431 research outputs found
The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure
The behavior of users of music streaming services is investigated from the
point of view of the temporal dimension of individual songs; specifically, the
main object of the analysis is the point in time within a song at which users
stop listening and start streaming another song ("skip"). The main contribution
of this study is the ascertainment of a correlation between the distribution in
time of skipping events and the musical structure of songs. It is also shown
that such distribution is not only specific to the individual songs, but also
independent of the cohort of users and, under stationary conditions, date of
observation. Finally, user behavioral data is used to train a predictor of the
musical structure of a song solely from its acoustic content; it is shown that
the use of such data, available in large quantities to music streaming
services, yields significant improvements in accuracy over the customary
fashion of training this class of algorithms, in which only smaller amounts of
hand-labeled data are available
Maximum entropy models capture melodic styles
We introduce a Maximum Entropy model able to capture the statistics of
melodies in music. The model can be used to generate new melodies that emulate
the style of the musical corpus which was used to train it. Instead of using
the body interactions of order Markov models, traditionally used in
automatic music generation, we use a nearest neighbour model with pairwise
interactions only. In that way, we keep the number of parameters low and avoid
over-fitting problems typical of Markov models. We show that long-range musical
phrases don't need to be explicitly enforced using high-order Markov
interactions, but can instead emerge from multiple, competing, pairwise
interactions. We validate our Maximum Entropy model by contrasting how much the
generated sequences capture the style of the original corpus without
plagiarizing it. To this end we use a data-compression approach to discriminate
the levels of borrowing and innovation featured by the artificial sequences.
The results show that our modelling scheme outperforms both fixed-order and
variable-order Markov models. This shows that, despite being based only on
pairwise interactions, this Maximum Entropy scheme opens the possibility to
generate musically sensible alterations of the original phrases, providing a
way to generate innovation
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction
RoboJam is a machine-learning system for generating music that assists users
of a touchscreen music app by performing responses to their short
improvisations. This system uses a recurrent artificial neural network to
generate sequences of touchscreen interactions and absolute timings, rather
than high-level musical notes. To accomplish this, RoboJam's network uses a
mixture density layer to predict appropriate touch interaction locations in
space and time. In this paper, we describe the design and implementation of
RoboJam's network and how it has been integrated into a touchscreen music app.
A preliminary evaluation analyses the system in terms of training, musical
generation and user interaction
Régularité, génération de documents, et Cyc
Nous nous intĂ©ressons Ă la modĂ©lisation des rĂ©seaux hiĂ©rarchiques, et avons dĂ©veloppĂ© un modĂšle de hiĂ©rarchies sĂ©mantiques basĂ© sur la RĂGULARITĂ, une gĂ©nĂ©ralisation de lâhĂ©ritage [MIL 88a]. Nous nous intĂ©ressons Ă©galement Ă la gĂ©nĂ©ration de documents sĂ©quentiels structurĂ©s Ă partir de documents hypertextes en utilisant la sĂ©mantique des liens hypertextes pour structurer la prĂ©sentation [MIL 90b]. Nous avons acquis une copie de la base de connaissances CYC [LEN 90a] dans le but de: 1) utiliser le rĂ©seau sĂ©mantique sous-jacent Ă CYC pour aider Ă la gĂ©nĂ©ration de textes, et 2) tester\ud
lâhypothĂšse de la rĂ©gularitĂ©. Ironiquement, la taille gigantesque de CYC a forcĂ© ses concepteurs dâadopter des optimisations dâimplantation qui la rendent peu adaptĂ©e aux explorations logiques profondes requises par la gĂ©nĂ©ration de textes. Par ailleurs, lâĂ©tude des patrons de rĂ©gularitĂ© dans CYC nous a amenĂ© Ă gĂ©nĂ©raliser la notion de rĂ©gularitĂ©, et Ă formuler un certain nombre dâhypothĂšses quant Ă la structure logique de la base de connaissances
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