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chirplet_analasis
针对信号自适应chirplet分解未知参数多、实现起来比较困难的特点，文献[1]提出了一种新的chirplet分解快速算法。该算法利用计算信号的二次相位函数，得到其能量分布集中于信号的调频率曲线上的结论，此时通过谱峰检测可同时获得chirplet调频率、时间中心和幅度的估计；然后通过解线性调频技术获得其初始频率和宽度的估计，仿真结果验证了本文算法的有效性。(this code includs one method for chirplet analasis,that is used in voice and ultra-wave analasys,etc.)
- 2009-03-09 10:23:45下载
- 积分：1
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SpecSubTD_Boll79
谱减法自适应滤波以及听觉掩蔽效应00865833(Spectral subtraction
)
- 2012-02-23 16:51:52下载
- 积分：1
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402864
语音库开发工具源码，例程程序创建机读SAPI.SpVoice对象开发语音库。(Speech development tools source , routine program to create machine-readable SAPI.SpVoice speech object development .)
- 2015-07-07 22:35:17下载
- 积分：1
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Speech-Signal-Processing
语音信号处理c语言程序-音高、加窗、MFCC、PLP、等(C programming language speech signal processing- pitch, add window, MFCC, PLP, etc)
- 2015-11-17 21:15:48下载
- 积分：1
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speaker_recognition
说话人识别系统,界面友好，可以对实时录音的说话人进行识别(Speaker recognition system, user-friendly, real-time recording can be carried out to identify the speaker)
- 2021-04-20 14:38:50下载
- 积分：1
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lms
基于lms的改进型谱减法，降低噪声效果好(Improved LMS-based spectral subtraction, noise reduction effect)
- 2008-05-07 13:30:14下载
- 积分：1
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BBS
基于FastICA相结合实现的语音信号的采集、随机混合，再通过盲分离将混合后的语音信号分离(Speech signal acquisition and random mixing based on FastICA, and then the mixed speech signal is separated by blind separation.)
- 2020-06-25 12:20:02下载
- 积分：1
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speech-enhancement
本资料涵盖了几乎所有的语音增强方面的方法，主要有谱减法，听觉掩蔽，最小均方误差，维纳滤波以及一些非主流的方法，这些对于研究语音增强的人来说是很有帮助的(The data cover almost all aspects of speech enhancement methods, the main spectral subtraction, auditory masking, minimum mean square error, Wiener filtering as well as some non-mainstream approach, which for the study of speech enhancement is helpful for people who)
- 2021-05-13 09:30:03下载
- 积分：1
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ABSE
熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)
- 2020-11-02 21:29:54下载
- 积分：1
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000971
语音识别，实现语音的识别功能，，特定人的语音识别。识别0~9的发音(Speech recognition, voice recognition function, a particular person' s speech recognition. Identify the pronunciation of 0 to 9)
- 2011-05-13 11:01:54下载
- 积分：1
