A fuzzy rule model for high level musical features on automated composition systems

Algorithmic composition systems are now well-understood. However, when they are used for specific tasks like creating material for a part of a piece, it is common to prefer, from all of its possible outputs, those exhibiting specific properties. Even though the number of valid outputs is huge, many times the selection is performed manually, either using expertise in the algorithmic model, by means of sampling techniques, or some times even by chance. Automations of this process have been done traditionally by using machine learning techniques. However, whether or not these techniques are really capable of capturing the human rationality, through which the selection is done, to a great degree remains as an open question. The present work discusses a possible approach, that combines expert’s opinion and a fuzzy methodology for rule extraction, to model high level features. An early implementation able to explore the universe of outputs of a particular algorithm by means of the extracted rules is discussed. The rules search for objects similar to those having a desired and pre-identified feature. In this sense, the model can be seen as a finder of objects with specific properties.Peer ReviewedPostprint (author's final draft

Creativity and Culture in Copyright Theory

Creativity is universally agreed to be a good that copyright law should seek to promote, yet copyright scholarship and policymaking have proceeded largely on the basis of assumptions about what it actually is. When asked to discuss the source of their inspiration, individual artists describe a process that is intrinsically ineffable. Rights theorists of all varieties have generally subscribed to this understanding, describing creativity in terms of an individual liberty whose form remains largely unspecified. Economic theorists of copyright work from the opposite end of the creative process, seeking to divine the optimal rules for promoting creativity by measuring its marketable byproducts. But these theorists offer no particular reason to think that marketable byproducts are either an appropriate proxy or an effective stimulus for creativity (as opposed to production), and more typically refuse to engage the question. The upshot is that the more we talk about creativity, the more it disappears from view. At the same time, the mainstream of intellectual property scholarship has persistently overlooked a broad array of social science methodologies that provide both descriptive tools for constructing ethnographies of creative processes and theoretical tools for modeling them

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

The play's the thing

For very understandable reasons phenomenological approaches predominate in the field of sensory urbanism. This paper does not seek to add to that particular discourse. Rather it takes Rorty’s postmodernized Pragmatism as its starting point and develops a position on the role of multi-modal design representation in the design process as a means of admitting many voices and managing multidisciplinary collaboration. This paper will interrogate some of the concepts underpinning the Sensory Urbanism project to help define the scope of interest in multi-modal representations. It will then explore a range of techniques and approaches developed by artists and designers during the past fifty years or so and comment on how they might inform the question of multi-modal representation. In conclusion I will argue that we should develop a heterogeneous tool kit that adopts, adapts and re-invents existing methods because this will better serve our purposes during the exploratory phase(s) of any design project that deals with complexity

The play's the thing

For very understandable reasons phenomenological approaches predominate in the field of sensory urbanism. This paper does not seek to add to that particular discourse. Rather it takes Rorty’s postmodernized Pragmatism as its starting point and develops a position on the role of multi-modal design representation in the design process as a means of admitting many voices and managing multidisciplinary collaboration. This paper will interrogate some of the concepts underpinning the Sensory Urbanism project to help define the scope of interest in multi-modal representations. It will then explore a range of techniques and approaches developed by artists and designers during the past fifty years or so and comment on how they might inform the question of multi-modal representation. In conclusion I will argue that we should develop a heterogeneous tool kit that adopts, adapts and re-invents existing methods because this will better serve our purposes during the exploratory phase(s) of any design project that deals with complexity

People-Powered Music: Using User-Generated Tags and Structure in Recommendations

Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches