Generation in context : an exploratory method for musical enquiry

This paper discusses a method, Generation in Context, for interrogating theories of music analysis and music perception. Given an analytic theory, the method consists of creating a generative process that implements the theory in reverse. Instead of using the theory to create analyses from scores, the theory is used to generate scores from analyses. Subjective evaluation of the quality of the musical output provides a mechanism for testing the theory in a contextually robust fashion. The method is exploratory, meaning that in addition to testing extant theories it provides a general mechanism for generating new theoretical insights. We outline our initial explorations in the use of generative processes for music research, and we discuss how generative processes provide evidence as to the veracity of theories about how music is experienced, with insights into how these theories may be improved and, concurrently, provide new techniques for music creation. We conclude that Generation in Context will help reveal new perspectives on our understanding of music.

Song means : Analysing and interpreting recorded popular song by Allan Moore

“Who are you? How do you define yourself, your identity?” With these words Allan Moore opens his exhaustive new work proposing a more comprehensive approach to the musicological analysis of popular song. The last three decades have seen a huge expansion of the anthology of the sociological and cultural meanings of pop, but Moore’s book is not another exploration of this field, although some of these ideas are incorporated in this work. Rather, he addresses the limitations of conventional musicology when dealing particularly with songs: “I address popular song rather than popular music. The defining feature of popular song lies in the interaction of everyday words and music… it is how they interact that produces significance in the experience of song”.

Pedestrian-cyclist interactions at bus stops along segregated bike paths : a case study of Montreal

Safety is one of the major world health issues, and is even more acute for “vulnerable” road users, pedestrians and cyclists. At the same time, public authorities are promoting the active modes of transportation that involve these very users for their health benefits. It is therefore important to understand the factors and designs that provide the best safety for vulnerable road users and encourage more people to use these modes. Qualitative and quantitative shortcomings of collisions make it necessary to use surrogate measures of safety in studying these modes. Some interactions without a collision such as conflicts can be good surrogates of collisions as they are more frequent and less costly. To overcome subjectivity and reliability challenges, automatic conflict analysis using video cameras and deriving users’ trajectories is a solution to overcome shortcomings of manual conflict analysis. The goal of this paper is to identify and characterize various interactions between cyclists and pedestrians at bus stops along bike paths using a fully automated process. Three conflict severity indicators are calculated and adapted to the situation of interest to capture those interactions. A microscopic analysis of users’ behavior is proposed to explain interactions more precisely. Eventually, the study aims to show the capability of automatically collecting and analyzing data for pedestrian-cyclist interactions at bus stops along segregated bike paths in order to better understand the actual and perceived risks of these facilities.

TraSe algorithm : automatic evolutionary morphing of electronic dance music

The TraSe (Transform-Select) algorithm has been developed to investigate the morphing of electronic music through automatically applying a series of deterministic compositional transformations to the source, guided towards a target by similarity metrics. This is in contrast to other morphing techniques such as interpolation or parameters or probabilistic variation. TraSe allows control over stylistic elements of the music through user-defined weighting of numerous compositional transformations. The formal evaluation of TraSe was mostly qualitative and occurred through nine participants completing an online questionnaire. The music generated by TraSe was generally felt to be less coherent than a human composed benchmark but in some cases judged as more creative.

Towards an automatic system for road lane marking extraction in large-scale aerial images acquired over rural areas by hierarchical image analysis and Gabor filter

An automatic approach to road lane marking extraction from high-resolution aerial images is proposed, which can automatically detect the road surfaces in rural areas based on hierarchical image analysis. The procedure is facilitated by the road centrelines obtained from low-resolution images. The lane markings are further extracted on the generated road surfaces with 2D Gabor filters. The proposed method is applied on the aerial images of the Bruce Highway around Gympie, Queensland. Evaluation of the generated road surfaces and lane markings using four representative test fields has validated the proposed method.

Event-related brain potentials dissociate the developmental time-course of automatic numerical magnitude analysis and cognitive control functions during the first three years of primary school

In this study we set out to dissociate the developmental time course of automatic symbolic number processing and cognitive control functions in grade 1-3 British primary school children. Event-related potential (ERP) and behavioral data were collected in a physical size discrimination numerical Stroop task. Task-irrelevant numerical information was processed automatically already in grade 1. Weakening interference and strengthening facilitation indicated the parallel development of general cognitive control and automatic number processing. Relationships among ERP and behavioral effects suggest that control functions play a larger role in younger children and that automaticity of number processing increases from grade 1 to 3.

