25 research outputs found
Interactive Music Generation with Positional Constraints using Anticipation-RNNs
Recurrent Neural Networks (RNNS) are now widely used on sequence generation
tasks due to their ability to learn long-range dependencies and to generate
sequences of arbitrary length. However, their left-to-right generation
procedure only allows a limited control from a potential user which makes them
unsuitable for interactive and creative usages such as interactive music
generation. This paper introduces a novel architecture called Anticipation-RNN
which possesses the assets of the RNN-based generative models while allowing to
enforce user-defined positional constraints. We demonstrate its efficiency on
the task of generating melodies satisfying positional constraints in the style
of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using
the Anticipation-RNN is of the same order of complexity than sampling from the
traditional RNN model. This fast and interactive generation of musical
sequences opens ways to devise real-time systems that could be used for
creative purposes.Comment: 9 pages, 7 figure
JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
We propose a novel approach for the generation of polyphonic music based on
LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord
progression based on a chord embedding. A second LSTM then generates polyphonic
music from the predicted chord progression. The generated music sounds pleasing
and harmonic, with only few dissonant notes. It has clear long-term structure
that is similar to what a musician would play during a jam session. We show
that our approach is sensible from a music theory perspective by evaluating the
learned chord embeddings. Surprisingly, our simple model managed to extract the
circle of fifths, an important tool in music theory, from the dataset.Comment: Paper presented at the 29th International Conference on Tools with
Artificial Intelligence, ICTAI 2017, Boston, MA, US
Towards an Agile Biodigital Architecture: Supporting a Dynamic Evolutionary and Developmental View of Architecture
Architecture and biology are fields of high complexity. Generative design approaches provide access to continuously increasing complexity in design. Some of these methods are based on biological principles but usually do not communicate the conceptual base necessary to appropriately reflect the input from biology into architecture. To address this, we propose a model for analysis and design of architecture based on a multistaged integrated design process that extends the common morphological process in digital morphogenesis with a typology-based ontological model. Biomimetics, an emerging field to strategically search for information transfer from biology to technological application, will assist in delivering a frame of reference and methodology for establishing valid analogies between the different realms as well as integration of the biological concept into a larger framework of analogy to biological processes. As the biomimetic translation of process and systems information promises more radical innovation, this chapter focuses on the dynamic perspectives provided by biological development and evolution to model the complexity of architecture. The proposed process was used to inform five parallel workshops to explore dynamic biological concepts in design. The potential of the process to investigate biomimetic processes in architecture is then discussed, and future work is outlined
Aesthetic Automata: Synthesis and Simulation of Aesthetic Behaviour in Cellular Automata
This thesis addresses the computational notion of aesthetics in the framework of multistate two-dimensional cellular automata (2D CA). The measure of complexity is a core concept in computational approaches to aesthetics. Shannon's information theory provided an objective measure of complexity, which led to the emergence of various informational theories of aesthetics. However, entropy fails to take into account the spatial characteristics of 2D patterns; these characteristics are fundamental in addressing the aesthetic problem, in general, and of CA-generated patterns, in particular. This thesis proposes two empirically evaluated alternative measures of complexity, taking into account the spatial characteristics of 2D patterns and experimental studies on human aesthetic perception in the visual domain. The measures are extended to robustly quantify the complexity of multi-state 2D CA-generated patterns.
The first model, spatial complexity, is based on the probabilistic spatial distribution of homogeneous/heterogeneous neighbouring cells over the lattice of a multi-state 2D cellular automaton. The second model is based on algorithmic information theory (Kolmogorov complexity) which is extended to estimate the complexity of 2D patterns. The spatial complexity measure presents performance advantage over information-theoretic models, specifically in discriminating symmetries and the orientation in CA-generated patterns, enabling more accurate measurement of complexity in relation to aesthetic evaluations of 2D patterns.
A series of experimental stimuli with various structural characteristics and levels of complexity were generated by seeding 3-state 2D CA with different initial configurations for psychological experiments. The results of experimentation demonstrate the presence of correlation between spatial complexity measures and aesthetic judgements of experimental stimuli. The same results were obtained for the estimations of Kolmogorov complexity of experimental stimuli
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