The symbiosis of improvised and rehearsed elements in the creation of contemporary jazz ensemble music

This research investigates the symbiotic relationship between composition and improvisation and the notion of improvisation itself. With a specific interest in developing, extending and experimenting with the relationship of improvisation within predetermined structures, the creative work component of this research involved composing six new works with varying approaches for The Andrea Keller Quartet and guest improvisers, for performance on a National Australian tour. This is documented in the CD recording Galumphing Round the Nation - Collaborations Tour 2009. The exegesis component is intended to run alongside the creative work and discusses the central issues surrounding improvisation in an ensemble context and the subject of composing for improvisers. Specifically, it questions the notion that when music emphasises a higher ratio of spontaneous to pre-determined elements, and is exposed to the many variables of a performance context, particularly through its incorporation of visitant improvisers, the resultant music should potentially be measurably altered with each performance. This practice-led research demonstrates the effect of concepts such as individuality, variability within context, and the interactive qualities of contemporary jazz ensemble music. Through the analysis and comparison of the treatment of the six pieces over thirteen performances with varying personnel, this exegesis proposes that, despite the expected potential for spontaneity in contemporary jazz music, the presence of established patterns, the desire for familiarity and the intuitive tendency towards accepted protocols ensure that the music which emerges is not as mutable as initially anticipated.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

What it means to become a public relations professional: student perceptions of professional identity through real-world learning

Public relations educators need new solutions to prepare students to become tomorrow's practitioner today. Managers and employers in the new creative workforce (McWilliam, 2008) expect graduates to be problem solvers, critical and creative thinkers, reflective, and self reliant (Barrie, 2008; David, 2004). Enabling students to develop these attributes requires a collaborative and creative approach to pedagogy (Jeffrey & Craft, 2001, 2004). A model for the next generation of public relations education was developed to integrate industry partnerships as a way to bridge pedagogy and professional practice. The model suggests (a) that industry partnerships be embedded in learning activities, (b) that assessment items be considered on a continuum and delivered incrementally across a course of study, and (c) that connections between classroom and workplace activities are clearly signposted for students.

The development of a new signal processing program at the Queensland University of Technology

The School of Electrical and Electronic Systems Engineering at Queensland University of Technology, Brisbane, Australia (QUT), offers three bachelor degree courses in electrical and computer engineering. In all its courses there is a strong emphasis on signal processing. A newly established Signal Processing Research Centre (SPRC) has played an important role in the development of the signal processing units in these courses. This paper describes the unique design of the undergraduate program in signal processing at QUT, the laboratories developed to support it, and the criteria that influenced the design.

On the computation and interpretation of auto- and cross-trispectra

This paper discusses the principal domains of auto- and cross-trispectra. It is shown that the cumulant and moment based trispectra are identical except on certain planes in trifrequency space. If these planes are avoided, their principal domains can be derived by considering the regions of symmetry of the fourth order spectral moment. The fourth order averaged periodogram will then serve as an estimate for both cumulant and moment trispectra. Statistics of estimates of normalised trispectra or tricoherence are also discussed.

Asphalt concrete surfaces macrotexture determination from still images

Road surface macrotexture is identified as one of the factors contributing to the surface's skid resistance. Existing methods of quantifying the surface macrotexture, such as the sand patch test and the laser profilometer test, are either expensive or intrusive, requiring traffic control. High-resolution cameras have made it possible to acquire good quality images from roads for the automated analysis of texture depth. In this paper, a granulometric method based on image processing is proposed to estimate road surface texture coarseness distribution from their edge profiles. More than 1300 images were acquired from two different sites, extending to a total of 2.96 km. The images were acquired using camera orientations of 60 and 90 degrees. The road surface is modeled as a texture of particles, and the size distribution of these particles is obtained from chord lengths across edge boundaries. The mean size from each distribution is compared with the sensor measured texture depth obtained using a laser profilometer. By tuning the edge detector parameters, a coefficient of determination of up to R2 = 0.94 between the proposed method and the laser profilometer method was obtained. The high correlation is also confirmed by robust calibration parameters that enable the method to be used for unseen data after the method has been calibrated over road surface data with similar surface characteristics and under similar imaging conditions.

Improving visual noise insensitivity in small vocabulary audio visual speech recognition applications

Visual noise insensitivity is important to audio visual speech recognition (AVSR). Visual noise can take on a number of forms such as varying frame rate, occlusion, lighting or speaker variabilities. The use of a high dimensional secondary classifier on the word likelihood scores from both the audio and video modalities is investigated for the purposes of adaptive fusion. Preliminary results are presented demonstrating performance above the catastrophic fusion boundary for our confidence measure irrespective of the type of visual noise presented to it. Our experiments were restricted to small vocabulary applications.

Vector quantization based Gaussian modeling for speaker verification

Gaussian mixture models (GMMs) have become an established means of modeling feature distributions in speaker recognition systems. It is useful for experimentation and practical implementation purposes to develop and test these models in an efficient manner particularly when computational resources are limited. A method of combining vector quantization (VQ) with single multi-dimensional Gaussians is proposed to rapidly generate a robust model approximation to the Gaussian mixture model. A fast method of testing these systems is also proposed and implemented. Results on the NIST 1996 Speaker Recognition Database suggest comparable and in some cases an improved verification performance to the traditional GMM based analysis scheme. In addition, previous research for the task of speaker identification indicated a similar system perfomance between the VQ Gaussian based technique and GMMs

Robust speaker verification via fusion of speech and lip modalities

This paper investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. It has been previously shown in our own work, and in the work of others, that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms the performance of either sub-system. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise