The sampling frequency of your audio file is 48000 Hz, which means that the maximum frequency represented in your audio file is 24000 Hz. If you recall from signals and systems, the maximum frequency that is represented in our signal is the sampling frequency divided by 2.
Denoise matlab code#
The code that will look the most frightening is the code above. Y = fft(f(:,1), N) / N % For normalizing, but not needed for our analysis I'm just plotting each channel individually in each subplot. The above code is quite straight forward. The above code produces the plot shown below: I won't get into it here, but you can read about how subplot works in detail by referencing this StackOverflow post I wrote here. subplot is a way to place multiple figures in the same window. Each point in time has a circle drawn at the point with a vertical line drawn from the horizontal axis to that point in time. Stem is a way to plot discrete points in MATLAB. N = size(f,1) % Determine total number of samples in audio file You then use ay to play the file in MATLAB so you can hear it. This step will allow you to create an audioplayer object that takes the signal you read in ( f), with the sampling frequency fs and outputs an object stored in pOrig. In general, the total number of channels in your audio file is denoted by the total number of columns in this matrix read in through audioread. The first column is the left channel while the second is the right channel. f would be the signal read into MATLAB while fs is the sampling frequency of your signal.
Denoise matlab windows#
It closes all of our windows (if any are open), and clears all of our variables in the MATLAB workspace. clearvars, close all just do clean up for us. Also, make sure you set your working directory to be where this file is being stored. Just specify what file you want within the ''. Filtered the signal then played it by constructing another audioplayer object.Īudioread will read in an audio file for you.Designed a bandpass filter that cuts off these frequencies.
![denoise matlab denoise matlab](https://www.mathworks.com/help/examples/wavelet/win64/DenoiseTimetableUsingBlockthresholdingExample_01.png)
![denoise matlab denoise matlab](https://americawakeup.net/pictures/discrete-wavelet-transform-documentation-for-matlab-4.jpg)
![denoise matlab denoise matlab](https://au.mathworks.com/help/examples/deeplearning_shared/win64/DenoiseSpeechUsingDeepLearningNetworksExample_02.png)
As such, we can apply a bandpass filter to get rid of the low noise, capture most of the voice, and any noisy frequencies on the higher side will get cancelled as well. This resides in the low frequency range of the spectrum, whereas the voice has a more higher frequency. What I was talking about with regards to the frequency spectrum is that if you hear the sound, the background noise has a very low hum. This is a pretty imperfect solution, especially since some of the noise is embedded in the same frequency range as the voice you hear on the file, but here goes nothing.