01, MICINN) projects