Real-time Blind Separation and Deconvolution of Real-world signals

Abstract

We present a realistic and robust implementation of Blind Source Separation and Blind Deconvolution. The algorithm is developed from the idea of natural gradient learning, wavelet filtering and denoising, and the characteristic of different sound source. Several hardware pieces are integrated, including a mobile robot NT workstation and DSP chip to achieve the real time separation of real world signal. Besides, a method of judging the separation performance without knowing the mixing matrix (mixing filter) is proposed and verified.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA605288

Entities

People

  • Yu Mao

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Acoustic Signals
  • Acoustics
  • Algorithms
  • Architectural Acoustics
  • Computational Science
  • Computer Programs
  • Digital Communications
  • Ear
  • Filters
  • Filtration
  • Frequency Bands
  • Frequency Domain
  • Mathematical Filters
  • Operating Systems
  • Random Variables
  • Stability Conditions
  • Statistical Distributions

Fields of Study

  • Engineering

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Database Systems and Applications
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy