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HMM_matlab

于 2020-12-10 发布 文件大小:2983KB
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代码说明：

  这是基于隐马尔可夫模型的连续语音识别代码，不同于dtw,这是个完整的工程，我把需要的voicebox也放进去了，一共用到9个子函数，包括模板的训练算法何识别算法，是完全可以使用的。(This is based on the hidden Markov model of continuous voice recognition code, different the dtw, which is a complete project, I need to put the voicebox into a total of nine sub-functions, including the template training algorithm to identify the algorithm, Is fully available.)

文件列表：

HMM_matlab
..........\0a.wav,29096,2001-10-21
..........\0b.wav,29640,2001-10-21
..........\1a.wav,33832,2001-10-21
..........\1b.wav,31800,2001-10-21
..........\2a.wav,32840,2001-10-21
..........\2b.wav,37224,2001-10-21
..........\3a.wav,38472,2001-10-21
..........\3b.wav,32664,2001-10-21
..........\4a.wav,37144,2001-10-21
..........\4b.wav,38264,2001-10-21
..........\5a.wav,34408,2001-10-21
..........\5b.wav,37144,2001-10-21
..........\6a.wav,36552,2001-10-21
..........\6b.wav,35624,2001-10-21
..........\7a.wav,36280,2001-10-21
..........\7b.wav,33576,2001-10-21
..........\8a.wav,21624,2001-10-21
..........\8b.wav,21608,2001-10-21
..........\9a.wav,25192,2001-10-21
..........\9b.wav,27192,2001-10-21
..........\baum.m,1919,2017-04-25
..........\getparam.m,2281,2017-04-25
..........\hmm.mat,63856,2001-11-12
..........\inithmm.m,1594,2017-04-25
..........\kmeans.m,2001,2017-04-25
..........\main.m,483,2017-04-25
..........\mcut.fig,18312,2001-10-21
..........\MCUT.m,3763,2001-10-21
..........\mfcc.m,1100,2017-04-25
..........\mixture.m,411,2017-04-25
..........\MKEY.M,2412,2001-10-21
..........\MMOUSE.M,2403,2001-10-21
..........\pdf.m,230,2001-11-10
..........\readwave.m,355,2001-10-21
..........\recog.m,300,2017-04-25
..........\samples.mat,630000,2001-11-12
..........\train.m,1005,2017-04-25
..........\UNTITLED.FIG,16552,2001-10-20
..........\UNTITLED.M,4841,2001-10-20
..........\vad.m,1896,2017-04-25
..........\viterbi.m,1053,2017-04-25
..........\voicebox
..........\........\activlev.m,16679,2017-04-14
..........\........\activlevg.m,8389,2017-04-14
..........\........\atan2sc.m,1793,2017-04-14
..........\........\axisenlarge.m,2112,2017-04-14
..........\........\bark2frq.m,4728,2017-04-14
..........\........\berk2prob.m,1705,2017-04-14
..........\........\bitsprec.m,3305,2017-04-14
..........\........\cblabel.m,2469,2017-04-14
..........\........\ccwarpf.m,2013,2017-04-14
..........\........\cent2frq.m,2046,2017-04-14
..........\........\cep2pow.m,2131,2017-04-14
..........\........\choosenk.m,2239,2017-04-14
..........\........\choosrnk.m,1603,2017-04-14
..........\........\Contents.m,14643,2017-04-14
..........\........\correlogram.m,3975,2017-04-14
..........\........\distchar.m,3819,2017-04-14
..........\........\distchpf.m,3422,2017-04-14
..........\........\disteusq.m,3688,2017-04-14
..........\........\distisar.m,4641,2017-04-14
..........\........\distispf.m,3736,2017-04-14
..........\........\distitar.m,4170,2017-04-14
..........\........\distitpf.m,3601,2017-04-14
..........\........\ditherq.m,1982,2017-04-14
..........\........\dlyapsq.m,2756,2017-04-14
..........\........\dualdiag.m,3479,2017-04-14
..........\........\dypsa.m,26494,2017-04-14
..........\........\enframe.m,6637,2017-04-14
..........\........\entropy.m,3906,2017-04-14
..........\........\erb2frq.m,3430,2017-04-14
..........\........\estnoiseg.m,7306,2017-04-14
..........\........\estnoisem.m,16236,2017-04-14
..........\........\ewgrpdel.m,2461,2017-04-14
..........\........\fig2emf.m,4127,2017-04-14
..........\........\figbolden.m,4593,2017-04-14
..........\........\filtbankm.m,15242,2017-04-14
..........\........\filterbank.m,3507,2017-04-14
..........\........\finishat.m,4876,2017-04-14
..........\........\flac.exe,262144,2017-04-14
..........\........\fopenmkd.m,2254,2017-04-14
..........\........\frac2bin.m,2483,2017-04-14
..........\........\fram2wav.m,5259,2017-04-14
..........\........\frq2bark.m,6116,2017-04-14
..........\........\frq2cent.m,2072,2017-04-14
..........\........\frq2erb.m,3305,2017-04-14
..........\........\frq2mel.m,2796,2017-04-14
..........\........\frq2midi.m,2082,2017-04-14
..........\........\fxpefac.m,16582,2017-04-14
..........\........\fxrapt.m,17319,2017-04-14
..........\........\gammabank.m,9992,2017-04-14
..........\........\gausprod.m,6728,2017-04-14
..........\........\gaussmix.m,25016,2017-04-14
..........\........\gaussmixd.m,8199,2017-04-14
..........\........\gaussmixg.m,17585,2017-04-14
..........\........\gaussmixk.m,6625,2017-04-14
..........\........\gaussmixm.m,6481,2017-04-14
..........\........\gaussmixp.m,11941,2017-04-14
..........\........\gaussmixt.m,11418,2017-04-14

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    SFS提供了计算语音本质的一系列工具。包括支持各种数据格式，信号处理和图形，频谱、共振峰分析，基频提取等等。在线文档说明请访问http://www.phon.ucl.ac.uk/resource/sfs/help/(SFS provides a voice calculated nature of a series of tools. Including support for various data formats, signal processing and graphics, spectrum, formant analysis, frequency, etc. extraction. Online document shows, please visit http :// www.phon.ucl.ac.uk/resource/sfs/help/)
    2007-04-03 14:54:52下载
    积分：1
  • 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.)
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    计算声音信号的总声压级，1/3/倍频程声压级，总响度，特性响度，尖锐度(Calculation of the total sound pressure level of the voice signal, 1/3/octave sound pressure level, the total loudness, loudness characteristics, acute severe)
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    2012-04-17 22:18:00下载
    积分：1
  • 最小均方误差的信噪比和信干噪比 lmssnr
    一个用matlab来实现的最小均方误差的信噪比和信干噪比(Using matlab to achieve the minimum mean square error, signal to noise ratio and signal to interference noise ratio )
    2020-07-10 01:08:57下载
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    语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等 绝对保证质量，是保研后导师布置的一些基础程序(Short-time speech signal analysis, mainly including: sub-frame, short-time energy, short-term average, short-time zero-crossing rate, short-time auto-correlation function, short-term rate of poor cepstrum, complex cepstrum, lpc coefficients, lpc spectral estimation, such as an absolute guarantee that the quality of instructors is the security arrangement after the inquest some of the basis of procedures)
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