VOICE ACTIVITY DETECTION BASED ON HIGHER ORDER CUMULANTS AND CONVOLUTION
Resumen
This paper refers to the application of higher-order statistical signal processing techniques (cumulant calculation) on noise
reduction. The performed procedure, joined to a convolution process, results in the complete estimation (i.e., amplitude,
frequency and phase recovery) of any corrupted periodic signal. The aim of this work lies in its application to the voice
activity detection (VAD) for environments with high noise levels. The minimum signal to noise ratio for all experiments
using the proposed algorithm was -5dB. Obtained results are highly satisfactory compared with existing models.
KEYWORDS: Higher-Order Statistics; Noise Reduction; Convolution; Voice.
MSC: 93E11 93E10 93C40
reduction. The performed procedure, joined to a convolution process, results in the complete estimation (i.e., amplitude,
frequency and phase recovery) of any corrupted periodic signal. The aim of this work lies in its application to the voice
activity detection (VAD) for environments with high noise levels. The minimum signal to noise ratio for all experiments
using the proposed algorithm was -5dB. Obtained results are highly satisfactory compared with existing models.
KEYWORDS: Higher-Order Statistics; Noise Reduction; Convolution; Voice.
MSC: 93E11 93E10 93C40
Texto completo:
PDFEnlaces refback
- No hay ningún enlace refback.