75,204 research outputs found
Instrumentational complexity of music genres and why simplicity sells
Listening habits are strongly influenced by two opposing aspects, the desire
for variety and the demand for uniformity in music. In this work we quantify
these two notions in terms of musical instrumentation and production
technologies that are typically involved in crafting popular music. We assign a
"complexity value" to each music style. A style is complex if it shows the
property of having both high variety and low uniformity in instrumentation. We
find a strong inverse relation between variety and uniformity of music styles
that is remarkably stable over the last half century. Individual styles,
however, show dramatic changes in their "complexity" during that period. Styles
like "new wave" or "disco" quickly climbed towards higher complexity in the 70s
and fell back to low complexity levels shortly afterwards, whereas styles like
"folk rock" remained at constant high complexity levels. We show that changes
in the complexity of a style are related to its number of sales and to the
number of artists contributing to that style. As a style attracts a growing
number of artists, its instrumentational variety usually increases. At the same
time the instrumentational uniformity of a style decreases, i.e. a unique
stylistic and increasingly complex expression pattern emerges. In contrast,
album sales of a given style typically increase with decreasing complexity.
This can be interpreted as music becoming increasingly formulaic once
commercial or mainstream success sets in.Comment: 17 pages, 5 figures, Supporting Informatio
A Memetic Analysis of a Phrase by Beethoven: Calvinian Perspectives on Similarity and Lexicon-Abstraction
This article discusses some general issues arising from the study of similarity in music, both human-conducted and computer-aided, and then progresses to a consideration of similarity relationships between patterns in a phrase by Beethoven, from the first movement of the Piano Sonata in A flat major op. 110 (1821), and various potential memetic precursors. This analysis is followed by a consideration of how the kinds of similarity identified in the Beethoven phrase might be understood in psychological/conceptual and then neurobiological terms, the latter by means of William Calvin’s Hexagonal Cloning Theory. This theory offers a mechanism for the operation of David Cope’s concept of the lexicon, conceived here as a museme allele-class. I conclude by attempting to correlate and map the various spaces within which memetic replication occurs
Introduction to Gestural Similarity in Music. An Application of Category Theory to the Orchestra
Mathematics, and more generally computational sciences, intervene in several
aspects of music. Mathematics describes the acoustics of the sounds giving
formal tools to physics, and the matter of music itself in terms of
compositional structures and strategies. Mathematics can also be applied to the
entire making of music, from the score to the performance, connecting
compositional structures to acoustical reality of sounds. Moreover, the precise
concept of gesture has a decisive role in understanding musical performance. In
this paper, we apply some concepts of category theory to compare gestures of
orchestral musicians, and to investigate the relationship between orchestra and
conductor, as well as between listeners and conductor/orchestra. To this aim,
we will introduce the concept of gestural similarity. The mathematical tools
used can be applied to gesture classification, and to interdisciplinary
comparisons between music and visual arts.Comment: The final version of this paper has been published by the Journal of
Mathematics and Musi
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Toward a Robust Diversity-Based Model to Detect Changes of Context
Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.Comment: 27th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital
Tune in to your emotions: a robust personalized affective music player
The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application
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