Audio information extraction from arbitrary sound recordings

Duncan, PJ, Mohammed, DY and Li, FF ORCID: https://orcid.org/0000-0001-9053-963X 2015, Audio information extraction from arbitrary sound recordings , in: 22nd International Congress on Sound and Vibration (ICSV22), 12th - 16th July, Florence, Italy.

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Abstract

Numerous archives of entertainment soundtracks and other recordings such as environmental noise samples have imposed a big data challenge in audio related industries. This necessitates the use of machine audition and retrieval tools to extract semantic information for various applications. Speech recognition, environmental noise classification and music information retrieval tools haven been developed in the past for specific purposes. Combined use of these tools to process arbitrary sound recordings remains challenging: overlap of diverse sources mitigates the classification, resulting in poor recognition and/or missing content. Following a review of a universal framework for arbitrary soundtrack information mining proposed by the authors, a new solution to the overlapped sound sources has been developed in this paper by iterative signal cleaning techniques. The system classifies the arbitrary audio signals into music, speech, ambient sounds and silence, allowing overlap. Validation tests have shown that the new techniques can reduce or eliminate information losses in machine audition, hence improving the usability of machine audition in processing real-world audio archives. This paper will also discuss the dataset and principles, present the validation results and discuss potential applications.

Item Type:	Conference or Workshop Item (Paper)
Themes:	Media, Digital Technology and the Creative Economy
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	22nd International Congress on Sound and Vibration (ICSV22)
Publisher:	The International Institute of Acoustics and Vibration (iiav)
Refereed:	Yes
Related URLs:	Organisation Organisation
Funders:	Ph.D sponsored by Iraqi governmnent
Depositing User:	PJ Duncan
Date Deposited:	08 Jun 2015 18:15
Last Modified:	15 Feb 2022 19:17
URI:	https://usir.salford.ac.uk/id/eprint/34789

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