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A self-organizing algorithm for vector quantizer design applied to signal processing.

F Madeiro¹, R M Vilar, J M Fechine

¹Laboratório de Automção e Processamento de Sinais-LAPS, UFPB-CCT-Campus II-Departamento de Engenharia Elétrica, Campina Grande, PB, Brasil. madeiro@dee.ufpb.br

International Journal of Neural Systems

|November 24, 1999

Summary

A new self-organizing algorithm (SOA) for vector quantizer design offers improved performance in signal compression and speaker identification compared to the traditional Linde-Buzo-Gray (LBG) algorithm. SOA achieves better reconstructed signal quality and higher speaker identification rates.

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Area of Science:

Signal Processing
Machine Learning
Data Compression

Background:

Vector quantization is crucial for speech/speaker recognition and signal compression.
Existing methods like Linde-Buzo-Gray (LBG) have limitations.
Unsupervised learning is key for efficient codebook design.

Purpose of the Study:

Introduce a novel unsupervised algorithm for vector quantizer design, named Self-Organizing Algorithm (SOA).
Compare SOA's performance against the traditional LBG algorithm.
Evaluate SOA's effectiveness in codebook design for Gauss-Markov and Gaussian sources.

Main Methods:

Developed an unsupervised algorithm inspired by Kohonen learning but without a fixed topological neighborhood.
Compared SOA with the LBG algorithm using simulations.
Evaluated codebook design for Gauss-Markov and Gaussian sources against Shannon's Rate-Distortion Theory bounds.

Main Results:

SOA codebooks yielded higher quality reconstructed signals in speech and image compression compared to LBG.
SOA demonstrated superior performance in speaker identification, achieving higher identification rates than LBG.
Investigated the impact of initial codebook selection and the algorithm's pattern learning capabilities.

Conclusions:

The Self-Organizing Algorithm (SOA) presents a viable and effective alternative to traditional methods like LBG for vector quantizer design.
SOA offers significant advantages in both signal compression quality and speaker identification accuracy.
The algorithm's performance is robust and adaptable, showing promise for various signal processing applications.