Description: 航空噪声背景下的语音端点检测和语音增强算法，在信噪比很低的强航空噪声背景下，效果特别好-aviation noise background voice endpoint detection and voice enhancement algorithms, and very low signal to noise ratio in the strong aviation background noise, particularly effective Platform: |
Size: 1436 |
Author:永强 |
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Description: 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 ratio in the environment for accurate endpoint detection more difficult, especially in or pronunciation of the silent before and after Platform: |
Size: 531719 |
Author:李一 |
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Description: 航空噪声背景下的语音端点检测和语音增强算法，在信噪比很低的强航空噪声背景下，效果特别好-aviation noise background voice endpoint detection and voice enhancement algorithms, and very low signal to noise ratio in the strong aviation background noise, particularly effective Platform: |
Size: 1024 |
Author:永强 |
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Description: 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 ratio in the environment for accurate endpoint detection more difficult, especially in or pronunciation of the silent before and after Platform: |
Size: 531456 |
Author:李一 |
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Description: 在低信噪比条件下的语音端点检测与增强方法-In low signal to noise ratio under the conditions of voice activity detection and enhancement method Platform: |
Size: 294912 |
Author:wangkaixi |
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Description: 在有背境噪音的条件下，进行语音端点检测，并取得很好的效果-Have background in the noise conditions, for voice activity detection and achieved good results Platform: |
Size: 622592 |
Author:读宴宾 |
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Description: 在强噪声环境下，说话人话音受到很强烈的干扰，语音增强是很有必要的。在TDS5410-TDK实验板，实现了谱衰减算法。主要包括有声/无声检测算法、发送/接收缓冲区的调度、语音降噪算法。试验结果表明，其降噪效果明显，试听效果较好。
-In strong noise environment, the speaker voice by the very strong interference, speech enhancement is necessary. TDS5410-TDK experiments in panels, to achieve a spectral attenuation algorithms. Include audio/silence detection algorithm, Send/Receive buffer scheduling, voice noise reduction algorithm. The experimental results show that the effect of its noise reduction obviously, listen better. Platform: |
Size: 25600 |
Author:chenziqiang |
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Description: ：由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效， 提出
应用独立成分分析( I C A) 方法对声音信号进行特征提取， 并证明了这种 I C A 变换能增强语音和音
乐信号的超高斯性． 在此基础上， 应用 I C A基函数作为滤波器， 通过阈值化的去噪方法对含有强高
斯背景噪声的声音信号进行去噪仿真实验． 结果表明， 本方法明显优于传统的均值滤波和小波去噪
方法， 为强背景噪声下弱信号的检测提供 了新的途径．-: As many of the traditional de-noising method in case of strong background noise, the ability to extract the voice signal even weaker failure, the application of independent component analysis (ICA) method of voice signal feature extraction, and prove that this transformation can be enhanced voice ICA and music of super-Gaussian signals. On this basis, the application of ICA basis function as a filter, through the threshold of the denoising method of Gaussian background noise contains strong voice signal denoising simulation. The results show that this method is obviously superior to the traditional mean filtering and wavelet denoising methods for the strong background noise under the weak signal detection provides a new way. Platform: |
Size: 212992 |
Author:金振东 |
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Description: 噪声背景下的语音端点检测和语音增强算法，在信噪比很低的强航空噪声背景下，-aviation noise background voice endpoint detection and voice enhancement algorithms, and very low signal to noise ratio in the strong aviation background noise, particularly effective Platform: |
Size: 3072 |
Author:杨开开 |
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Description: 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 algorithm is proposed based on the algorithm the hardware
Design, describes the design principles of each module. The program increased the Voice Activity Detection feature, which allows voice signals to detect the noise frame to improve the characteristic parameters
The number of reliability. Platform: |
Size: 749568 |
Author:于高 |
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Description: 小波处理+源代码+实践步骤，初学者可以看看哈-aviation noise background voice endpoint detection and voice enhancement algorithms, and very low signal to noise ratio in the strong aviation background noise, particularly effective Platform: |
Size: 229376 |
Author:解昆 |
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Description: 语音增强是影响语音识别系统性能的重要成分。为了比较语音增强算法的性能，采用Matlab软件进行了数值仿真，对不同噪声环境下的语音用3种不同的方法进行降噪，采用信噪比、端点检测等方法来降噪效果，并对几种增强算法的性能进行了比较分析。结果表明，在变噪声环境下短时谱MMSE法最佳，谱减法和维纳滤波法各有优点。-Speech enhancement of voice recognition is an important component of system performance. In order to compare the performance of speech enhancement algorithm using the Matlab software, a numerical simulation, speech under different noise environments with 3 different methods of noise reduction, the use of signal to noise ratio, endpoint detection method to the noise reduction effect, and a few kinds of enhanced performance of the algorithm were compared. The results show that changing the noise environment in the MMSE method was the best short-term spectrum, spectral subtraction and Wiener filtering methods have their advantages. Platform: |
Size: 376832 |
Author:static |
