The rapid proliferation of generative audio synthesis and editing technologies has raised significant concerns about copyright infringement, data provenance, and the spread of misinformation through deepfake audio. Watermarking offers a proactive solution by embedding imperceptible, identifiable, and traceable marks into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to achieve both robust detection and accurate attribution simultaneously. This paper introduces the Cross-Attention Robust Audio Watermark (XAttnMark), which bridges this gap by leveraging partial parameter sharing between the generator and the detector, a cross-attention mechanism for efficient message retrieval, and a temporal conditioning module for improved message distribution. Additionally, we propose a psychoacoustic-aligned temporal-frequency masking loss that captures fine-grained auditory masking effects, enhancing watermark imperceptibility. Our approach achieves state-of-the-art performance in both detection and attribution, demonstrating superior robustness against a wide range of audio transformations, including challenging generative editing with strong editing strength. This work represents a significant step forward in protecting intellectual property and ensuring the authenticity of audio content in the era of generative AI.

Inverse problems describe the task of recovering an underlying signal of interest given observables. Typically, the observables are related via some non-linear forward model applied to the underlying unknown signal. Inverting the non-linear forward model can be computationally expensive, as it often involves computing and inverting a linearization at a series of estimates. Rather than inverting the physics-based model, we instead train a surrogate forward model (emulator) and leverage modern auto-grad libraries to solve for the input within a classical optimization framework. Current methods to train emulators are done in a black box supervised machine learning fashion and fail to take advantage of any existing knowledge of the forward model. In this article, we propose a simple learned weighted average model that embeds linearizations of the forward model around various reference points into the model itself, explicitly incorporating known physics. Grounding the learned model with physics based linearizations improves the forward modeling accuracy and provides richer physics based gradient information during the inversion process leading to more accurate signal recovery. We demonstrate the efficacy on an ocean acoustic tomography (OAT) example that aims to recover ocean sound speed profile (SSP) variations from acoustic observations (e.g. eigenray arrival times) within simulation of ocean dynamics in the Gulf of Mexico.

A common problem in recommendation systems is to learn a model of user preferences based only on comparisons of the relative attractiveness of different items. We consider this problem in the context of an ideal point model of user preference, where each user can be represented as a point in a low-dimensional space together with a set of items. In this model, the closer an item is to a user’s ideal point, the more that user prefers the item. When an embedding of items is known a priori, the problem of localizing a user’s ideal point from comparisons amongst items is well studied. However, relatively little work exists on learning embeddings for new items based only on such comparisons. In this paper, we consider the problem of embedding a set of items using paired comparisons from a set of known users. Specifically, we present a novel convex lifted method of learning the embedding representation p1,…,pn∈ Rnof n items given noisy responses of the form "user ukprefers item pito item pj" for an arbitrary set of users {uk} in Rd. We provide a range of simulations that validate the efficacy of our approach.

We develop a framework for localizing an unknown point w using paired comparisons of the form "w is closer to point xithan to xj" when the points lie in a union of known subspaces. This model, which extends a broad class of existing methods to exploit union of subspaces structure, provides a powerful framework for using the types of structure found in many practical applications. We divide the problem into two phases: (1) determining which subspace w lies in, and (2) localizing w within the identified subspace using existing techniques. We introduce two algorithms for determining the subspace in which an unknown point lies: the first admits a sample complexity guarantee demonstrating the advantage of the union of subspaces model, and the second improves performance in practice using an adaptive Bayesian strategy. We demonstrate the efficacy of our method with experiments on synthetic data and in an image search application.