arXiv subjects

Peter Dodds

Publications and source records attributed to Peter Dodds.

4 recordsLinked to original sources

arXiv2024

Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art

Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers.

cs.CY↗

arXiv2022

Reconstructing the Dynamic Directivity of Unconstrained Speech

This article presents a method for estimating and reconstructing the spatial energy distribution pattern of natural speech, which is crucial for achieving realistic vocal presence in virtual communication settings. The method comprises two stages. First, recordings of speech captured by a real, static microphone array are used to create an egocentric virtual array that tracks the movement of the speaker over time. This virtual array is used to measure and encode the high-resolution directivity pattern of the speech signal as it evolves dynamically with natural speech and movement. In the second stage, the encoded directivity representation is utilized to train a machine learning model that can estimate the full, dynamic directivity pattern given a limited set of speech signals, such as those recorded using the microphones on a head-mounted display. Our results show that neural networks can accurately estimate the full directivity pattern of natural, unconstrained speech from limited information. The proposed method for estimating and reconstructing the spatial energy distribution pattern of natural speech, along with the evaluation of various machine learning models and training paradigms, provides an important contribution to the development of realistic vocal presence in virtual communication settings.

eess.AS↗

arXiv2022

Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks

Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning. Our framework is driven by a carefully designed neural network that jointly estimates the impulse response and the (apriori unknown) spectral noise characteristics of an observed signal given the source signal. We demonstrate robustness in estimation, even under low signal-to-noise ratios, and show strong results when learning from spatio-temporal real-world speech data. Our framework provides a natural way to interpolate impulse responses on a spatial grid, while also allowing for efficiently compressing and storing them for real-time rendering applications in augmented and virtual reality.

cs.SD↗

arXiv2019

Logarithmic submajorization, uniform majorization and H\"older type inequalities for $\tau$-measurable operators

We extend the notion of the determinant function $\Lambda$, originally introduced by T.Fack for $\tau$-compact operators, to a natural algebra of $\tau$-measurable operators affiliated with a semifinite von Neumann algebra which coincides with that defined by Haagerup and Schultz in the finite case and on which the determinant function is shown to be submultiplicative. Application is given to H\"older type inequalities via general Araki-Lieb-Thirring inequalities due to Kosaki and Han and to a Weyl-type theorem for uniform majorization.

math.FA↗