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Description: 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 signal is acquired in noisy environments. The
proposed VAD method uses an additional stage of wavelet
subband denoising. We compared this approach with other
standard methods i.e.: zero-crossing rate and spectral
entropy analysis. Additionally we present in this paper our
basic results illustrating the main aim of this contribution,
consisting in application of intelligent denoising strategies
to various VAD algorithms. Platform: |
Size: 276480 |
Author:Tomasz |
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Description: 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 activity detection (VAD) in low SNR (10 dB) accuracy under proposed based on short-time fractal dimension of the improved algorithm. Combined with the characteristics of the speech signal, to 2 commonly used fractal dimension of speech signals are compared and calculated choice to follow the same dynamic endpoint threshold adaptive detection of voice. The results showed that: 6 ~ 10 dB for the signal to noise ratio of noisy speech, this method can detect the entire speech, but has some noise robustness, the system can be adaptively adjusted during operation to adapt to environmental noise threshold of changes to improve the accuracy of VAD algorithms. This is the source matlab. Platform: |
Size: 79872 |
Author:liuhongfu |
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Description: 使用能量特征、过零率特征设计一个语音检测算法。要求能在普通的实验室噪声环境下，准确地检测出语音信号的起终点位置-Use of energy characteristics, design features a zero-rate voice detection algorithm. Required in an ordinary lab noise environment, accurately detect the location of the voice signal from the end Platform: |
Size: 1024 |
Author:王涛 |
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Description: 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 describes a low computational-intensive software AGC for use in
handheld devices. The AGC provides options for static and dynamic noise floor estimation in a VAD
module. Further, this paper describes analog and digital gain adjustment with gain curve selection to allow
for distance perception during the AGC operation.
Platform: |
Size: 724992 |
Author:azza |
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Description: Electronic industry now growing towards the automation of everything, It includes development of Touch screen Interface Voice Recognition etc. These are leads to a world in which almost all electronic systems are capable of working in response with human emotions and gestures. One major aspect to this growth is the detection or identification of human voice so that the system can response based on voice inputs. The application of such system involves in most of the day to day electronic devices as well as industry and computer. Voice recognition is the process of automatically recognizing what is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker‟ s voice control various operations. Platform: |
Size: 1161216 |
Author:pkmohamad |
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Description: 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
rarely obtained in ‘clean’ acoustic environments in the real world. As a result, the
parameter estimation algorithms we employ must be robust to environmental factors
such as additive noise and reverberation. In this work we derive and evaluate approximate Bayesian algorithms for the following speech processing tasks: 1) speech
enhancement 2) speaker identification 3) speaker verification and 4) voice activity
detection. Platform: |
Size: 1728512 |
Author:an mchol |
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Description: 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 output voice activity detection for noise reduction in hearing aids matlab code Platform: |
Size: 5120 |
Author:wuchao |
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Description: 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 detection directed by noise classification, J Saeedi, SM Ahadi, K Faez
Signal, Image and Video Processing, 1-12
the folder is also contained the following
* different noise models for svm
* different sub_functions.
* three speech signal TIMIT dataset and their vad labels
Note that you need to download noise dataset from
http://spib.rice.edu/spib/select.
and libsvm toolbox from
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
It should be mentioned that both speech and noise should be sampled at 8 KHz.
Jamal Saeedi
Amirkabir University of Technology
Electrical Engineering Department-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 detection directed by noise classification, J Saeedi, SM Ahadi, K Faez
Signal, Image and Video Processing, 1-12
the folder is also contained the following
* different noise models for svm
* different sub_functions.
* three speech signal TIMIT dataset and their vad labels
Note that you need to download noise dataset from
http://spib.rice.edu/spib/select.
and libsvm toolbox from
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
It should be mentioned that both speech and noise should be sampled at 8 KHz.
Jamal Saeedi
Amirkabir University of Technology
Electrical Engineering Department Platform: |
Size: 1024 |
Author:Ilya |
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Search - NOISE DETECTION IN VOICE