Recording sound from Microphone

MATLAB command "wavrecord" to read the audio signals from the microphone directly. The command format is

y = wavrecord(n, fs);

fs=16000; % Sampling rate

duration=2; % Recording duration

fprintf('Press any key to start  %g seconds of recording...', duration); pause

fprintf('Recording...');

y=wavrecord(duration*fs, fs);  % duration*fs is the total number of sample points

fprintf('Finished recording.\n');

 fprintf('Press any key to play the recording...'); pause;

fprintf('\n');

wavplay(y,fs);

Band Pass Filter

There are applications where a particular band, or spread, or frequencies need to be filtered from a wider range of mixed signals. Filter circuits can be designed to accomplish this task by combining the properties of low-pass and high-pass into a single filter. The result is called a band-pass filter. Creating a bandpass filter from a low-pass and high-pass filter can be illustrated using block diagrams:

The main function of such a filter in a transmitter is to limit the bandwidth of the output signal to the minimum necessary to convey data at the desired speed and in the desired form. In a receiver, a band-pass filter allows signals within a selected range of frequencies to be heard or decoded, while preventing signals at unwanted frequencies from getting through.

There are basically two types of bandpass filters wide bandpass and narrow bandpass filters.

Digital Filtering in Matlab

Digital filtering is a widely used technique that is common in many fields of science and engineering.  Filters remove unwanted signals and noise from a desired signal.  There are many different kinds of filters, including low pass, high pass, band pass and band stop filters.  There is a large collection of filters that famous engineers and mathematicians have invented, including Hanning, Hamming, Blackman, Kaiser and Tukey windows.


y = filter(b,a,x) filters the input data, x, using a rational transfer function defined by the numerator and denominator coefficients b and a, respectively.
If a(1) is not equal to 1, then filter normalizes the filter coefficients by a(1). Therefore, a(1) must be nonzero.

y - Filtered data, returned as a vector, matrix, or multidimensional array of the same size as the input data, x.

• If x is a vector, then filter returns the filtered data as a vector of the same size as x.
• If x is a matrix, then filter acts along the first dimension and returns the filtered data for each column.
• If x is a multidimensional array, then filter acts along the first array dimension whose size does not equal 1. 

* 'low' : Low-pass filters, which remove frequencies greater than some specified value.
* 'high' : High-pass filters, which remove frequencies lower than some specified value.
* 'stop' : Stop-band filters, which remove frequencies in a given range of values.

Signal Normalization

Normalization is basically bringing the two signals to the same range or a predefined range. It is to do with removing scale factor effect. Thus we may rescale to normalize amplitude dynamic range to swing between +1/-1 or power to be unity or phase to be zero. A typical example of a predefined range is the statistical perception of the normalization, which is transforming the signal so that its mean is 0 and standard deviation is 1. After such transform the canonical form is obtained. Transforming all signals to such canonical form eases and robustifies the process of comparisons as well as serving too different needs such as visualization, and analysis.


when comparing two different signals that mean two different things (but equally important or with an equal significance regarding the information the signals carry), would you expect to be comparing two signals, one with values in the ballpark of ±1 and the other with no samples with magnitude at least 2−8 or so?

Normalization means that you're not comparing an elephant to a bug at least not with regard to mass. unless you make the bug look as big as the elephant, and then you start comparing what makes the bug different than the elephant.