danstowell/wiener_deconvolution_example.py

## wiener_deconvolution_example.py
#!/usr/bin/env python

# Simple example of Wiener deconvolution in Python.
# We use a fixed SNR across all frequencies in this example.
#
# Written 2015 by Dan Stowell. Public domain.

import numpy as np
from numpy.fft import fft, ifft, ifftshift

import matplotlib
#matplotlib.use('PDF') # http://www.astrobetter.com/plotting-to-a-file-in-python/
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.backends.backend_pdf import PdfPages
plt.rcParams.update({'font.size': 6})

##########################
# user config
sonlen  = 128
irlen   =  64

lambd_est = 1e-3  # estimated noise lev

##########################

def gen_son(length):
	"Generate a synthetic un-reverberated 'sound event' template"
	# (whitenoise -> integrate -> envelope -> normalise)
	son = np.cumsum(np.random.randn(length))
	# apply envelope
	attacklen = length / 8
	env = np.hstack((np.linspace(0.1, 1, attacklen), np.linspace(1, 0.1, length - attacklen)))
	son *= env
	son /= np.sqrt(np.sum(son * son))
	return son

def gen_ir(length):
	"Generate a synthetic impulse response"
	# First we generate a quietish tail
	son = np.random.randn(length)
	attacklen = length / 2
	env = np.hstack((np.linspace(0.1, 1, attacklen), np.linspace(1, 0.1, length - attacklen)))
	son *= env
	son *= 0.05
	# Here we add the "direct" signal
	son[0] = 1
	# Now some early reflection spikes
	for _ in range(10):
		son[ int(length * (np.random.rand()**2))  ] += np.random.randn() * 0.5
	# Normalise and return
	son /= np.sqrt(np.sum(son * son))
	return son

def wiener_deconvolution(signal, kernel, lambd):
	"lambd is the SNR"
	kernel = np.hstack((kernel, np.zeros(len(signal) - len(kernel)))) # zero pad the kernel to same length
	H = fft(kernel)
	deconvolved = np.real(ifft(fft(signal)*np.conj(H)/(H*np.conj(H) + lambd**2)))
	return deconvolved

if __name__ == '__main__':
	"simple test: get one soundtype and one impulse response, convolve them, deconvolve them, and check the result (plot it!)"
	son = gen_son(sonlen)
	ir  = gen_ir(irlen)
	obs = np.convolve(son, ir, mode='full')
	# let's add some noise to the obs
	obs += np.random.randn(*obs.shape) * lambd_est
	son_est = wiener_deconvolution(obs, ir,  lambd=lambd_est)[:sonlen]
	ir_est  = wiener_deconvolution(obs, son, lambd=lambd_est)[:irlen]
	# calc error
	son_err = np.sqrt(np.mean((son - son_est) ** 2))
	ir_err  = np.sqrt(np.mean((ir  -  ir_est) ** 2))
	print("single_example_test(): RMS errors son %g, IR %g" % (son_err, ir_err))
	# plot
	pdf = PdfPages('wiener_deconvolution_example.pdf')
	plt.figure(frameon=False)
	#
	plt.subplot(3,2,1)
	plt.plot(son)
	plt.title("son")
	plt.subplot(3,2,3)
	plt.plot(son_est)
	plt.title("son_est")
	plt.subplot(3,2,2)
	plt.plot(ir)
	plt.title("ir")
	plt.subplot(3,2,4)
	plt.plot(ir_est)
	plt.title("ir_est")
	plt.subplot(3,1,3)
	plt.plot(obs)
	plt.title("obs")
	#
	pdf.savefig()
	plt.close()
	pdf.close()
	#!/usr/bin/env python

	# Simple example of Wiener deconvolution in Python.
	# We use a fixed SNR across all frequencies in this example.
	#
	# Written 2015 by Dan Stowell. Public domain.

	import numpy as np
	from numpy.fft import fft, ifft, ifftshift

	import matplotlib
	#matplotlib.use('PDF') # http://www.astrobetter.com/plotting-to-a-file-in-python/
	import matplotlib.pyplot as plt
	import matplotlib.cm as cm
	from matplotlib.backends.backend_pdf import PdfPages
	plt.rcParams.update({'font.size': 6})

	##########################
	# user config
	sonlen = 128
	irlen = 64

	lambd_est = 1e-3 # estimated noise lev

	##########################

	def gen_son(length):
	"Generate a synthetic un-reverberated 'sound event' template"
	# (whitenoise -> integrate -> envelope -> normalise)
	son = np.cumsum(np.random.randn(length))
	# apply envelope
	attacklen = length / 8
	env = np.hstack((np.linspace(0.1, 1, attacklen), np.linspace(1, 0.1, length - attacklen)))
	son *= env
	son /= np.sqrt(np.sum(son * son))
	return son

	def gen_ir(length):
	"Generate a synthetic impulse response"
	# First we generate a quietish tail
	son = np.random.randn(length)
	attacklen = length / 2
	env = np.hstack((np.linspace(0.1, 1, attacklen), np.linspace(1, 0.1, length - attacklen)))
	son *= env
	son *= 0.05
	# Here we add the "direct" signal
	son[0] = 1
	# Now some early reflection spikes
	for _ in range(10):
	son[ int(length * (np.random.rand()*2)) ] += np.random.randn() 0.5
	# Normalise and return
	son /= np.sqrt(np.sum(son * son))
	return son

	def wiener_deconvolution(signal, kernel, lambd):
	"lambd is the SNR"
	kernel = np.hstack((kernel, np.zeros(len(signal) - len(kernel)))) # zero pad the kernel to same length
	H = fft(kernel)
	deconvolved = np.real(ifft(fft(signal)np.conj(H)/(Hnp.conj(H) + lambd**2)))
	return deconvolved

	if __name__ == '__main__':
	"simple test: get one soundtype and one impulse response, convolve them, deconvolve them, and check the result (plot it!)"
	son = gen_son(sonlen)
	ir = gen_ir(irlen)
	obs = np.convolve(son, ir, mode='full')
	# let's add some noise to the obs
	obs += np.random.randn(obs.shape) lambd_est
	son_est = wiener_deconvolution(obs, ir, lambd=lambd_est)[:sonlen]
	ir_est = wiener_deconvolution(obs, son, lambd=lambd_est)[:irlen]
	# calc error
	son_err = np.sqrt(np.mean((son - son_est) ** 2))
	ir_err = np.sqrt(np.mean((ir - ir_est) ** 2))
	print("single_example_test(): RMS errors son %g, IR %g" % (son_err, ir_err))
	# plot
	pdf = PdfPages('wiener_deconvolution_example.pdf')
	plt.figure(frameon=False)
	#
	plt.subplot(3,2,1)
	plt.plot(son)
	plt.title("son")
	plt.subplot(3,2,3)
	plt.plot(son_est)
	plt.title("son_est")
	plt.subplot(3,2,2)
	plt.plot(ir)
	plt.title("ir")
	plt.subplot(3,2,4)
	plt.plot(ir_est)
	plt.title("ir_est")
	plt.subplot(3,1,3)
	plt.plot(obs)
	plt.title("obs")
	#
	pdf.savefig()
	plt.close()
	pdf.close()