Recomposing the City: A Survey of Recent Sound Art in Belfast

This article introduces examples of recent sound art in Belfast, a city that has undergone radical transformation over the past decade and is home to a burgeoning community of sound artists. The text investigates the ways in which sonic art can redraw boundaries in a city historically marked by myriad political, socioeconomic,
religious and sectarian divisions. The article focuses on sound works that reimagine a “post-conflict” Belfast. These include site-specific sound installations in urban and public spaces, soundwalks, sculptures, locative and online works, and experimental
sonic performances that draw upon traditional Irish song and music.

Adaptive NormalHedge for robust visual tracking

In this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.

To be inside someone else's dream: On Music for Sleeping & Waking Minds

Music for Sleeping & Waking Minds (2011-2012) is a new,overnight work in which four performers fall asleep while wearing custom designed EEG sensors which monitor their brainwave activity. The data gathered from the EEG sensors is applied in real time to different audio and image signal processing functions, resulting in continuously evolving multichannel sound environment and visual projection. This material serves as an audiovisual description of the individual and collective neuro physiological state of the ensemble. Audiences are invited to experience the work in different states of attention: while alert and asleep, resting and awakening.

Deep Salience: Visual Salience Modeling via Deep Belief Propagation

Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem’s biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform eleven state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the best F-measure and mean-square error in the experiments.

Mixed Messages: American Correspondences in Visual and Verbal Practices

Mixed Messages presents and interrogates ten distinct moments from the arts of nineteenth, twentieth and twenty-first century America where visual and verbal forms blend and clash. Charting correspondences concerned with the expression and meaning of human experience, this volume moves beyond standard interdisciplinary theoretical approaches to consider the written and visual artwork in embodied, cognitive, and contextual terms.

Offering a genuinely interdisciplinary contribution to the intersecting fields of art history, avant-garde studies, word-image relations, and literary studies, Mixed Messages takes in architecture, notebooks, poetry, painting, conceptual art, contemporary art, comic books, photographs and installations, ending with a speculative conclusion on the role of the body in the experience of digital mixed media. Each of the ten case studies explores the juxtaposition of visual and verbal forms in a manner that moves away from treating verbal and visual symbols as operating in binary or oppositional systems, and towards a consideration of mixed media, multi-media and intermedia work as brought together in acts of creation, exhibition, reading, viewing, and immersion. The collection advances research into embodiment theory, affect, pragmatist aesthetics, as well as into the continuing legacy of romanticism and of dada, conceptual art and surrealism in an American context.

The idea, the machine, and the art: word and image in the 21st century

Taking in recent advances in neuroscience and digital technology, Gander and Garland assess the state of the inter-arts in America and the Western world, exploring and questioning the primacy of affect in an increasingly hypertextual everyday environment. In this analysis they signal a move beyond W. J. T. Mitchell’s coinage of the ‘imagetext’ to an approach that centres the reader-viewer in a recognition, after John Dewey, of ‘art as experience’. New thinking in cognitive and computer sciences about the relationship between the body and the mind challenges any established definitions of ‘embodiment’, ‘materiality’, ‘virtuality’ and even ‘intelligence, they argue, whilst ‘Extended Mind Theory’, they note, marries our cognitive processes with the material forms with which we engage, confirming and complicating Marshall McLuhan’s insight, decades ago, that ‘all media are “extensions of man”’. In this chapter, Gander and Garland open paths and suggest directions into understandings and critical interpretations of new and emerging imagetext worlds and experiences.

Museum City - Improvisation and the narratives of space

This paper provides four viewpoints on the narratives of space, allowing us to think about possible relations between sites and sounds, reflecting on how places might tell stories, or how practitioners embed themselves in a place in order to shape cultural, social and/or political narratives through the use of sound. I propose four viewpoints that investigate the relationship between sites and sounds, where narratives are shaped and made through the exploration of specific sonic activities. These are:
- sonic activism
- sonic preservation
- sonic participatory action
- sonic narrative of space

I examine each of these ideas in turn before focusing in more detail on the final viewpoint, which provides the context for discussing and analysing a recent site-specific music improvisation project, entitled ‘Museum City’, a work that aligns closely with my proposal for a ‘sonic narrative of space’.
The work ‘Museum City’ by Pedro Rebelo, Franziska Schroeder, Ricardo Jacinto and André Cepeda specifically enables me to reflect on how derelict and/or transitional spaces might be re-examined through the use of sound, particularly through means of live music improvisation. The spaces examined as part ‘Museum City’ constitute either deserted sites or sites about to undergo changes in their architectural layout, their use and sonic make-up. The practice in ‘Museum City’ was born out of a performative engagement with[in] those sites, but specifically out of an intimate listening relationship by three improvisers situated within those spaces.
The theoretical grounding for this paper is situated within a wider context of practising and cognising musical spatiality, as proposed by Georgina Born (2013), particularly her proposition for three distinct lineages that provide an understanding of space in/and music. Born’s third lineage, which links more closely with practices of sound art and challenges a Euclidean orientation of pitch and timbre space, makes way for a heightened consideration of listening and ‘the place’ of sound. This lineage is particularly crucial for my discussion, since it positions music in relation to social experiences and the everyday, which the work ‘Museum City’ endeavoured to embrace.

Learning about probability from text and tables: Do color coding and labeling through an interactive-user interface help?

Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly assigned to learn about probabilistic reasoning from one of 4 computer-based lessons generated from a 2 (color coding/no color coding) by 2 (labeling/no labeling) between-subjects design. Learners added the labels or color coding at their own pace by clicking buttons in a computer-based lesson. Participants' eye movements were recorded while viewing the lesson. Labeling was beneficial for learning, but color coding was not. In addition, labeling, but not color coding, increased attention to important information in the table and time with the lesson. Both labeling and color coding increased looks between the text and corresponding information in the table. The findings provide support for the multimedia principle, and they suggest that providing labeling enhances learning about probabilistic reasoning from text and tables