Enzo De Sena

Evaluating vehicle network strategies for downtown Portland: opportunistic infrastructure and the importance of realistic mobility models

In an urban environment, vehicles can opportunistically exploit infrastructure through open Access Points (APs) to efficiently communicate with other vehicles. This is to avoid long wireless ad hoc paths, and to alleviate congestion in the wireless grid. Analytic and simulation models are used to optimize the communications and networking strategies. For realistic results, one important challenge is the accurate representation of traffic mobility patterns. In this paper we introduce realistic vehicular mobility traces of downtown Portland, Oregon, obtained fromextremely detailed large scale traffic simulations performed at the Los Alamos National Laboratories (LANL). To the best of our knowledge, these are among the most accurate synthetic motion traces available for study, with the exception of actual car trace measurements. The new mobility model is used to evaluate AODV [1] in flat and opportunistic infrastructure routing. To assess the importance of a realistic mobility model for this evaluation, we compare these results with those obtained with CORSIM [2] traces. The paper makes the following contributions: (a) introduction of efficient, opportunistic strategies for extending the AP infrastructure to use vehicle to vehicle paths, and (b) assessment of different mobility models - CORSIM traces and LANLs realistic vehicular traces - in the modeling of different routing strategies.

VERGILIUS: A Scenario Generator for VANET

Vehicular networks are on the fast track to become a reality either through a car manufacturer that introduces a communication device in the car electronics or through an aftermarket vendor such a GPS navigator or a in-vehicle entertainment system. This paper introduces VERGILIUS a nouvelle urban mobility and propagation toolbox designed to streamline the mobility trace generation and path loss computation in vehicular network studies. The aim of VERGILIUS is to enable a whole new level of simulation through the introduction of Urban Maps, finely tunable motion patterns, and detailed trace analysis.

Efficient synthesis of room acoustics via scattering delay networks

An acoustic reverberator consisting of a network of delay lines connected via scattering junctions is proposed. All parameters of the reverberator are derived from physical properties of the enclosure it simulates. It allows for simulation of unequal and frequency-dependent wall absorption, as well as directional sources and microphones. The reverberator renders the first-order reflections exactly, while making progressively coarser approximations of higher-order reflections. The rate of energy decay is close to that obtained with the image method (IM) and consistent with the predictions of Sabine and Eyring equations. The time evolution of the normalized echo density, which was previously shown to be correlated with the perceived texture of reverberation, is also close to that of the IM. However, its computational complexity is one to two orders of magnitude lower, comparable to the computational complexity of a feedback delay network and its memory requirements are negligible.

A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

Parametric modeling of room acoustics aims at representing room transfer functions by means of digital filters and finds application in many acoustic signal enhancement algorithms. In previous work by other authors, the use of orthonormal basis functions (OBFs) for modeling room acoustics has been proposed. Some advantages of OBF models over all-zero and pole-zero models have been illustrated, mainly focusing on the fact that OBF models typically require less model parameters to provide the same model accuracy. In this paper, it is shown that the orthogonality of the OBF model brings several additional advantages, which can be exploited if a suitable algorithm for identifying the OBF model parameters is applied. Specifically, the orthogonality of OBF models does not only lead to improved model efficiency (as pointed out in previous work), but also leads to improved model scalability and model stability. Its appealing scalability property derives from a previously unexplored interpretation of the OBF model as an approximation to a solution of the inhomogeneous acoustic wave equation. Following this interpretation, a novel identification algorithm is proposed that takes advantage of the OBF model orthogonality to deliver efficient, scalable, and stable OBF model estimates, which is not necessarily the case for nonlinear estimation techniques that are normally applied.

Perceptual Spatial Audio Recording, Simulation, and Rendering: An overview of spatial-audio techniques based on psychoacoustics

Developments in immersive audio technologies have been evolving in two directions: physically motivated systems and perceptually motivated systems. Physically motivated techniques aim to reproduce a physically accurate approximation of desired sound fields by employing a very high equipment load and sophisticated, computationally intensive algorithms. Perceptually motivated techniques, however, aim to render only the perceptually relevant aspects of the sound scene by means of modest computational and equipment load. This article presents an overview of perceptually motivated techniques, with a focus on multichannel audio recording and reproduction, audio source and reflection culling, and artificial reverberators.

Multichannel identification of room acoustic systems with adaptive filters based on orthonormal basis functions

Many acoustic signal enhancement applications require adaptive filters with a long impulse response, but with a small number of filter parameters. Fixed-poles infinite impulse response (IIR) adaptive filters based on orthonormal basis functions (OBFs) present advantages over finite impulse response filters and other IIR filters, assuring stability and fast global convergence in the adaptation of the filter parameters. A scalable algorithm is introduced for the estimation of the poles of an adaptive OBF filter from multichannel input-output data. The set of poles, common to all the acoustic channels considered, is estimated in parallel to the adaptation of the linear filter parameters. It will be shown that the result of the identification with common poles is quite robust to variations in the room transfer function, suggesting the possibility that poles may be kept fixed after estimation.

A computational model for the estimation of localisation uncertainty

A computational model for prediction of localisation uncertainty of phantom auditory sources is proposed. The interaural level and time difference pairs due to point sources in free field are used as a reference. The mismatch between these “natural” pairs and interaural time and level difference pairs elicited by phantom sources is quantified by means of the 0.5-norm distance, which is justified on psychoacoustic grounds. The model is validated by results of subjective listening tests, achieving a high level of correlation with experimental data.

A generalized design method for directivity patterns of spherical microphone arrays

Spherical microphone arrays provide a flexible solution to obtaining higher-order directivity patterns, which are useful in audio recording and reproduction. A general systematic approach to the design of directivity patterns for spherical microphone arrays is introduced in this paper. The directivity patterns are obtained by optimizing a cost function which is a convex combination of a front-back energy ratio and a smoothness term. Most of the standard directivity patterns - i.e. omnidirectional, cardioid, subcardioid, hypercardioid and supercardioid - are particular solutions of this optimization problem with specific values of two free parameters: the angle of the frontal sector, and the convex combination factor. By varying these two parameters, more general solutions of practical use are obtained.

Improving the perceptual quality of ideal binary masked speech

It is known that applying a time-frequency binary mask to very noisy speech can improve its intelligibility but results in poor perceptual quality. In this paper we propose a new approach to applying a binary mask that combines the intelligibility gains of conventional binary masking with the perceptual quality gains of a classical speech enhancer. The binary mask is not applied directly as a time-frequency gain as in most previous studies. Instead, the mask is used to supply prior information to a classical speech enhancer about the probability of speech presence in different time-frequency regions. Using an oracle ideal binary mask, we show that the proposed method results in a higher predicted quality than other methods of applying a binary mask whilst preserving the improvements in predicted intelligibility.

Room impulse response interpolation using a sparse spatio- temporal representation of the sound field

Room Impulse Responses (RIRs) are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.