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Chapter 17 added
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‎.DS_Store‎

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‎Additional Scripts/testing.py‎

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‎Ch. 16 - Algorithmic Reverb Effects/apf.py‎

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# APF
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# This function creates an all-pass filter by processing an individual
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# input sample and updating a delay buffer used in a loop to index each
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# sample in a signal.
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#
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# Input Variables
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# n: current sample number of the input signal
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# delay: samples of delay
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# gain: feedback gain (linear scale)
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# amp: amplitude of LFO modulation
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# rate: frequency of LFO modulation
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import numpy as np
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def apf(x, buffer, Fs, n, delay, gain, amp, rate):
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# Calculate time in seconds for the current sample
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t = n/Fs
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fracDelay = amp * np.sin(2 * np.pi * rate * t)
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intDelay = int(np.floor(fracDelay))
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frac = fracDelay - intDelay
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# Determine indexes for circular buffer
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M = len(buffer)
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indexC = int(np.mod(n, M)) # Current index
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indexD = int(np.mod((n-delay+intDelay), M)) # Delay index
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indexF = int(np.mod((n-delay+intDelay+1), M)) # Fractional index
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# Temp variable for output of delay buffer
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w = (1 - frac) * buffer[indexD] + frac * buffer[indexF]
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# Temp variable used for the node after the input sum
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v = x + (-gain * w)
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# Summation at output
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out = (gain * v) + w
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# Store the current input to delay buffer
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buffer[indexC] = v
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return out, buffer

‎Ch. 16 - Algorithmic Reverb Effects/apfExample.py‎

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# APFEXAMPLE
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# This script uses an all-pass filter function applied to an acoustic
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# guitar recording.
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#
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# See also APF
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import numpy as np
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import soundfile
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from apf import apf
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from IPython.display import Audio
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x, Fs = soundfile.read('AcGtr.wav')
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maxDelay = int(np.ceil(0.05 * Fs)) # maximum delay of 50ms
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buffer = np.zeros(maxDelay)
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d = np.ceil(0.042 * Fs) # 42ms of delay
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g = 0.9
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rate = 0.9 # Hz (frequency of LFO)
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amp = 6 # Range of +/- 6 samples for delay
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# Initialize output signal
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N = len(x)
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out = np.zeros(N)
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for n in range(N):
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# Use apf function
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out[n], buffer = apf(x[n], buffer, Fs, n, d, g, amp, rate)
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Audio(out, rate=Fs)

‎Ch. 16 - Algorithmic Reverb Effects/crossoverFeedback.py‎

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# CROSSOVERFEEDBACK
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# This script implements two comb filters with crossover feedback.
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#
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# See also MODDELAY
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import numpy as np
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import matplotlib.pyplot as plt
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from modDelay import modDelay
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from IPython.display import Audio
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Fs = 48000
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x = np.append(1, np.zeros(int(Fs*0.5))) # Add zero-padding for reverb tail
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# Max delay of 70ms
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maxDelay = int(np.ceil(0.07 * Fs))
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# Initialize all buffers
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buffer1 = np.zeros(maxDelay)
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buffer2 = np.zeros(maxDelay)
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# Early reflections tapped delay line
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bufferER = np.zeros(maxDelay)
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# Delay and gain parameters
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d1 = np.fix(0.0297 * Fs)
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g11 = -0.75
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g12 = -0.75
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d2 = np.fix(0.0419 * Fs)
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g21 = -0.75
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g22 = -0.75
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# LFO parameters
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rate1 = 0.6
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amp1 = 3
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rate2 = 0.71
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amp2 = 3
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# Initialize output signal
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N = len(x)
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out = np.zeros(N)
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fb1 = 0
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fb2 = 0 # feedback holding variables
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for n in range(N):
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# Combine input with feedback for respective delay lines
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xDL1 = x[n] + fb1
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xDL2 = x[n] + fb2
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# Two parallel delay lines
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outDL1, buffer1 = modDelay(xDL1, buffer1, Fs, n, d1, amp1, rate1)
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outDL2, buffer2 = modDelay(xDL2, buffer2, Fs, n, d2, amp2, rate2)
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# Combine parallel paths
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out[n] = 0.5 * (outDL1 + outDL2)
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# Calculate feedback (including crossover)
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fb1 = 0.5 * (g11 * outDL1 + g21 * outDL2)
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fb2 = 0.5 * (g12 * outDL2 + g22 * outDL2)
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plt.plot(out)
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plt.show()
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Audio(out, rate=Fs)

‎Ch. 16 - Algorithmic Reverb Effects/earlyReflections.py‎

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# EARLYREFLECTIONS
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# This function creates a tapped delay line to be used for the early
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# reflections of a reverb algorithm. THe delays and gains of the taps
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# are included in this function and were based on an IR measurement from a
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# recording studio in Nashville, TN.
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#
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# Also see MOORERREVERB
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import numpy as np
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def earlyReflections(x, buffer, Fs, n):
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# Delay times converted from milliseconds
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delayTimes = [np.fix(0*Fs), np.fix(0.01277*Fs), np.fix(0.01283*Fs), np.fix(0.01293*Fs), np.fix(0.01333*Fs),
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np.fix(0.01566*Fs), np.fix(0.02404*Fs), np.fix(0.02679*Fs), np.fix(0.02731*Fs), np.fix(0.02737*Fs), np.fix(0.02914*Fs),
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np.fix(0.02920*Fs), np.fix(0.02981*Fs), np.fix(0.03389*Fs), np.fix(0.04518*Fs), np.fix(0.04522*Fs), np.fix(0.04527*Fs),
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np.fix(0.05452*Fs), np.fix(0.06958*Fs)]
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numDelays = len(delayTimes)
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for delay in range(numDelays):
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delayTimes[delay] = int(delayTimes[delay])
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# There must be a 'gain' for each of the 'delayTimes'
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gains = [1, 0.1526, -0.4097, 0.2984, 0.1553, 0.1442,
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-0.3124, -0.4176, -0.9391, 0.6926, -0.5787, 0.5782,
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0.4206, 0.3958, 0.3450, -0.5361, 0.417, 0.1948, 0.1548]
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# Determine indexes for circular buffer
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M = len(buffer)
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indexC = np.mod(n, M) # current index
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buffer[indexC] = x
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out = 0 # initialize the output to be used in loop
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# Loop through all the taps
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for tap in range(len(delayTimes)):
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# Find the circular buffer index for the current tap
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indexTDL = np.mod(n-delayTimes[tap], M)
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# 'Tap' the delay line and add current tap with output
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out = out + gains[tap] * buffer[indexTDL]
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return out, buffer

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