Parameter mapping sonification is the most widely used technique for representing multi-dimensional data in sound. However, it is known to be unreliable when used for detecting information in some types of data. This is generally thought to be the result of the co-dependency of the psychoacoustic dimensions used in the mapping.

Positing its perceptual basis in a theory of embodied cognition, the most common approach to overcoming this limitation involves techniques that afford the interactive exploration of the data using gross body gestures. In some circumstances, such exploration is not possible and, even when it is, it may be neither necessary nor sufficient.

This article explores some other possible reasons for the unreliability of parameter mapping sonification and, drawing from the experience of expressive musical performance, suggests that the problem lies not in the parametric approach per se, nor in the lack of interactivity, but in the extent to which the parameters employed contribute to coherent gestalts. A method for how this might be achieved that relies on the use of micro-gestural information is proposed. While this is speculative, the use of such gestural inflections is well known in music performance, is supported by findings in neuroscience and lends itself to empirical testing.

The desire to make data accessible through the sense of listening has led to ongoing research in the fields of sonification and auditory display since the early 1990s. Coming from the disciplines of computer sciences and human computer interface (HCI), the conceptualisation of sonification has been mostly driven by application areas and methods. On the other hand, the sonic arts, which have always participated in the auditory display community, have a genuine focus on sound. Despite these close interdisciplinary relationships between communities of sound practitioners, a rich and sound- or listening-centred concept of sonification is still missing for design guidelines. Complementary to the useful organisation by fields of application, a proper conceptual framework for sound needs to be abstracted from applications and also to some degree from tasks, as both are not directly related to sound. As an initial approach to recasting the thinking about sonification, we propose a conceptualisation of sonifications along two poles in which sound serves either a normative or a descriptive purpose. According to these two poles, design guidelines can be developed proper to display purposes and listening modes.

Alvin Lucier's Music for Solo Performer (1965), often referred to as the ‘brain wave piece’, has become a key work of experimental music. Its setup, in which the brain waves of a solo performer are made to excite percussion instruments, has given the work a central place in the discourse on artistic sonification. However, only a small number of the authors making reference to the work seem to have studied the score, and even fewer have given thought to the score's implications for performance practice and aesthetic reflection. This paper pays detailed attention to these yet overlooked aspects, drawing on accounts of early performances as well as the authors’ participation in a 2012 performance led by the composer. We also trace the history of live-electronic equipment used for Music for Solo Performer and discuss the work's reception in sonification research.

We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise to robust adaptive global properties. We illustrate the algorithm in both single robot and multi-robot cases via simulations and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype.

Aesthetics are gaining increasing recognition as an important topic in auditory display. This article looks to embodied cognition to provide an aesthetic framework for auditory display design. It calls for a serious rethinking of the relationship between aesthetics and meaning-making in order to tackle the mapping problem which has resulted from historically positivistic and disembodied approaches within the field. Arguments for an embodied aesthetic framework are presented. An early example is considered and suggestions for further research on the road to an embodied aesthetics are proposed. Finally a closing discussion considers the merits of this approach to solving the mapping problem and designing more intuitively meaningful auditory displays.

Emotion is a word not often heard in sonification, though advances in affective computing make the data type imminent. At times the relationship between emotion and sonification has been contentious due to an implied overlap with music. This paper clarifies the relationship, demonstrating how it can be mutually beneficial. After identifying contexts favourable to auditory display of emotion, and the utility of its development to research in musical emotion, the current state of the field is addressed, reiterating the necessary conditions for sound to qualify as a sonification of emotion. With this framework, strategies for display are presented that use acoustic and structural cues designed to target select auditory-cognitive mechanisms of musical emotion. Two sonifications are then described using these strategies to convey arousal and valence though differing in design methodology: one designed ecologically, the other computationally. Each model is sampled at 15-second intervals at 49 evenly distributed points on the AV space, and evaluated using a publically available tool for computational music emotion recognition. The computational design performed 65 times better in this test, but the ecological design is argued to be more useful for emotional communication. Conscious of these limitations, computational design and evaluation is supported for future development.

This essay elaborates a field of general aesthetic considerations relevant to the sonification of data. A set of dialectical tropes are introduced to define the possibility space for organised sonified data: data-in-itself and the listener-for-itself; cognitive support and sabotage; and the Peircean triad of rheme–dicisign–argument. Taken together, these three dialectical parameters elaborate a conceptual space in which strategies can be sought for mapping acoustic parameters to data features, data structure and sonic transformations, all with respect to listener reception. A work-in-progress is discussed in connection with this general aesthetic field, and considerations of the aesthetic space are applied to several works. Finally, the notion of data verité is explored in connection to ‘big data’ and issues related to the transformation of data into information generally.

Sonification is described as an under-utilised dimension of the ‘wow!’ factor in science engagement multi-media. It is suggested that sonification's potential value, like much of the scientific visualisation content, probably lies less in hard facts and more in how it may serve as a stimulant for curiosity. Sound is described as a multi-dimensional phenomenon, and a number of approaches to creating sonifications are reviewed. Design strategies are described for five types of phenomena that were sonified for works created by cosmologist George Smoot III and percussionist/ethnomusicologist Mickey Hart, most particularly for their film Rhythms of the Universe (Hart and Smoot 2013).

Is there a notion of contradiction—let us call it, for dramatic effect, “absolute”—making all contradictions, so understood, unacceptable also for dialetheists? It is argued in this paper that there is, and that spelling it out brings some theoretical benefits. First it gives us a foothold on undisputed ground in the methodologically difficult debate on dialetheism. Second, we can use it to express, without begging questions, the disagreement between dialetheists and their rivals on the nature of truth. Third, dialetheism has an operator allowing it, against the opinion of many critics, to rule things out and manifest disagreement: for unlike other proposed exclusion-expressing-devices (for instance, the entailment of triviality), the operator used to formulate the notion of absolute contradiction appears to be immune both from crippling expressive limitations and from revenge paradoxes—pending a rigorous nontriviality proof for a formal dialetheic theory including it.

In this paper, we study the problem of structural analysis of Web documents aiming at extracting the sectional hierarchy of a document. In general, a document can be represented as a hierarchy of sections and subsections with corresponding headings and subheadings. We developed two machine learning models: heading extraction model and hierarchy extraction model. Heading extraction was formulated as a classification problem whereas a tree-based learning approach was employed in hierarchy extraction. For this purpose, we developed an incremental learning algorithm based on support vector machines and perceptrons. The models were evaluated in detail with respect to the performance of the heading and hierarchy extraction tasks. For comparison, a baseline rule-based approach was used that relies on heuristics and HTML document object model tree processing. The machine learning approach, which is a fully automatic approach, outperformed the rule-based approach. We also analyzed the effect of document structuring on automatic summarization in the context of Web search. The results of the task-based evaluation on TREC queries showed that structured summaries are superior to unstructured summaries both in terms of accuracy and user ratings, and enable the users to determine the relevancy of search results more accurately than search engine snippets.

Statistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expressive feature specification language that can be used to define arbitrarily rich feature models, and two machine learning libraries, each with their own parameters. MaltOptimizer is an interactive system that performs parser optimization in three stages. First, it performs an analysis of the training set in order to select a suitable starting point for optimization. Second, it selects the best parsing algorithm and tunes the parameters of this algorithm. Finally, it performs feature selection and tunes machine learning parameters. Experiments on a wide range of data sets show that MaltOptimizer quickly produces models that consistently outperform default settings and often approach the accuracy achieved through careful manual optimization.