A survey of tagging techniques for music, speech and environmental sound

Sound tagging has been studied for years. Among all sound types, music, speech, and environmental sound are three hottest research areas. This survey aims to provide an overview about the state-of-the-art development in these areas.We discuss about the meaning of tagging in different sound areas at the beginning of the journey. Some examples of sound tagging applications are introduced in order to illustrate the significance of this research. Typical tagging techniques include manual, automatic, and semi-automatic approaches.After reviewing work in music, speech and environmental sound tagging, we compare them and state the research progress to date. Research gaps are identified for each research area and the common features and discriminations between three areas are discovered as well. Published datasets, tools used by researchers, and evaluation measures frequently applied in the analysis are listed. In the end, we summarise the worldwide distribution of countries dedicated to sound tagging research for years.

Using static analysis for automatic assessment and mitigation of unwanted and malicious activities within Android applications

In the last decade, smartphones have gained widespread usage. Since the advent of online application stores, hundreds of thousands of applications have become instantly available to millions of smart-phone users. Within the Android ecosystem, application security is governed by digital signatures and a list of coarse-grained permissions. However, this mechanism is not fine-grained enough to provide the user with a sufficient means of control of the applications' activities. Abuse of highly sensible private information such as phone numbers without users' notice is the result. We show that there is a high frequency of privacy leaks even among widely popular applications. Together with the fact that the majority of the users are not proficient in computer security, this presents a challenge to the engineers developing security solutions for the platform. Our contribution is twofold: first, we propose a service which is able to assess Android Market applications via static analysis and provide detailed, but readable reports to the user. Second, we describe a means to mitigate security and privacy threats by automated reverse-engineering and refactoring binary application packages according to the users' security preferences.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Automatic clustering and population analysis of white matter tracts using maximum density paths

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

Using quantitative wastewater analysis to measure daily usage of conventional and emerging illicit drugs at an annual music festival

Introduction and Aims Wastewater analysis provides a non-intrusive way of measuring drug use within a population. We used this approach to determine daily use of conventional illicit drugs [cannabis, cocaine, methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA)] and emerging illicit psychostimulants (benzylpiperazine, mephedrone and methylone) in two consecutive years (2010 and 2011) at an annual music festival. Design and Methods Daily composite wastewater samples, representative of the festival, were collected from the on-site wastewater treatment plant and analysed for drug metabolites. Data over 2 years were compared using Wilcoxon matched-pair test. Data from 2010 festival were compared with data collected at the same time from a nearby urban community using equivalent methods. Results Conventional illicit drugs were detected in all samples whereas emerging illicit psychostimulants were found only on specific days. The estimated per capita consumption of MDMA, cocaine and cannabis was similar between the two festival years. Statistically significant (P < 0.05; Z = −2.0–2.2) decreases were observed in use of methamphetamine and one emerging illicit psychostimulant (benzyl piperazine). Only consumption of MDMA was elevated at the festival compared with the nearby urban community. Discussion and Conclusions Rates of substance use at this festival remained relatively consistent over two monitoring years. Compared with the urban community, drug use among festival goers was only elevated for MDMA, confirming its popularity in music settings. Our study demonstrated that wastewater analysis can objectively capture changes in substance use at a music setting without raising major ethical issues. It would potentially allow effective assessments of drug prevention strategies in such settings in the future.

Local music repertoire and the digitization of the international music industry: An empirical analysis 1994 – 2013

In some of the countries where there has been a rapid increase in the use of online music distribution technologies, analysts have reported about declining sales of local music repertoire (e.g. Nordgård, 2013). The analysts are concerned about such tendencies since local music repertoire accounts for a sizable share of an average country’s total recorded music sales (e.g. IFPI, 2012). This paper searches for empirical evidence that may confirm these reports in a number of music markets in North America, Europe and Australasia. The paper makes a contribution to the literature on the digital transformation of the music industry since it combines and analyses data sources that previously have not been used in this context and gives a new perspective on changing user consumption practices in the music industry. The paper also examines the variation of geographic diversity over time among international acts that become commercially successful in the countries covered by the study.

Job-related diversity and team performance: An empirical analysis of the US music industry

A significant amount of research has been carried out to investigate the existing bonds between team characteristics and team outcomes in contexts of social creativity. Specifically, how work group diversity affects its performance is of great relevance but unfortunately, there is no clear understanding of the diversity-performance relationship. Therefore, to improve our understanding of this phenomenon, it would be worthwhile to investigate further empirical settings. For this reason, we decided to study the music industry that, to our knowledge, has never been chosen as empirical setting for the application of the theoretical constructs linked to the topic of team diversity and performance. Our research aims at analyze the US music industry to study the relationship between job-related characteristics of team diversity and team performances.