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vad（语音活动检测）的matlab代码，采用频域的方法计算信噪比。-vad (voice activity detection) Matlab code, using frequency-domain approach to calculate the signal-to-noise ratio.
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语音端点检测是语音识别中至关重要的技术。无论军用还是民用，语音端点检测都有着广泛的应用。在低信噪比的环境中进行精确的端点检测比较困难，尤其是在无声段或者发音前后-voice activity detection is critical speech recognition technologies. Whether military or civilian, voice endpoint detection have broad application. Low signal-to-noise
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ITU-T G.729语音压缩算法。
descr iption:
Fixed-point descr iption of commendation G.729 with ANNEX B Coding of Speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuo
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语音端点检测Matlab程序，附带计算信噪比的Matlab子程序，文本文档内是常用语音库的下载网址，非常使用-Voice activity detection Matlab program calculated signal to noise ratio with Matlab subroutine, the text is a common voice within the document library to download URL is used
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为提高语音端点检测(VAD)在较低信噪比(10 dB)下的准确率,提出一种基于短时分形维数的改进算法。结合语音信号的特点,对2种常用的语音信号分形维数计算方法进行了比较和选择,同时采用动态跟随门限值实现语音端点的自适应检测。试验结果表明:对于信噪比6~10 dB的带噪语音,此方法可以实现整段语音的检测,而且具有一定的噪声鲁棒性,系统运行期间能够自适应调整门限值以适应环境噪声的变化,提高了VAD算法的准确率。这个是源码matlab。-In order to improve voice activi
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为了实现高速语音特征参数的提取，在分析了美尔频率倒谱特征参数提取算法的基础上，提出了算法的硬件
设计方案，介绍了各模块的设计原理。该方案增加了语音激活检测功能，可对语音信号中的噪音帧进行检测，提高了特征参
数的可靠性。-In order to achieve high-speed voice characteristic parameter extraction, in the analysis of Mel frequency cepstral feature extraction a
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一个基于MMSE噪声后验概率估计的话音激活检测算法.-A voice activity detector with MMSE a posteriori noise estimation and Decision-Directed SNR estimation
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一种用于3G 系统中复杂背景噪声环境下的话音激活检测算法-One for 3G systems under complex background noise environment, voice activity detection algorithm
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活动语音端点检测的程序，在较强背景噪音下，具有较好的效果-Voice activity detection process, under the strong background noise, with good results
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In this paper we propose a method for voice
activity detection (VAD) in a speech signal recorded in the
presence of noise. The so-called endpoint detection (EPD),
i.e., detection of voice activity (speech) boundaries is very
difficult if the
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detect voice from signal of speach Voice Activity Detector (VAD)
with MMSE a posteriori noise estimation and Decision-Directed SNR estimation-detect voice from signal of speach Voice Activity Detector (VAD)
with MMSE a posteriori noise est
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GIPS Video&Voice Engine。
GIPSVideoEngine动态库说明：
GIPSVideoEngine是一个基于WebRTC的Video&Voice Engine，当前主要支持Windows和Android，Linux/MAC/iOS也在开发中，
支持如下功能：
1. 音频采集（支持Windows Wave和Windows core），编码（当前主要支持ISAC， iLBC， PCM， g722等，可以扩展支持729，723等），
音频处理（
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An Automatic Gain Controller (AGC) for speech signals embedded in additive noise requires Voice Activity
Detection (VAD) to avoid noise amplification, a peak level detector for computing gain, and a gain
controller for adjusting gain. This paper
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vad（语音活动检测）的matlaab代码，使用频域的方法计算信噪比。
-vad (voice activity detection) matlaab code, using the frequency domain method to calculate the signal to noise ratio.
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G.720.1是ITU-T最新制定的语音标准，其主要包括活动话音检测、语音和噪声分类功能，可以适用与宽带和窄带语音。-G.720.1 is a newly developed speech standard ITU-T, which mainly includes a voice activity detection, voice and noise classification function can be applied with both broadband and narrowband
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语音增强 维纳滤波 VAD语音激活检测估计噪声-Wiener filtering speech enhancement estimate the noise VAD Voice Activity Detection
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本文首先研究了语音激活检测算法。对基于短时能量和短时过零率双门限法语音激活检测的噪声估计算法做了研究及仿真，同时还研究了一种基于最小子带能量的噪声估计方法。-Firstly, studied voice activity detection algorithms. Based on activity detection and short-term energy zero rate threshold method voice noise estimation algorithm to do t
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Speech processing applications such as speech enhancement and speaker identification rely on the estimation of relevant parameters from the speech signal. These
parameters must often be estimated from noisy observations since speech signals are
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Ngo,Kim Variable speech distortion weighted multichannel wiener filter based on soft output voice activity detection for noise reduction in hearing aids matlab 实现-Ngo,Kim Variable speech distortion weighted multichannel wiener filter based on soft o
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vad_directed_by_noise_classification.m
This code is an implementation of VAD algorithm proposed in:
Robust voice activity detection directed by noise classification
please cite the article in your paper:
Robust voice activity detec
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