This paper presents an audio event recognition methodology in the case of basketball content. The proposed method leverages low-level features of the audio component of basketball videos to identify basic events of the game. Through the process of detecting and defining audio event classes, a sound event taxonomy of the sport is formed. The tasks of detecting acoustic events related to basketball games, namely referee whistles and court air horns, are investigated. For the purpose of audio event detection, a feature vector is extracted and evaluated for the training of one-class classifiers. The detected events are used to segment basketball games, while the results are combined with Speech-To-Text and text mining in order to pinpoint keywords in every segment.
Filippidis, Panagiotis-Marios; Vryzas, Nikolaos; Kotsakis, Rigas; Thoidis, Iordanis; Dimoulas, Charalampos A.; Bratsas, Charalampos
Affiliation: Aristotle University of Thessaloniki, Thessaloniki, Greece
AES Convention: 146 (March 2019) Paper Number: 10190
Publication Date: March 10, 2019
Subject: Poster Session 3
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