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separate jupyter notebooks into individual scripts
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‎Ch. 11 - Echo Effects/convertSecSamples.py‎

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# CONVERTSECSAMPLES
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# This script provides two examples for converting a time delay in units of
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# seconds to samples and milliseconds to samples.
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#
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# See also CONVERTTEMPOSAMPLES
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import numpy as np
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# Example 1 - Seconds to samples
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Fs = 48000 # arbitrary sampling rate
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timeSec = 1.5 # arbitrary time in units of seconds
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# Convert to units of samples
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timeSamples = np.fix(timeSec * Fs) # round to nearest integer sample
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# Example 2 - Milliseconds to samples
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timeMS = 330 # arbitrary time in units of milliseconds
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# Convert to units of seconds
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timeSec = timeMS/1000
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# Convert to units of samples
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timeSamples = np.fix(timeSec * Fs) # round to nearest integer samples

‎Ch. 11 - Echo Effects/convertTempoSamples.py‎

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# CONVERTTEMPOSAMPLES
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# This script provides an example for calculating a delay time in units of
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# samples that will be synchronized with the tempo of a song in units of
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# beats per minutes (BPM).
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#
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# Assume a (4/4) time signature where BEAT = QUARTER NOTE
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#
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# See also CONVERTSECSAMPLES
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import numpy as np
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Fs = 48000
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beatsPerMin = 90
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beatsPerSec = beatsPerMin/60
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secPerBeat = 1/beatsPerSec
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noteDiv = 1
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timeSec = noteDiv * secPerBeat
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timeSamples = np.fix(timeSec * Fs)

‎Ch. 11 - Echo Effects/convolutionExample.py‎

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# CONVOLUTIONEXAMPLE
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# This script demonstrates the numpy convolution function - y = np.convolve(x, h)
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# The example demonstrated is with a single cycle of a sine wave. When the sine
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# wave is convolved with the impulse response for an echo effect, the output
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# signal has delayed copies of the sine wave at different amplitudes at
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# different times.
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#
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# See also CONV
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import soundfile
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import numpy as np
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import matplotlib.pyplot as plt
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# Import previously saved IR
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[h, Fs] = soundfile.read('impResp.wav')
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N = len(h)
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# Synthesize input signal
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f = 4
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t = np.arange(0, N*0.125)/Fs
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sinWave = np.sin(2 * np.pi * f * t)
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pad = np.zeros([int(N*0.875)])
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x = np.concatenate((sinWave, pad))
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# Perform convolution
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y = np.convolve(x, h)
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# Plot signals
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xAxis = np.arange(0, N)/Fs
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plt.subplot(3,1,1)
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plt.plot(xAxis, x)
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plt.axis([-0.1, 2, -1.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Input Signal - x[n]')
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plt.subplot(3,1,2)
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plt.stem(xAxis, h)
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plt.axis([-0.1, 2, -1.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Impulse Response')
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plt.subplot(3,1,3)
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plt.plot(xAxis, y[0:Fs*2])
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plt.axis([-0.1, 2, -1.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Output Signal - y[n]')
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plt.show()

‎Ch. 11 - Echo Effects/echoFeedback.py‎

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# ECHOFEEDBACK
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# This script demonstrates one example to create a feedback, tempo-synchronized
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# echo effect.
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#
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# See also ECHOSYNC
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import soundfile
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import numpy as np
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import matplotlib.pyplot as plt
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from IPython.display import Audio
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# Import audio file
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[x, Fs] = soundfile.read('sw20.wav')
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Ts = 1/Fs
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# Known tempo of recording
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beatsPerMin = 102 # units of beats per minute
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# Calculate beats per second
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beatsPerSec = beatsPerMin / 60 # 1 minute / 60 seconds
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# Calculate # of seconds per beat
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secPerBeat = 1/beatsPerSec
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# Note division
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# 4 = whole, 2 = half, 1 = quarter, 0.5 = 8th, 0.25 = 16th
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noteDiv = 0.5
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timeSec = noteDiv * secPerBeat
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# Convert to units of samples
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d = int(np.fix(timeSec * Fs)) # round to nearest integer sample
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a = -0.75 # amplitude of delay branch
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# Index each element of our signal to create the output
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N = len(x)
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y = np.zeros([N, 1])
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for n in range(N):
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# When the sample number is less than the time delay
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# Avoid indexing negative sample number
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if n < d + 1:
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# output = input
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y[n] = x[n]
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# Now add in the delayed signal
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else:
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# output = input + delayed version of output
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# reduce relative amplitude of delay to 3/4
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y[n] = x[n] + (-a) * y[n-d]
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t = np.arange(0, N) * Ts
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plt.plot(t, x, t, y)
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plt.xlabel('Time (sec.)')
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plt.ylabel('Amplitude')
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plt.title('Waveform')
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plt.show()
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Audio(y, rate=Fs)

