Guidance for importing a .wav audio file into MATLAB for digital filter simulation.

 Dear All,

 

I hope this message finds you well. I am currently working with Altera FPGA Cyclone IV and aim to implement a FIR filter for the ADC data originating from a microphone. Quartus provides an IP core (FIR II) for this purpose, and I intend to implement the coefficients generated with Matlab FilterDesigner.

 

More specifically I record for 2 seconds the sound from a microphone but when I choose to play the recording it has a lot of noise.

 

I would like to inquire if it's possible to import a .wav audio file into MATLAB for simulation and testing. I aim to fine-tune the filter and subsequently proceed to FPGA. Could you please guide me on the necessary steps to import the audio file, pass it through the digital filter, and observe the results in MATLAB?"

 

 NOTE:-

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Step 1: Import the .wav Audio File

 

MATLAB's audioread function is perfect for importing audio files. You can use it to read your recorded .wav file as follows:

 

 

[signal, fs] = audioread('your_audio_file.wav');

• signal contains the imported audio data.
• fs is the sampling frequency of the audio file.

Step 2: Design Your FIR Filter

 

you can apply the filter to your audio signal using the filter function. If you have the Digital Signal Processing Toolbox, you might also explore designfilt and filtfilt for more advanced filter design and zero-phase filtering.

 

Assuming you have a set of coefficients named b (the numerator coefficients of the filter), you can apply your filter like this:

 

 

filtered_signal = filter(b, 1, signal);

 

Step 3: Play and Compare the Audio

 

After filtering, you might want to listen to the original and filtered audio to compare them. Use MATLAB's sound function to play back audio directly from MATLAB.

 

To play the original audio:

 

sound(signal, fs);

To play the filtered audio:

sound(filtered_signal, fs);

Step 4: Visualize the Results

 

Plotting the signals in the time domain or their frequency spectra can be very useful.

 

subplot(2,1,1);

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