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Title: Speech/music discrimination : novel features in time domain
Author: Alnadabi, Muhammad Saeid Muhammad
ISNI:       0000 0004 2683 8928
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2010
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This research aimed to find novel features that can be used to discriminate between speech and music in the time domain for the purpose of data retrieval. The study used speech and music data that were recorded in standard anechoic chambers and sampled at 44.1 kHz. Two types of new features were found and thoroughly examined: the Ratio of Silent Frames (RSF) feature and the Time Series Events (TSE) set of features. The Receiver Operating Characteristics (ROC) curves were used to assess each one of the proposed features as well as certain relevant features from the literature for the purpose of comparison. The RSF feature introduced up to 8% enhancement when compared to a couple of relevant features from the literature. One of the TSE set of features provided close to 100% speech/music discrimination.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available