Efficient Implementation of Voiced/Unvoiced Sounds Classification Based on GMM for SMV Codec

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概要

• 論文の詳細を見る

• In this letter, we propose an efficient method to improve the performance of voiced/unvoiced (V/UV) sounds decision for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). We first present an effective analysis of the features and the classification method adopted in the SMV. And feature vectors which are applied to the GMM are then selected from relevant parameters of the SMV for the efficient V/UV classification. The performance of the proposed algorithm are evaluated under various conditions and yield better results compared to the conventional method of the SMV.

• （社）電子情報通信学会の論文
• 2009-08-01

著者

• Song Ji-hyun

  School Of Electronic Engineering Inha University

• CHANG Joon-Hyuk

  School of Electronic Engineering, Inha University

• Chang Joon-hyuk

  School Of Electronic And Electrical Engineering Inha University

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