Audio content analysis for online audiovisual data segmentation and classification

By Tong Zhang, C.-C. Jay Kuo. IEEE Transactions on Speech and Audio Processing, 2001

454 citations recorded by the source corpus.

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A regularized kernel-based approach to unsupervised audio segmentation (2009, background): “Segmentation is an important preliminary task for archivin g audio or audiovisual content in databases while allowing for cont ent-based retrieval of the data [1].”
Environmental Sound Recognition With Time–Frequency Audio Features (2009, background): “Others include the discrimination between musical instruments [17], [18], musical genres [19], and between variations of speech, nonspeech and music [20]–[22].”
Musical genre classification of audio signals (2002, methodology): “A heuristic rule-based system for the segmentation and classification of audio signals from movies or TV programs based on the time-varying properties of simple features is proposed in [6].”
A comparative study on content-based music genre classification (2003, background): “Zhang and Kuo [34] propose a heuristic rule-based system to segment and classify audio signals from movies or TV programs.”
Features for audio and music classification (2003, background): “In addition, classification is better, on average, if based on features from models of auditory perception rather than on standard features.”
Acoustic Scene Classification: Classifying environments from the sounds they produce (2014, methodology): “streams that also rely on the recognition or clustering of sound events have been used for personal archiving [19] and audio segmentation [33] and retrieval [53].”