• Home
•  
• Call for Papers
• Paper Status
• Registration
• Workshops
• Exhibits
•  
• Technical Program
• Side Meetings
• Tutorials
• Plenaries
• Special Sessions
• Panel Sessions
• My Schedule
•  
• Practicalities
• Housing & Venue
• Special Events
• Visits
• Supporters
•  
• Contact / Discussion
• Organizing Committee
• Program Committee
• Review Committee
•  
• Related Conferences

Technical Program

Paper Detail

Paper:	MLSP-P5.11
Session:	Blind Source Separation III
Time:	Friday, May 19, 14:00 - 16:00
Presentation:	Poster
Topic:	Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis
Title:	PHONEMES AS SHORT TIME COGNITIVE COMPONENTS
Authors:	Ling Feng, Lars Kai Hansen, Technical University of Denmark, Denmark
Abstract:	Cognitive component analysis (COCA) is defined as the process of unsupervised grouping of data such that the resulting group structure is well-aligned with that resulting from human cognitive activity [1]. In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive units in the sound system of a language. Generalizable components were found deriving from phonemes based on homomorphic filtering features with basic time scale (20 msec). We sparsified the features based on energy as a preprocessing means to eliminate the intrinsic noise. Independent component analysis was compared with latent semantic indexing, and was demonstrated to be a more appropriate model in COCA.