- D. Ellis, B. Raj, Judith C. Brown, M. Slaney, P. Smaragdis. 2006. Editorial: Special Section on Statistical and Perceptual Audio Processing. Abstract: H UMAN perception has always been an inspiration for automatic processing systems, not least because tasks such as speech recognition only exist because people do them—and, indeed, without that example we might wonder if they were possible at all. As computational power grows, we have increasing opportunities to model and duplicate perceptual abilities with greater fidelity, and, most importantly, based on larger and larger amounts of raw data describing both what signals exist in the real world, and how people respond to them. The power to deal with large data sets has meant that approaches that were once mere theoretical possibilities, such as exhaustive search of exponentially-sized codebooks, or real-time direct convolution of long sequences, have become increasingly practical and even unremarkable. A major consequence of this is the growth of statistical or corpus-based approaches, where complex relations, discriminations, or structures are inferred directly from example data (for instance by optimizing the parameters of a very general algorithm). An increasing number of complex tasks can be given empirically optimal solutions based on large, representative datasets. The traditional idea of perceptually-inspired processing is to develop a machine algorithm for a complex task such as melody recognition or source separation through inspiration and intro-spection about how individuals perform the task, and on the basis of direct psychological or neurophysiological data. The results can appear to be at odds with the statistical perspective, since perceptually-motivated work is often ad-hoc, comprising many stages whose individual contributions are difficult to separate. We believe that it is important to unify these two approaches: to employ rigorous, exhaustive techniques taking advantage of the statistics of large data sets to develop and solve perceptually based and subjectively-defined problems. With this in mind, we organized a one-day workshop on Statistical and Perceptual Audio Processing as a satellite to the International Although independent of the workshops, this special issue is based on the same insight and goal. In our call for papers, and in our editorial choices of which papers to consider for the special issue, we were looking specifically for rigorous, statistical techniques applied to perceptually-defined tasks or processing in innovative and interesting ways. As can be seen from the papers that follow, this seemingly narrow focus can be interpreted in a wide variety of ways. In fact, we received more than 50 submissions for the special issue—many more than we ex-pected—which says something about the timeliness and resonance …
