def filter(input, ratio=4, threshold=1, softness=1): import numpy as np output = np.zeros(input.shape, dtype=np.float64) input2 = input**2 in2filter = DoubleInPointerFilter(input2, False) in2filter.input_sampling_rate = sample_rate infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = sample_rate gainfilter = DoubleGainExpanderFilter(1) gainfilter.input_sampling_rate = sample_rate gainfilter.set_input_port(0, in2filter, 0) gainfilter.threshold = threshold gainfilter.ratio = ratio gainfilter.softness = softness applygainfilter = DoubleApplyGainFilter(1) applygainfilter.input_sampling_rate = sample_rate applygainfilter.set_input_port(0, gainfilter, 0) applygainfilter.set_input_port(1, infilter, 0) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = sample_rate outfilter.set_input_port(0, applygainfilter, 0) outfilter.process(input.shape[1]) return output
def max_colored_filter(input, ratio=4, threshold=1, softness=1, quality=1, color=1, max_reduction=-10): import numpy as np output = np.zeros(input.shape, dtype=np.float64) input2 = input**2 in2filter = DoubleInPointerFilter(input2, False) in2filter.input_sampling_rate = sample_rate infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = sample_rate gainfilter = DoubleGainMaxColoredExpanderFilter(1) gainfilter.input_sampling_rate = sample_rate gainfilter.set_input_port(0, in2filter, 0) gainfilter.threshold = threshold gainfilter.ratio = ratio gainfilter.color = color gainfilter.softness = softness gainfilter.quality = quality gainfilter.max_reduction = 10 ** (max_reduction / 20.) applygainfilter = DoubleApplyGainFilter(1) applygainfilter.input_sampling_rate = sample_rate applygainfilter.set_input_port(0, gainfilter, 0) applygainfilter.set_input_port(1, infilter, 0) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = sample_rate outfilter.set_input_port(0, applygainfilter, 0) outfilter.process(input.shape[1]) return output
def filter(input, ratio=4, threshold=1, softness=1):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
input2 = input**2
in2filter = DoubleInPointerFilter(input2, False)
in2filter.input_sampling_rate = sample_rate
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = sample_rate
gainfilter = DoubleGainCompressorFilter(1)
gainfilter.input_sampling_rate = sample_rate
gainfilter.set_input_port(0, in2filter, 0)
gainfilter.threshold = threshold
gainfilter.ratio = ratio
gainfilter.softness = softness
applygainfilter = DoubleApplyGainFilter(1)
applygainfilter.input_sampling_rate = sample_rate
applygainfilter.set_input_port(0, gainfilter, 0)
applygainfilter.set_input_port(1, infilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = sample_rate
outfilter.set_input_port(0, applygainfilter, 0)
outfilter.process(input.shape[1])
return output
def max_colored_filter(input,
ratio=4,
threshold=1,
softness=1,
quality=1,
color=1,
max_reduction=-10):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
input2 = input**2
in2filter = DoubleInPointerFilter(input2, False)
in2filter.input_sampling_rate = sample_rate
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = sample_rate
gainfilter = DoubleGainMaxColoredExpanderFilter(1)
gainfilter.input_sampling_rate = sample_rate
gainfilter.set_input_port(0, in2filter, 0)
gainfilter.threshold = threshold
gainfilter.ratio = ratio
gainfilter.color = color
gainfilter.softness = softness
gainfilter.quality = quality
gainfilter.max_reduction = 10**(max_reduction / 20.)
applygainfilter = DoubleApplyGainFilter(1)
applygainfilter.input_sampling_rate = sample_rate
applygainfilter.set_input_port(0, gainfilter, 0)
applygainfilter.set_input_port(1, infilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = sample_rate
outfilter.set_input_port(0, applygainfilter, 0)
outfilter.process(input.shape[1])
return output
def filter(input):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = sample_rate
powerfilter = DoublePowerFilter(1)
powerfilter.input_sampling_rate = sample_rate
powerfilter.set_input_port(0, infilter, 0)
powerfilter.memory = np.exp(-1 / (sample_rate * 1e-3))
attackreleasefilter = DoubleAttackReleaseFilter(1)
attackreleasefilter.input_sampling_rate = sample_rate
attackreleasefilter.set_input_port(0, powerfilter, 0)
attackreleasefilter.attack = np.exp(-1 / (sample_rate * 1e-3))
attackreleasefilter.release = np.exp(-1 / (sample_rate * 100e-3))
gainfilter = DoubleGainCompressorFilter(1)
gainfilter.input_sampling_rate = sample_rate
gainfilter.set_input_port(0, attackreleasefilter, 0)
gainfilter.threshold = 0.5
gainfilter.ratio = 4
gainfilter.softness = 1
applygainfilter = DoubleApplyGainFilter(1)
applygainfilter.input_sampling_rate = sample_rate
applygainfilter.set_input_port(0, gainfilter, 0)
applygainfilter.set_input_port(1, infilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = sample_rate
outfilter.set_input_port(0, applygainfilter, 0)
outfilter.process(input.shape[1])
return output
def filter(input): import numpy as np output = np.zeros(input.shape, dtype=np.float64) infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = sample_rate sinusfilter = DoubleSinusGeneratorFilter() sinusfilter.input_sampling_rate = sample_rate sinusfilter.offset = 0.5 sinusfilter.volume = 0.5 sinusfilter.frequency = 10 gainfilter = DoubleApplyGainFilter(1) gainfilter.input_sampling_rate = sample_rate gainfilter.set_input_port(0, infilter, 0) gainfilter.set_input_port(1, sinusfilter, 1) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = sample_rate outfilter.set_input_port(0, gainfilter, 0) outfilter.process(input.shape[0]) return output
def filter(input):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
infilter.input_sampling_rate = sample_rate
sinusfilter = DoubleSinusGeneratorFilter()
sinusfilter.input_sampling_rate = sample_rate
sinusfilter.offset = 0.5
sinusfilter.volume = 0.5
sinusfilter.frequency = 10
gainfilter = DoubleApplyGainFilter(1)
gainfilter.input_sampling_rate = sample_rate
gainfilter.set_input_port(0, infilter, 0)
gainfilter.set_input_port(1, sinusfilter, 1)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.input_sampling_rate = sample_rate
outfilter.set_input_port(0, gainfilter, 0)
outfilter.process(input.shape[0])
return output
def filter(input): import numpy as np output = np.zeros(input.shape, dtype=np.float64) infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = sample_rate powerfilter = DoublePowerFilter(1) powerfilter.input_sampling_rate = sample_rate powerfilter.set_input_port(0, infilter, 0) powerfilter.memory = np.exp(-1/(sample_rate*1e-3)) attackreleasefilter = DoubleAttackReleaseFilter(1) attackreleasefilter.input_sampling_rate = sample_rate attackreleasefilter.set_input_port(0, powerfilter, 0) attackreleasefilter.attack = np.exp(-1/(sample_rate*1e-3)) attackreleasefilter.release = np.exp(-1/(sample_rate*100e-3)) gainfilter = DoubleGainCompressorFilter(1) gainfilter.input_sampling_rate = sample_rate gainfilter.set_input_port(0, attackreleasefilter, 0) gainfilter.threshold = 0.5 gainfilter.ratio = 4 gainfilter.softness = 1 applygainfilter = DoubleApplyGainFilter(1) applygainfilter.input_sampling_rate = sample_rate applygainfilter.set_input_port(0, gainfilter, 0) applygainfilter.set_input_port(1, infilter, 0) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = sample_rate outfilter.set_input_port(0, applygainfilter, 0) outfilter.process(input.shape[1]) return output
