Modelling music selection in everyday life with applications for psychology-informed music recommender systems

Music is a highly functional and utilitarian resource. It enables people to regulate emotions, reduce distractions, stimulate physical action, and connect with others. However, with technologically facilitated ubiquitous listening now commonplace, new problems have emerged. The main problem is that of choice: how, given millions of songs to choose from, should providers curate listening experiences? To resolve this, many online platforms employ recommender systems, and there have been concerted efforts to orientate these systems in such a way that they are responsive to the short-term, dynamic needs of listeners in everyday situations. However, there is increasing scrutiny around the impact of automated recommender systems in terms of interpretability and data usage. To this end, researchers have begun exploring ways of integrating knowledge about user behaviours into the recommendation process, rather than through purely data-driven approaches. This thesis aims to bridge these strands of intrigue by exploring an approach to generating situationally determined recommendations, based on an understanding of how and why contextual factors influence music selection in everyday life. This is achieved through three studies, in which contexts, functions, and content of listeners’ music selections are triangulated to make inferences and estimates of situationally congruent musical characteristics. Firstly, a psychometric structure of the functions of music listening is generated. Secondly, this is triangulated with contextual factors and audio features of music selection. Finally, this is supplemented with an exploratory approach to generating recommendations through the explanatory model. These three studies result in both: a preliminary model of goal-orientated music listening that can be deployed by recommender procedures; and provides an exemplar methodology of how to construct behavioural models that can drive such systems. This thesis therefore holds relevance to both psychological research and those interested in music curation techniques

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

Navigating the space of your music

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 121-124).Navigating increasingly large personal music libraries is commonplace. Yet most music browsers do not enable their users to explore their collections in a guided and manipulable fashion, often requiring them to have a specific target in mind. MusicBox is a new music browser that provides this interactive control by mapping a music collection into a two-dimensional space, applying principal components analysis (PCA) to a combination of contextual and content-based features of each of the musical tracks. The resulting map shows similar songs close together and dissimilar songs farther apart. MusicBox is fully interactive and highly flexible: users can add and remove features from the included feature list, with PCA recomputed on the fly to remap the data. MusicBox is also extensible; we invite other music researchers to contribute features to its PCA engine. A small user study has shown that MusicBox helps users to find music in their libraries, to discover new music, and to challenge their assumptions about relationships between types of music.by Anita Shen Lillie.S.M

On the Incorporation of Psychologically-Driven 'Music' Preference Models for Music Recommendation

There are hundreds of millions of songs available to the public, necessitating the use of music recommendation systems to discover new music. Currently, such systems account for only the quantitative musical elements of songs, failing to consider aspects of human perception of music and alienating the listener’s individual preferences from recommendations. Our research investigated the relationships between perceptual elements of music, represented by the MUSIC model, with computational musical features generated through The Echo Nest, to determine how a psychological representation of music preference can be incorporated into recommendation systems to embody an individual’s music preferences. Our resultant model facilitates computation of MUSIC factors using The Echo Nest features, and can potentially be integrated into recommendation systems for improved performance

Binaural virtual auditory display for music discovery and recommendation

Emerging patterns in audio consumption present renewed opportunity for searching or navigating music via spatial audio interfaces. This thesis examines the potential benefits and considerations for using binaural audio as the sole or principal output interface in a music browsing system. Three areas of enquiry are addressed. Specific advantages and constraints in spatial display of music tracks are explored in preliminary work. A voice-led binaural music discovery prototype is shown to offer a contrasting interactive experience compared to a mono smartspeaker. Results suggest that touch or gestural interaction may be more conducive input modes in the former case. The limit of three binaurally spatialised streams is identified from separate data as a usability threshold for simultaneous presentation of tracks, with no evident advantages derived from visual prompts to aid source discrimination or localisation. The challenge of implementing personalised binaural rendering for end-users of a mobile system is addressed in detail. A custom framework for assessing head-related transfer function (HRTF) selection is applied to data from an approach using 2D rendering on a personal computer. That HRTF selection method is developed to encompass 3D rendering on a mobile device. Evaluation against the same criteria shows encouraging results in reliability, validity, usability and efficiency. Computational analysis of a novel approach for low-cost, real-time, head-tracked binaural rendering demonstrates measurable advantages compared to first order virtual Ambisonics. Further perceptual evaluation establishes working parameters for interactive auditory display use cases. In summation, the renderer and identified tolerances are deployed with a method for synthesised, parametric 3D reverberation (developed through related research) in a final prototype for mobile immersive playlist editing. Task-oriented comparison with a graphical interface reveals high levels of usability and engagement, plus some evidence of enhanced flow state when using the eyes-free binaural system

