Implementation of Bark and ERB Filter Banks

Below is a sample Matlab source code implementation for Bark and ERB filter banks: The code begins by initializing key filter parameters including sampling rate, frequency range, and number of frequency bands. The GenerateERBBands function creates an Equivalent Rectangular Bandwidth (ERB) scale filter bank that approximates human auditory frequency resolution. % Initialize filter parameters fs = 44100; % Sampling rate (standard audio frequency) fmin = 20; % Minimum frequency in Hz (lower hearing threshold) fmax = 20000; % Maximum frequency in Hz (upper hearing threshold) numBands = 24; % Number of bands in filter bank (typical for auditory modeling) % Generate ERB filter bank - creates logarithmically spaced bands % based on psychoacoustic equivalent rectangular bandwidth erb_bands = GenerateERBBands(fmin, fmax, numBands); % Load audio file for processing filename = 'audio.wav'; [x, fs] = audioread(filename); % Reads audio data and actual sampling rate % Apply Bark and ERB filter bank % This function decomposes audio into frequency subbands using % both Bark scale (critical bandwidths) and ERB scale filters y = ApplyBarkERBFilterbank(x, fs, erb_bands); % Add your custom processing code here % (e.g., spectral modification, feature extraction, or signal analysis) % Play processed audio using amplitude scaling soundsc(y, fs); % Automatically scales audio to prevent clipping Please note that this example code is for demonstration purposes only. You can modify and extend it according to your specific requirements. The implementation follows psychoacoustic principles where ERB scales provide better frequency resolution matching human auditory perception compared to linear frequency scales. The filter bank effectively decomposes audio signals into frequency components that correspond to how the human ear processes sound.