Probabilistic Modeling Paradigms for Audio Source Separation

This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems

Knowledge transfer in project-based organisations: the need for a unique approach

Effective knowledge transfer between infrastructure projects plays a significant role in organisational success and discovery of new technologies, helping to achieve and maintain competitive advantage and, in effect, sustainable infrastructure development. Knowledge is recognised as an important organisational asset that adds value while being shared. To date, research on knowledge transfer has focused on traditional (functional) types of organisations. However, existing knowledge transfer approaches fail to address the issue of unique characteristics of project-based organisations, and the fact that functional and project-based organisations significantly differ in terms of structure, processes, and characteristics. Therefore, there is a need for a different, separate approach for managing knowledge in the project environment. The aim of this chapter is to highlight this need. An extensive literature review is provided on the areas of project management, knowledge management, and organisational structure; this is further supported by empirical evidence from interviews with project management practitioners. Conducting a ‘cross-field’ literature review provides a better understanding of the knowledge transfer mechanisms and its application to projects, and of the importance of knowledge transfer across projects. This research is crucial to gaining a better understanding of knowledge transfer in the project environment. It stresses that there are dissimilarities between project-based organisations and functional organisations in terms of organisational structure, duration of processes, viewpoint of time, response to change, and mobility of people, and that there is a need for a unique strategic approach in order to achieve effective transfer of knowledge. Furthermore, findings presented in this chapter reveal key elements that play an important role in across project knowledge transfer. These elements include: social communication, lessons learned databases, and project management offices