arXiv subjects

Rumeshika Pallewela

Publications and source records attributed to Rumeshika Pallewela.

4 recordsLinked to original sources

arXiv2026

Acoustic Image Source Interpolation with Optimal Transport Barycenter

Room impulse responses can be estimated via the image source model (ISM) using the image source point cloud (ISPC) of a physical source. However, because the source movement changes the ISPC, estimating the ISPC at a new source position typically requires repeated acoustic measurements. We propose an optimal transport (OT) barycenter framework to interpolate the ISPC of a new source location from ISPCs of known sources. The method jointly estimates image-source associations and the ISPC at the new location. The OT ground cost exploits the property that the image sources undergo the same displacement as their physical sources. This approach is realized for both grid-based and support-free configurations. The support-free method addresses the resulting nonconvex joint estimation problem by alternating between identifying image-source associations across the ISPCs and refining the target image-source locations. This enables the interpolation of ISPCs without repeated measurements, facilitating efficient and flexible room-acoustic modeling.

eess.AS↗

arXiv2026

All-pole centroids in the Wasserstein metric with applications to clustering of spectral densities

In this work, we propose a method for computing centroids, or barycenters, in the spectral Wasserstein-2 metric for sets of power spectral densities, where the barycenters are restricted to belong to the set of all-pole spectra with a certain model order. This may be interpreted as finding an autoregressive representative for sets of second-order stationary Gaussian processes. While Wasserstein, or optimal transport, barycenters have been successfully used earlier in problems of spectral estimation and clustering, the resulting barycenters are non-parametric and the complexity of representing and storing them depends on, e.g., the choice of discretization grid. In contrast, the herein proposed method yields compact, low-dimensional, and interpretable spectral centroids that can be used in downstream tasks. Computing the all-pole centroids corresponds to solving a non-convex optimization problem in the model parameters, and we present a gradient descent scheme for addressing this. Although convergence to a globally optimal point cannot be guaranteed, the sub-optimality of the obtained centroids can be quantified. The proposed method is illustrated on a problem of phoneme classification.

eess.SP↗

arXiv2025

Room Impulse Response Estimation through Optimal Mass Transport Barycenters

In this work, we consider the problem of jointly estimating a set of room impulse responses (RIRs) corresponding to closely spaced microphones. The accurate estimation of RIRs is crucial in acoustic applications such as speech enhancement, noise cancellation, and auralization. However, real-world constraints such as short excitation signals, low signal-to-noise ratios, and poor spectral excitation, often render the estimation problem ill-posed. In this paper, we address these challenges by means of optimal mass transport (OMT) regularization. In particular, we propose to use an OMT barycenter, or generalized mean, as a mechanism for information sharing between the microphones. This allows us to quantify and exploit similarities in the delay-structures between the different microphones without having to impose rigid assumptions on the room acoustics. The resulting estimator is formulated in terms of the solution to a convex optimization problem which can be implemented using standard solvers. In numerical examples, we demonstrate the potential of the proposed method in addressing otherwise ill-conditioned estimation scenarios.

eess.AS↗

arXiv2024

An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications

This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications. The MEE criterion leverages higher-order statistical properties, offering robustness in noisy scenarios like Rayleigh fading and impulsive interference. In addition, we propose a less computationally complex version of the MEE function to enhance practical usability in wireless communications. The method is evaluated through simulations on two critical applications: over-the-air regression and indoor localization. Results indicate that the MEE criterion outperforms conventional loss functions, such as mean squared error (MSE) and mean absolute error (MAE), achieving significant performance improvements in terms of accuracy, over $20 \%$ gain over traditional methods, and convergence speed across various channel conditions. This work establishes MEE as a promising alternative for wireless communication tasks in deep learning models, enabling better resilience and adaptability.

cs.IT↗