Description: This paper deals with the problem of speech enhancement when a
corrupted speech signal with an additive colored noise is the only
information available for processing. Kalman filtering is known as
an effective speech enhancement technique, in which speech signal
is usually modeled as autoregressive (AR) process and represented
in the state-space domain.-This paper deals with the problem of speech enhancement when a corrupted speech signal wit h an additive colored noise is the only informat ion available for processing. Kalman filterin g is known as an effective speech enhancement te chnique. in which speech signal is usually modeled as aut oregressive (AR) process and represented in th e state-space domain. Platform: |
Size: 103119 |
Author:rifer |
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Description: This paper deals with the problem of speech enhancement when
only a corrupted speech signal is available for processing. Kalman
filtering is known as an effective speech enhancement technique,
in which speech signal is usually modeled as autoregressive (AR)
model and represented in the state-space domain.-This paper deals with the problem of speech enhancement when only a corrupted speech signa l is available for processing. Kalman filterin g is known as an effective speech enhancement te chnique. in which speech signal is usually modeled as aut oregressive (AR) model and represented in the s tate-space domain. Platform: |
Size: 245868 |
Author:rifer |
Hits:
Description: This paper deals with the problem of speech enhancement when a
corrupted speech signal with an additive colored noise is the only
information available for processing. Kalman filtering is known as
an effective speech enhancement technique, in which speech signal
is usually modeled as autoregressive (AR) process and represented
in the state-space domain.-This paper deals with the problem of speech enhancement when a corrupted speech signal wit h an additive colored noise is the only informat ion available for processing. Kalman filterin g is known as an effective speech enhancement te chnique. in which speech signal is usually modeled as aut oregressive (AR) process and represented in th e state-space domain. Platform: |
Size: 102400 |
Author:rifer |
Hits:
Description: This paper deals with the problem of speech enhancement when
only a corrupted speech signal is available for processing. Kalman
filtering is known as an effective speech enhancement technique,
in which speech signal is usually modeled as autoregressive (AR)
model and represented in the state-space domain.-This paper deals with the problem of speech enhancement when only a corrupted speech signa l is available for processing. Kalman filterin g is known as an effective speech enhancement te chnique. in which speech signal is usually modeled as aut oregressive (AR) model and represented in the s tate-space domain. Platform: |
Size: 245760 |
Author:rifer |
Hits:
Description: 该程序是卡曼滤波法在语音处理上的应用,能有效的去除噪声,达到语音增强的目的!-The program is Kaman filtering method in the voice processing application, can effectively remove the noise, to achieve the purpose of speech enhancement! Platform: |
Size: 1024 |
Author:zhjuna |
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Description: 下载的一个卡尔曼语音增强程序,做了修改后给大家参考参考-Download a Kalman speech enhancement program, made modifications to your information Platform: |
Size: 3072 |
Author:微风 |
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Description: 卡尔曼滤波程序实现语音增强算法,在Matlab上程序实现了该算法-Program for Kalman filtering speech enhancement algorithm in Matlab program implements the algorithm on Platform: |
Size: 3072 |
Author:Chris |
Hits:
Description: A matlab code for speech enhancement using Kalman filtering. Speech corrupted with car inside noise travelling at 60kmph with windows closed. Platform: |
Size: 2048 |
Author:Harish Mahendru |
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Description: A matlab code for speech enhancement using Kalman filtering. Speech corrupted with street noise while sitting inside an auto rickshaw. Platform: |
Size: 2048 |
Author:Harish Mahendru |
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Description: 硕士学位论文
基于卡尔曼滤波的语音增强算法研究
姓名:丌贺-Master degree thesis
Research on speech enhancement algorithm based on Calman filter
Name: don t he Platform: |
Size: 5907456 |
Author:lbxin |
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