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.
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
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
Hilbert
- 基于labview8.6的希尔伯特变换高精度测量低信噪比条件下两路正弦波相位差-Labview8.6 based Hilbert transform high-precision measurement of the sine wave phase difference of the two low SNR
fig3_5
- when SNR is high , 1st sample estimator provides good estimate of A. it has no ise effect . so we don’t need averaging effect to reduce noise when SNR is high. However variance is still low in sample mean estimator which shows that in reality ,samp
Digital-phase-sensitive-detector
- 基于labview8.6的数字相敏检波算法源码，高精度实现测量低信噪比条件下正弦波幅值和初相的测量-Based labview8.6 digital phase-sensitive detection algorithm source code, to achieve high-precision measurement of the amplitude and the initial phase of the sine wave measured under low SNR
An
- 语音端点检测，一种基于MFCC的余弦值单门限值检测方法，适用于低SNR环境(Speech endpoint detection, a single threshold value detection method based on the cosine value of MFCC, suitable for low SNR environment)