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Intrinsic Dimensionality and its Implication for Performance Prediction in Pattern Classification
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
2006 (English)In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828Article in journal (Other academic) Submitted
Place, publisher, year, edition, pages
IEEE, 2006.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-5788OAI: oai:DiVA.org:kth-5788DiVA: diva2:10283
Note

QS 20120316

Available from: 2006-05-23 Created: 2006-05-23 Last updated: 2016-12-06Bibliographically approved
In thesis
1. Entropy and Speech
Open this publication in new window or tab >>Entropy and Speech
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

In this thesis, we study the representation of speech signals and the estimation of information-theoretical measures from observations containing features of the speech signal. The main body of the thesis consists of four research papers.

Paper A presents a compact representation of the speech signal that facilitates perfect reconstruction. The representation is constituted of models, model parameters, and signal coefficients. A difference compared to existing speech representations is that we seek a compact representation by adapting the models to maximally concentrate the energy of the signal coefficients according to a selected energy concentration criterion. The individual parts of the representation are closely related to speech signal properties such as spectral envelope, pitch, and voiced/unvoiced signal coefficients, bene cial for both speech coding and modi cation.

From the information-theoretical measure of entropy, performance limits in coding and classi cation can be derived. Papers B and C discuss the estimation of di erential entropy. Paper B describes a method for estimation of the di erential entropies in the case when the set of vector observations (from the representation) lie on a lower-dimensional surface (manifold) in the embedding space. In contrast to the method presented in Paper B, Paper C introduces a method where the manifold structures are destroyed by constraining the resolution of the observation space. This facilitates the estimation of bounds on classi cation error rates even when the manifolds are of varying dimensionality within the embedding space.

Finally, Paper D investigates the amount of shared information between spectral features of narrow-band (0.3-3.4 kHz) and high-band (3.4-8 kHz) speech. The results in Paper D indicate that the information shared between the high-band and the narrow-band is insufficient for high-quality wideband speech coding (0.3-8 kHz) without transmission of extra information describing the high-band.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. xii, 38 p.
Series
Trita-EE, ISSN 1653-5146 ; 2006:014
Keyword
speech representation, energy concentration, entropy estimation, manifolds
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-3990 (URN)91-628-6861-6 (ISBN)
Public defence
2006-06-08, D3, Lindstedtsvägen 5, Stockholm, 14:00
Opponent
Supervisors
Note
QC 20100914Available from: 2006-05-23 Created: 2006-05-23 Last updated: 2010-09-14Bibliographically approved

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