OK Computer Analysis: An Audio Corpus Study of Radiohead

The application of music information retrieval techniques in popular music studies has great promise. In the present work, a corpus of Radiohead songs across their career from 1992 to 2017 are subjected to automated audio analysis. We examine findings from a number of granularities and perspectives, including within song and between song examination of both timbral-rhythmic and harmonic features. Chronological changes include possible career spanning effects for a band's releases such as slowing tempi and reduced brightness, and the timbral markers of Radiohead's expanding approach to instrumental resources most identified with the Kid A and Amnesiac era. We conclude with a discussion highlighting some challenges for this approach, and the potential for a field of audio file based career analysis

Content Management for the Live Music Industry in Virtual Worlds: Challenges and Opportunities

International audienceThe real-world music industry is undergoing a transition away from the retailing and distribution of fixed objects (records, files) to the consumption of live,interactive events (concerts, happenings). This development is paralleled with the recent flourishing of live music in virtual worlds, which in many ways could become the epitome of its real-world counterpart: for the artists, virtual concerts are cheap and easy to organize, and can therefore be a viable alternative to performing in the real world; for the music promoter and marketer, virtual concert attendance can be traced and analyzed more easily than in the real world; for the virtual concertgoer, attending concerts that are happening a (virtual) world away is possible with a single click. Taking insights from both a survey among the Second-Life music practitioners and from our own prototype of a live music recommendation system built on top of Second-Life, this article shows that the technical infrastructure of current virtual worlds is not well-suited to the development of the content management tools needed to support this opportunity. We propose several new ways to address these problems, and advocate for their recognition both by the artistic and the technical community

(How) Does Data-based Music Discovery Work?

This paper analyses a new type of business operations that mediate the production and consumption of music. Online environment has largely abolished constraints on the variety of music that can be economically distributed, but, at the same time, it reveals another problem. How do people learn what music items do they want to listen to? In the music industry, the product space consists of thousands of artists, songs and albums, and is expanding rapidly. More effective forms of music discovery could therefore create considerable new value by allowing people to listen to music that better matches their taste. We analyse data from Last.fm music discovery service that deploys a collaborative filtering recommender system and social media features to aid music discovery. The analysis finds evidence that the new form of music discovery is valuable to consumers, yet it is relatively less important than an opportunity to listen to music for free. The findings lead us to discuss how the nature of analytical problem and product space, consumer taste, and social media features shape the potential value of created by big data

A hybrid recommender system for improving automatic playlist continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.This work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).Peer ReviewedPostprint (author's final draft

p � Sequential Group Recommendations p

p � Group recommendations p � Rank aggregation – optimal aggregation p � Rank aggregation for group recommendation p � Dimensions considered in the study n � Group size n � Inter group similarity n � Rank aggregation method

Situation Awareness for Recommender Systems

One major shortcoming of traditional recommender systems is their inability to adjust to users' short-term preferences resulting from varying situation-specific factors. To address this, we propose the notion of situation-aware recommender systems, which are supposed to autonomously determine the users' current situation based on a multitude of contextual side information and generate truly personalized recommendations. In particular, we develop a situation awareness model for recommender systems, include it in a situation-aware recommendation process, and derive generic design steps for the design of situation-aware recommender systems. The feasibility of these concepts is demonstrated by directly employing them for the development and implementation of a music recommender system for everyday situations. Moreover, their meaningfulness is shown by means of an empirical user study. The outcomes of the evaluation indicate a significant increase in user satisfaction compared to traditional (i.e. non-situation-aware) recommendations

A hybrid approach for item collection recommendations : an application to automatic playlist continuation

Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version

Recommended by algorithm : relevance, affordances and agency of music recommender systems

Software has indeed become an essential part of how cultural artifacts are circulated. Due to advancements in technology in the last decades, a significant amount of our everyday life activities is now mediated by these various applications. Software does not, however, only provide us tools we need; it also shapes our actions and transfers our boundaries of abilities to act. Therefore, it is not enough to analyze the relationship between user and technology only as a relationship between an actor and a tool. Instead, the relationship should be problematized and it should be acknowledged that it has become more complex and intertwined than ever before. In my thesis, I focus on music recommender systems that are great examples of software technology since they increasingly influence on what information we receive and perceive most relevant. They also represent the development in which personalization and customization of services are becoming more common. In overall, these systems have been studied mostly from the technical perspective leaving a more cultural approach and user point of view often disregarded. In this thesis, I sought to to fill this gap in research by focusing on the user experiences instead of the systems. My main research question was: “how recommender systems shape and participate in the practices of music discovery and consumption of the users?”. This question was further divided into three themes: taste, relevance and agency. In order to be able to answer my research questions, I interviewed eight people by using semi-structured focused interview as my data collection method and analyzed it by using theory-related content analysis. The interviews were conducted in Finnish as well as the analysis. The quotations presented in this thesis, however, are translated into English. The results suggest that the user perceptions of the ability of the recommender systems to learn the taste of the user varied a lot. For some, recommendations were accurate and constructed a stylistic or aesthetic ‘profile’ of the user whereas in other cases, users thought that recommender systems made too simplifying deductions or misinterpreted the taste totally. The attitude towards recommendations was also shaped by how users perceived themselves as discoverers of music. Furthermore, it turned out that music recommender systems have its biases and affordances – for better or worse. The recommender systems were mostly given a great deal of autonomy which blurred the perception of who or what is actually acting