A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors

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概要

• 論文の詳細を見る

• In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.

• （社）電子情報通信学会の論文
• 2008-06-01

著者

• CHANG Joon-Hyuk

  Department of Electronic Engineering, Inha University

• Chang Joon-hyuk

  Department Of Electrical And Computer Engineering University Of California At Santa Barbara

• Lee Kye-hwan

  Department Of Electronic Engineering Inha University

• Park Yun-sik

  Department Of Electronic Engineering Inha University

• JO Q-Haing

  Department of Electronic Engineering, Inha University

• Jo Q-haing

  Department Of Electronic Engineering Inha University

関連論文

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• Discriminative Weight Training for Support Vector Machine-Based Speech/Music Classification in 3GPP2 SMV Codec
• Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine
• A Statistical Model-Based V/UV Decision under Background Noise Environments(Speech and Hearing)
• Efficient Speech Reinforcement Based on Low-Bit-Rate Speech Coding Parameters
• Multiband Vector Quantization Based on Inner Product for Wideband Speech Coding(Speech and Hearing)
• A Novel Approach to a Robust a Priori SNR Estimator in Speech Enhancement(Multimedia Systems for Communications)
• A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors
• Improved Global Soft Decision Incorporating Second-Order Conditional MAP in Speech Enhancement

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