搜索资源列表
uvsegment
- 用信息熵进行语音信号声韵分割，尤其适合低信噪比的语音。-with information entropy voice signal eloquence and segmentation, especially for low SNR voice.
vad
- 语音信号的端点检测matlab程序，利用短时平均能量和短时平均过零率判断语音端点的起始。还有语音信号信噪比的计算程序。
fenxing
- 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
lq
- 语音信号的数字滤波处理 加入高斯噪声再滤除,信噪比小于20分贝-Voice signal digital filter handle add further filtered Gaussian noise, signal to noise ratio is less than 20 dB
test
- 自己编写的语音信号的采集，fft变换（两种），以及信噪比的计算！希望对大家有所帮助！-I have written the speech signal acquisition, fft transform (two kinds), and the calculation of signal to noise ratio! We want to help!
signal_noise_SNR
- 介绍对语音信号叠加白噪声或指定的噪声，满足一定的信噪比，又提供了检验带噪语音信噪比的函数。-Introduction of the voice signal superimposed noise, white noise or designated to meet a certain signal to noise ratio, but also provides a test signal to noise ratio of Noisy Speech function.
endpoint_detection_with_noise
- 提出了一种基于时频方差和的语音端点检测算法。实验证明该算法能够在低信噪比的情况下,准确地检测出语音信号-Proposed based on time-frequency variance and Speech Endpoint Detection Algorithm. Experiments show that the algorithm at low SNR cases, accurately detect the speech signal
snr
- 计算语音、音频信号的信噪比程序（包含音频样本文件）-Calculation of voice, audio program signal to noise ratio (including the audio sample file)
11
- 为提高语音端点检测系统在低信噪(0 dB 以下) 下 检测的准确率, 提出了一种基于谱熵的端点检测算法。将每 帧信号分为16 个子带, 选取频谱分布在250～ 3. 5 kHz 并且 能量不超过该帧总能量90 的子带, 计算经过语音增强后的 子带能量以及各子带信噪比, 根据各子带信噪比的不同调整 其在整个谱熵计算过程中的权重, 然后平滑谱熵, 以最终的 谱熵作为端点检测的依据-To improve endpoint detection system in the low
7102
- 基于匹配滤波器的语音识别。本程序接触匹配滤波对加噪语音进行匹配输出，使得信号的信噪比得到提升，送入后端DTW的语音识别系统，实验证明，本程序对语音提升有很大的帮助-A speech recoginiton base on the matched filter. this code will use matched filter and get speech SNR output enhanced. then we will extrace MFCC, send the feature to th
double_threshold
- 传统的语音信号双门限端点检测算法，适用于高信噪比，低信噪比不适用。(The traditional double threshold detection algorithm for speech signal is suitable for high signal to noise ratio and low signal to noise ratio.)