No abstract

Sound zones are typically created using Acoustic Contrast Control (ACC), Pressure Matching (PM), or variations of the two. ACC maximizes the acoustic potential energy contrast between a listening zone and a quiet zone. Although the contrast is maximized, the phase is not controlled. To control both the amplitude and the phase, PM instead minimizes the difference between the reproduced sound field and the desired sound field in all zones. On the surface, ACC and PM seem to control sound fields differently, but we here demonstrate they are actually extreme special cases of a much more general framework. The framework is inspired by the variable span linear filtering framework for speech enhancement. Using this framework, we demonstrate that 1) ACC gives the best contrast, but the highest signal distortion in the bright zone, and 2) PM gives the smallest signal distortion in the bright zone, but the worst contrast. Aside from showing this mathematically, we also demonstrate this via a small toy example.

A new single-and multichannel audio recordings database (SMARD) is presented in this paper. The database contains recordings from a box-shaped listening room for various loudspeaker and array types. The recordings were made for 48 different configurations of three different loudspeakers and four different microphone arrays. In each configuration, 20 different audio segments were played and recorded ranging from simple artificial sounds to polyphonic music. SMARD can be used for testing algorithms developed for numerous application, and we give examples of source localisation results.

Model comparison and selection is an important problem in many model-based signal processing applications. Often, very simple information criteria such as the Akaike information criterion or the Bayesian information criterion are used despite their shortcomings. Compared to these methods, Djuric's asymptotic MAP rule was an improvement, and in this paper we extend the work by Djuric in several ways. Specifically, we consider the elicitation of proper prior distributions, treat the case of real-and complex-valued data simultaneously in a Bayesian framework similar to that considered by Djuric, and develop new model selection rules for a regression model containing both linear and non-linear parameters. Moreover, we use this framework to give a new interpretation of the popular information criteria and relate their performance to the signal-to-noise ratio of the data. By use of simulations, we also demonstrate that our proposed model comparison and selection rules outperform the traditional information criteria both in terms of detecting the true model and in terms of predicting unobserved data. The simulation code is available online.