Signal Sampling and quantification

There are two major tasks for signal processing. One is signal analysis, including time domain and frequency domain. The other is filter design, including fir and IIR.

To represent continuous analog signals in MATLAB, T = 0: DT: TF is generally used to represent time points. Although vertices in Matlab are discrete, As long as dT is small enough, such as 0.001, it can approximate the continuous time.

The digital signal is represented by x (n) = x (t), t = Nt, n = 0: TF/T as the time point, and T is the sampling period.

Dt = 0.001;

TF = 6;

T = 0: DT: TF;

XA = SQRT (t) + cos (t );

T = 0.5;

N = 0: TF/T;

XB = SQRT (N * t) + cos (N * t );

MATLAB quantifies the data by converting X into the quantified value XQ: XQ = round (x/deltax) * deltax.

Here, deltax is the quantitative step, and round is the rounded integer. In addition, the integer method also obtains the rounded up Ceil, the rounded down floor, and the rounded fix to zero.

The larger the number of quantization digits, the smaller the quantization step, the closer it is to the real value.

The characteristics of this mean and quantization are big numbers, small quantization error, and large Quantization Error for small numbers. Therefore, nonlinear quantifiers are introduced in some cases.

On your computer, you can collect sound signals through recording and import them to Matlab.

Save the wav file to the path and enter [x, FS, N] = wavread ('dajiahao ');

Then, you can use save dajiahao X to generate the mat data file, which is convenient for processing by the MATLAB tool. Each number is saved in double-precision format, which occupies 64 bits in eight bytes.

Signal Sampling and quantification