Listening and remembering: networked off-line improvisation for four commuters

This paper analyses the experience of the networked off-line improvisation 'Listening and Remembering', a performance for four commuters using voices and sounds from the Mexico City and Paris metros. It addresses the question: how can an act of collective remembering, inspired by listening to metro soundscapes, lead to the creation of networked voice- and sound-based narratives about the urban commuting experience? The networked experience is seen here from the structural perspective (telematic setting), the sonic underground context, the ethnographic process that led to the performance, the narratives that are created in the electro-acoustic setting, the shared acoustic environments that those creations suggest, and the technical features and participants' responses that prevent or facilitate interaction. Emphasis is placed on the participants' status as non-performers, and on their familiarity with the sonic environment, as a context that allows the participation of non-musicians in the making of music through telematically shared interfaces, using soundscape and real-time voice. Participants re-enact their routine experience through a dialogical relation- ship with the sounds, the other participants, themselves, and the experience of sharing: a collective memory

Landscape History and Theory: from Subject Matter to Analytic Tool

This essay explores how landscape history can engage methodologically with the adjacent disciplines of art history and visual/cultural studies. Central to the methodological problem is the mapping of the beholder � spatially, temporally and phenomenologically. In this mapping process, landscape history is transformed from subject matter to analytical tool. As a result, landscape history no longer simply imports and applies ideas from other disciplines but develops its own methodologies to engage and influence them. Landscape history, like art history, thereby takes on a creative cultural presence. Through that process, landscape architecture and garden design regain the cultural power now carried by the arts and museum studies, and has an effect on the innovative capabilities of contemporary landscape design

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on Autonomous Mental Development (TAMD