University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Phase as a feature extraction tool for audio classification and signal localisation.

Paraskevas, Ioannis. (2005) Phase as a feature extraction tool for audio classification and signal localisation. Doctoral thesis, University of Surrey (United Kingdom)..

10148092.pdf - Version of Record
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (6MB) | Preview


The aim of this research is to demonstrate the significance of signal phase content in time localization issues in synthetic signals and in the extraction of appropriate features from acoustically similar audio recordings (non-synthetic signals) for audio classification purposes. Published work, relating to audio classification, tends to be1 focused on the discrimination of audio classes that are dissimilar acoustically. Consequently, a wide range of features, extracted from the audio recordings, has been appropriate for the classification task. In this research, the audio classification application involves audio recordings (digitized through the same pre-processing conditions) that are acoustically similar and hence, only a few features are appropriate, due to the similarity amongst the classes. The difficulties in processing the phase spectrum of a signal have probably led previous researchers to avoid its investigation. In this research, the sources of these difficulties are studied and certain methods are employed to overcome them. Subsequently, the phase content of the signal has been found to be useful for various applications. The justification of this, is demonstrated via audio classification (non-synthetic signals) and time localization (synthetic signals) applications. Summarizing, the original contributions, introduced based on this research work, are the 'whitened' Hartley spectrum and its short-time analysis, as well as the use of the Hartley phase cepstrum as a time localization tool and the use of phase related feature vectors for the audio classification application.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors :
Paraskevas, Ioannis.
Date : 2005
Depositing User : EPrints Services
Date Deposited : 09 Nov 2017 12:16
Last Modified : 16 Jan 2018 11:19

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800