Hi,
I understand that you’re trying to use the melfb function in your speaker recognition project, and you're seeking an explanation of how the code generates mel filter banks from FFT bins.
I assume that you're familiar with basic signal processing and the concept of mel-scale filtering, but need clarity on how the code translates mel-scale logic into filter bank computation using MATLAB.
In order to understand how this code computes mel-spaced triangular filter banks, you can follow the below explanation:
Step 1: Set up mel frequency conversion parameters
The mel scale approximates human hearing and is calculated as:
mel(f) = log(1 + f/700)
The variable f0 = 700/fs and lr = log(1 + 0.5/f0)/(p+1) convert frequency to mel-space and divide the mel scale evenly.
Step 2: Determine filter bank edges in FFT bins
The code computes boundaries for p filters across the spectrum:
- bl = n * (f0 * (exp([...]) - 1)) maps evenly spaced mel points back to linear frequency (in FFT bin numbers).
- b1, b2, b3, and b4 define the valid range of bins contributing to the filter bank.
Step 3: Compute triangular weights for filters
- pf converts bin indices to positions in mel space.
- fp gives the lower bin of the triangle for each frequency.
- pm gives the fractional part for linear interpolation.
- The vectors r, c, and v form the row indices, column indices, and values of the sparse matrix.
Step 4: Construct sparse filter bank matrix
- m = sparse(r, c, v, p, 1+fn2) builds a p × (n/2 + 1) matrix.
- Each row in m is a triangular filter centered at a mel-scaled frequency, used to convert FFT magnitudes to mel-filtered energy.
This matrix m is used to transform the FFT magnitude spectrum into mel filterbank energies, essential in computing MFCCs for speaker recognition.
Refer to the documentation of:
- "sparse": https://www.mathworks.com/help/matlab/ref/sparse.html
- "floor": https://www.mathworks.com/help/matlab/ref/floor.html
- "exp": https://www.mathworks.com/help/matlab/ref/exp.html
Hope this helps!
