D. Ramsay, I. Ananthabhotla, and J. Paradiso, “The Intrinsic Memorability of Everyday Sounds,” in Proc. AES Conference: 2019 AES International Conference on Immersive and Interactive Audio, Mar. 2019, Paper 87. [Online]. Available: https://aes.org/publications/elibrary-page/?id=20434
Ramsay D, Ananthabhotla I, Paradiso J. The Intrinsic Memorability of Everyday Sounds. In: AES Conference: 2019 AES International Conference on Immersive and Interactive Audio. Audio Engineering Society; 2019. Paper 87. Available from: https://aes.org/publications/elibrary-page/?id=20434
@inproceedings{Ramsay2019_20434,
author = {Ramsay, David and Ananthabhotla, Ishwarya and Paradiso, Joseph},
title = {{The Intrinsic Memorability of Everyday Sounds}},
booktitle = {AES Conference: 2019 AES International Conference on Immersive and Interactive Audio},
note = {Paper 87},
year = {2019},
month = mar,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=20434}
}
TY - CPAPER
TI - The Intrinsic Memorability of Everyday Sounds
AU - Ramsay, David
AU - Ananthabhotla, Ishwarya
AU - Paradiso, Joseph
T2 - AES Conference: 2019 AES International Conference on Immersive and Interactive Audio
M1 - Paper 87
PY - 2019
DA - 2019/03/06
UR - https://aes.org/publications/elibrary-page/?id=20434
PB - Audio Engineering Society
LA - en
AB - Of the many sounds we encounter throughout the day, some stay lodged in our minds more easily than others; these may serve as powerful triggers of our memories. In this paper, we measure the memorability of everyday sounds across 20,000 crowd-sourced aural memory games, and then analyze the relationship between memorability and acoustic cognitive salience features; we also assess the relationship between memorability and higher-level gestalt features such as its familiarity, valence, arousal, source type, causal certainty, and verbalizability. We suggest that modeling these cognitive processes opens the door for human-inspired compression of sound environments, automatic curation of large-scale environmental recording datasets, and real-time modification of aural events to alter their likelihood of memorability.
ER -