Audio-Based Retrieval of Musical Score Data

Given an audio query, such as polyphonic musical piece, this thesis address the problem of retrieving a matching (similar) musical score data from a collection of musical scores. There are different techniques for measuring similarity between any musical piece such as metadata based similarity measure, collaborative filtering and content-based similarity measure. In this thesis, we use the information in the digital music itself for similarity measures and this technique is known as content-based similarity measure. First we extract chroma features to represents musical segments. Chroma feature captures both melodic information and harmonic information and is robust to timbre variation. Tempo variation in the performance of a same song may cause dissimilarity between them. In order to address this issue we extract beat sequences and combine them with chroma features to obtain beat synchronous chroma features. Next, we use Dynamic Time Warping (DTW) algorithm. This algorithm first computes the DTW matrix between two feature sequences and calculates the cost of traversing from starting point to end point of the matrix. Minimum the cost value, more similar the musical segments are. The performance of DTW is improved by choosing suitable path constraints and path weight. Then, we implement LSH algorithm, which first indexes the data and then searches for a similar item. Processing time of LSH is shorter than that of DTW. For a smaller fragment of query audio, say 30 seconds, LSH outperformed DTW. Performance of LSH depends on the number of hash tables, number of projections per table and width of the projection. Both algorithms were applied in two types of data sets, RWC (where audio and midi are from the same source) and TUT (where audio and midi are from different sources). The contribution of this thesis is twofold. First we proposed a suitable feature representation of a musical segment for melodic similarity. And then we apply two different similarity measure algorithms and enhance their performances. This thesis work also includes development of mobile application capable of recording audio from surroundings and displaying its acoustic features in real time

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation

Music Performance Science (MPS), sometimes termed systematic musicology in Northern Europe, is concerned with designing, testing and applying quantitative measurements to music performances. It has applications in art musics, jazz and other genres. It is least concerned with aesthetic judgements or with ontological considerations of artworks that stand alone from their instantiations in performances. Musicians deliver expressive performances by manipulating multiple, simultaneous variables including, but not limited to: tempo, acceleration and deceleration, dynamics, rates of change of dynamic levels, intonation and articulation. There are significant complexities when handling multivariate music datasets of significant scale. A critical issue in analyzing any types of large datasets is the likelihood of detecting meaningless relationships the more dimensions are included. One possible choice is to create algorithms that address both volume and complexity. Another, and the approach chosen here, is to apply techniques that reduce both the dimensionality and numerosity of the music datasets while assuring the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of timeseries, that can extract time-related characteristics of music performances to generate performance fingerprints (dissimilarities from an ‘average performance’) to be used for comparative purposes. The model is applied to recordings of Arnold Schoenberg’s Phantasy for Violin with Piano Accompaniment, Opus 47 (1949), having initially been validated on Chopin Mazurkas.1 The results are subsequently used to test hypotheses about evolution in performance styles of the Phantasy since its composition. It is hoped that further research will examine other works and types of music in order to improve this model and make it useful to other music researchers. In addition to its benefits for performance analysis, it is suggested that the model has clear applications at least in music fraud detection, Music Information Retrieval (MIR) and in pedagogical applications for music education

A generic approach to the evolution of interaction in ubiquitous systems

This dissertation addresses the challenge of the configuration of modern (ubiquitous, context-sensitive, mobile et al.) interactive systems where it is difficult or impossible to predict (i) the resources available for evolution, (ii) the criteria for judging the success of the evolution, and (iii) the degree to which human judgements must be involved in the evaluation process used to determine the configuration. In this thesis a conceptual model of interactive system configuration over time (known as interaction evolution) is presented which relies upon the follow steps; (i) identification of opportunities for change in a system, (ii) reflection on the available configuration alternatives, (iii) decision-making and (iv) implementation, and finally iteration of the process. This conceptual model underpins the development of a dynamic evolution environment based on a notion of configuration evaluation functions (hereafter referred to as evaluation functions) that provides greater flexibility than current solutions and, when supported by appropriate tools, can provide a richer set of evaluation techniques and features that are difficult or impossible to implement in current systems. Specifically this approach has support for changes to the approach, style or mode of use used for configuration - these features may result in more effective systems, less effort involved to configure them and a greater degree of control may be offered to the user. The contributions of this work include; (i) establishing the the need for configuration evolution through a literature review and a motivating case study experiment, (ii) development of a conceptual process model supporting interaction evolution, (iii) development of a model based on the notion of evaluation functions which is shown to support a wide range of interaction configuration approaches, (iv) a characterisation of the configuration evaluation space, followed by (v) an implementation of these ideas used in (vi) a series of longitudinal technology probes and investigations into the approaches