Auditory scene analysis as Bayesian inference in sound source models. - Maddie Cusimano; Luke B. Hewitt; Josh Tenenbaum 001; Josh H. McDermott (CogSci, 2018)

Scrape, rub, and roll: causal inference in the perception of sustained contact sounds. - Maddie Cusimano; Josh H. McDermott (CogSci, 2019)

Object-Based Synthesis of Scraping and Rolling Sounds Based on Non-Linear Physical Constraints. - Vinayak Agarwal; Maddie Cusimano; James Traer; Josh H. McDermott (DAFx, 2021)

Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind. - Sean Dae Houlihan; Desmond C. Ong; Maddie Cusimano; Rebecca Saxe (CogSci, 2022)

Modeling Animal Vocalizations through Synthesizers. - Masato Hagiwara; Maddie Cusimano; Jen-Yu Liu (CoRR, 2022)

BEANS: The Benchmark of Animal Sounds. - Masato Hagiwara; Benjamin Hoffman; Jen-Yu Liu; Maddie Cusimano; Felix Effenberger; Katie Zacarian (ICASSP, 2023)

A benchmark for computational analysis of animal behavior, using animal-borne tags. - Benjamin Hoffman; Maddie Cusimano; Vittorio Baglione; Daniela Canestrari; Damien Chevallier; Dominic L. DeSantis; Lorène Jeantet; Monique A. Ladds; Takuya Maekawa; Vicente Mata-Silva; Víctor Moreno-González; Eva Trapote; et al. (CoRR, 2023)

Biodenoising: Animal Vocalization Denoising without Access to Clean Data. - Marius Miron; Sara Keen; Jen-Yu Liu; Benjamin Hoffman; Masato Hagiwara; Olivier Pietquin; Felix Effenberger; Maddie Cusimano (ICASSP, 2025)

Robust detection of overlapping bioacoustic sound events. - Louis Mahon; Benjamin Hoffman; Logan S. James; Maddie Cusimano; Masato Hagiwara; Sarah C. Woolley; Olivier Pietquin (CoRR, 2025)