‎Ch. 11 - Echo Effects/echoSync.py‎

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# ECHOSYNC
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# This script demonstrates one example to create a feedforward,
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# tempo-synchronized echo effect.
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#
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# See also CONVERTTEMPOSAMPLES
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import soundfile
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import numpy as np
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import matplotlib.pyplot as plt
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from IPython.display import Audio
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# Import our audio file
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[x, Fs] = soundfile.read('sw20.wav')
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Ts = 1/Fs
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# Known tempo of recording
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beatsPerMin = 102 # units of beats/minute
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# Calculate beats fore second
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beatsPerSec = beatsPerMin / 60 # 1 minute/60 seconds
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# Calculate # of seconds per beat
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secPerBeat = 1/beatsPerSec
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# Note division
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# 4 = whole, 2 = half, 1 = quarter, 0.5 = 8th, 0.25 = 16th
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noteDiv = 0.5
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# Calculate delay time in seconds
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timeSec = noteDiv * secPerBeat
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# Convert to units of samples
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d = int(np.fix(timeSec * Fs)) # round to nearest integer sample
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b = 0.75 # amplitude of delay branch
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# Total number of samples
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N = len(x)
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y = np.zeros([N, 1])
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# Index each element of our signal to create the output
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for n in range(N):
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# When the sample number is less than the time delay
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# Avoid indexing a negative number
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if n < d + 1:
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# output = input
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y[n] = x[n]
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# Now add in the delayed signal
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else:
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# output = input + delayed version of input
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# reduce relative amplitude of delay to 3/4
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echo = n - d
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y[n] = x[n] + b * x[echo]
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t = np.arange(0, N) * Ts
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plt.plot(t, x, t, y)
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plt.axis([0, 1, -1.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.ylabel('Amplitude')
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plt.title('Waveform')
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plt.show()
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Audio(y, rate=Fs)

‎Ch. 11 - Echo Effects/impFIR.py‎

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# IMPFIR
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# This script demonstrates one example to measure the impulse response of an
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# FIR system.
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#
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# See also IMPIIR
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import numpy as np
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import matplotlib.pyplot as plt
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import soundfile
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Fs = 48000
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N = Fs * 2
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# Synthesize the impulse signal
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imp = np.zeros([N,1])
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imp[1] = 1 # Change the first sample = 1
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d1 = int(0.5 * Fs) # 1/2 second delay
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b1 = 0.7 # Gain of first delay line
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d2 = int(1.5 * Fs) # 3/2 second delay
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b2 = 0.5 # Gain of second delay line
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# Zero-pad the beginning of the signal for indexing based on the maximum
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# delay time
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pad = np.zeros([d2, 1])
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impPad = np.concatenate((pad, imp))
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out = np.zeros([N, 1])
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# Index each element of our signal to create the output
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for n in range(N):
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index = n + d2
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out[n] = impPad[index] + b1 * impPad[index - d1] + b2 * impPad[index - d2]
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t = np.arange(0, N) / Fs
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plt.subplot(1,2,1)
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plt.stem(t, imp) # Plot the impulse response
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plt.axis([-0.1, 2, -0.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Input Impulse')
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plt.show()
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plt.subplot(1,2,2)
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plt.stem(t, out) # Plot the impulse response
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plt.axis([-0.1, 2, -0.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Output Impulse Response')
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plt.show()
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soundfile.write('impResp.wav', out, Fs)

