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Measuring a speakers ability to respond to an instantaneous pulse of energy will result in distortion at its output. Factors such as speaker geometry, material properties, equipment error, and the conditions of the environment will create artifacts within the captured data. This paper explores the extraction of time-domain features from these responses, and the training of a predictive model to allow for classification and rapid quality assurance.
Author (s): O`Donnell, Gregg
Affiliation:
Undergraduate Student, University of New Brunswick
(See document for exact affiliation information.)
AES Convention: 158
Paper Number:349
Publication Date:
2025-05-12
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Permalink: https://aes2.org/publications/elibrary-page/?id=22900
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O`Donnell, Gregg; 2025; Supervised Machine Learning for Quality Assurance in Loudspeakers: Time Distortion Analysis [PDF]; Undergraduate Student, University of New Brunswick; Paper 349; Available from: https://aes2.org/publications/elibrary-page/?id=22900
O`Donnell, Gregg; Supervised Machine Learning for Quality Assurance in Loudspeakers: Time Distortion Analysis [PDF]; Undergraduate Student, University of New Brunswick; Paper 349; 2025 Available: https://aes2.org/publications/elibrary-page/?id=22900