chirplet_analasis

基于MATLAB7．0软件的实时数据交换的实现(MATLAB7.0 software-based real-time data exchange to achieve)

（附带有文档，文档中有方案，有程序有结果，设计完后对一段语音进行处理）给定滤波器的规一化性能指标（参考指标,实际中依据每个同学所叠加噪声情况而定）通带截止频率wp=0.25*pi。 采用窗函数法设计低通、高通、带通型FIR滤波器，对叠加噪声前后的语音信号进行滤波处理，绘出滤波器的频域响应，绘出滤波后信号的时域波形和频谱，并对滤波前后的信号进行对比，分析信号的变化；在相同的性能指标下比较各方法的滤波效果，并从理论上进行分析((With the document, document, program, program results, the design after a voice) to set the filter rules of performance indicators (reference, practice, each student adding noise may be) passband cutoff frequency wp = 0.25* pi. Window function method to design low pass, high pass, band-pass FIR filter, filtering the voice signal before and after adding noise, draw the filter frequency response plot the time-domain waveform and spectrum of the filtered signal, and filtering the signal before and after comparison and analysis of signal changes the filtering effect of the various methods in the same performance indicators, and the theoretical analysis)

自相关函数的应用，进行基音检测，在MATLAB下进行仿真(Application of autocorrelation function for pitch detection, carried out in the MATLAB simulation)

基于vc++开发，dtw语音识别,MFCC参数(DTW recognice based on vc++)

speech dereverberation using homomorphic processing

hmm 说话人识别 matlab 算法，c++算法，(speaker recognition hmm)

matlab在语音信号处理中的使用，包括基频提取，语音识别，语音增强等(The application of MATLAB in speech signal processing including pitch extraction, speech recognition, speech enhancement and so on.It uses matlab.)

一个有效的，可以直接运行的HMM—GMM代码，而且带有语音数据。不存在任何障碍，值得初学者下载的浅显易懂的例子。(An effective, the HMM-GMM code that can be run directly, and with the voice data. There are no obstacles, beginners should download easy to understand examples.)

一个语音分析程序，包含gammatone滤波器组的分解和重组。对学习语音分析的很有帮助(A speech analysis program contains the gammatone filter group decomposition and reorganization. Helpful learning speech analysis)

Chapter 2 Low Bit-Rate Speech Coding In this chapter an overview is given of speech coding techniques at several bit rates. Most of them use Linear Prediction. This overview is not meant to be complete its purpose is to make the reader somewhat familiar with Linear Predictive Coding which is necessary for a proper understanding of later chapters. Section 2.1 treats the subject of quantisation and coding. In section 2.2 a description of speech production and speech sounds is given. Coders based on linear prediction can be considered as being based on a simple speech production model. This model is explained in section 2.3. Section 2.4 describes various speech coding algorithms and techniques. Section 2.5 briefly describes some measures for the quality of coded speech.(In this chapter an overview is given of speech coding techniques at several bit rates. Most of them use Linear Prediction. This overview is not meant to be complete its purpose is to make the reader somewhat familiar with Linear Predictive Coding which is necessary for a proper understanding of later chapters. Section 2.1 treats the subject of quantisation and coding. In section 2.2 a description of speech production and speech sounds is given. Coders based on linear prediction can be considered as being based on a simple speech production model. This model is explained in section 2.3. Section 2.4 describes various speech coding algorithms and techniques. Section 2.5 briefly describes some measures for the quality of coded speech.)