‎Ch. 11 - Echo Effects/impIIR.py‎

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# IMPIIR
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# This script demonstrates one example to approximate the impulse response
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# of an IIR system.
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#
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# See also IMPFIR
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import numpy as np
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import matplotlib.pyplot as plt
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import soundfile
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Fs = 48000
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Ts = 1/Fs
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N = Fs * 2 # Number of samples
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# Synthesize impulse signal
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imp = np.zeros([N, 1])
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imp[1] = 1 # Change the first sample = 1
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out = np.zeros([N * 5, 1])
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d1 = int(0.5 * Fs) # 1/2 second delay
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a1 = -0.7 # Gain of feedback delay line
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# Index each element of our signal to create the output
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for n in range(d1):
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out[n] = imp[n] # Initially there is no delay
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for n in np.arange(d1+1, Fs*2): # Then there is signal + delay
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out[n] = imp[n] + a1 * out[n - d1]
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for n in np.arange(Fs*2+1, Fs*10): # Finally, there is only delay
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out[n] = a1 * out[n - d1] # After input finished
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t = np.arange(0, N) / Fs
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plt.subplot(1,2,1)
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plt.stem(t, imp) # Plot the impulse response
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plt.axis([-0.1, 2, -0.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Input Impulse')
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plt.show()
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t = np.arange(0, Fs * 10) * Ts
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plt.subplot(1,2,2)
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plt.stem(t, out) # Plot the impulse response
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plt.axis([-0.1, 10, -1.1, 1.1])
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plt.xlabel('Time (sec.)')
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plt.title('Output Impulse Response')
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plt.show()
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# soundfile.write('impResp.wav', imp, Fs)

‎Ch. 11 - Echo Effects/reverbConv.py‎

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# REVERBCONV
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# This script demonstrates the process to create a stereo convolution reverb
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# by using a two-channel impulse response. This impulse response is based
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# on a measurement of a recording studio in Nashville, TN.
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import soundfile
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import numpy as np
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import matplotlib.pyplot as plt
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from IPython.display import Audio
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# Import sound file and IR measurement
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[x, Fs] = soundfile.read('AcGtr.wav') # Mono signal
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[h,_] = soundfile.read('reverbIR.wav') # Stereo IR
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# Visualize one channel of the impulse response
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plt.plot(h[:, 0])
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plt.plot(h[:, 1])
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plt.show()
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# Perform convolution
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yLeft = np.convolve(x, h[:,0])
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yRight = np.convolve(x, h[:,1])
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y = [yLeft, yRight]
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Audio(y, rate=Fs)

‎Ch. 17 - Amplitude Envelope Effects/Amplitude Envelope Effects.ipynb‎

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‎Ch. 17 - Amplitude Envelope Effects/adsr.py‎

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# ADSR
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# This function can be used to apply an ADSR envelope on to an input signal.
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#
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# Input Variables
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# attackTime: length of attack ramp in milliseconds
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# decayTime: length of decay ramp in milliseconds
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# sustainAmplitude: linear amplitude of sustain segment
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# releaseTime: length of release ramp in ms
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import numpy as np
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def adsr(x, Fs, attackTime, decayTime, sustainAmplitude, releaseTime):
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# Convert time inputs to seconds
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attackTimeSec = attackTime/1000
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decayTimeSec = decayTime/1000
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releaseTimeSec = releaseTime/1000
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# Convert seconds to samples and determine sustain time
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a = int(np.round(attackTimeSec * Fs)) # Round each to an integer
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d = int(np.round(decayTimeSec * Fs)) # number of samples.
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r = int(np.round(releaseTimeSec * Fs))
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s = len(x) - (a + d + r) # determine length of sustain
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# Create linearly spaced fades for A, D, and R. Create hold for S.
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aFade = np.linspace(0, 1, a)
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dFade = np.linspace(1, sustainAmplitude, d)
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sFade = sustainAmplitude * np.ones(s)
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rFade = np.linspace(sustainAmplitude, 0, r)
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# Concatenates total ADSR envelope
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env = np.append(aFade, dFade)
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env = np.append(env, sFade)
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env = np.append(env, rFade)
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# Applies ADSR shaping to x
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y = x * env
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return y

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