Mercurial > hg > cm
changeset 1:dc43033a2c20
* Added .dat files containing parameters and filter coefficients
* In cortical_model: added get_specific_loudness() function and dependencies.
* In cortical_model & in utils: fixed bugs created from changing variable and function names before last submit
* In cortical_model: changed plot_excitation_tf (now named plot_excitation_response) to allow an input signal to be optionally given to be analysed.
TODO:
* In cortical_model: comment
* In cortical_model: add partial loudness function
| author | Carl Bussey <c.bussey@se10.qmul.ac.uk> |
|---|---|
| date | Sun, 02 Feb 2014 22:35:48 +0000 |
| parents | 5609fd93e935 |
| children | 3b3c1c4ad283 |
| files | A.dat alpha.dat cortical_model.py erb.dat outMidFir.dat tq.dat utils.py |
| diffstat | 7 files changed, 263 insertions(+), 30 deletions(-) [+] |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/A.dat Sun Feb 02 22:35:48 2014 +0000 @@ -0,0 +1,1 @@ +4.7209,4.7204,4.7169,4.7084,4.6964,4.6814,4.6658,4.6565,4.6494,4.6421,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387,4.6387
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/alpha.dat Sun Feb 02 22:35:48 2014 +0000 @@ -0,0 +1,1 @@ +0.2,0.19999,0.19992,0.19975,0.19952,0.19921,0.19886,0.19863,0.19843,0.19818,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803,0.19803
--- a/cortical_model.py Sat Jan 25 20:00:38 2014 +0000 +++ b/cortical_model.py Sun Feb 02 22:35:48 2014 +0000 @@ -11,6 +11,7 @@ * utils.py and/or utils.pyc * erb.dat * outMidFir.dat + * tq.dat External dependencies: * scipy @@ -21,21 +22,108 @@ import utils import scipy.signal as sp +import scipy.fftpack as fft import numpy as np from copy import deepcopy import matplotlib.pyplot as plt -def get_partial_loudness(): +def get_specific_loudness(exc_i, inc_loud_region = False): """ - A function to calculate the partial loudness of an excitation pattern at given ERB. + A function to calculate the specific loudness of the excitation patterns at each ERB. TO DO """ + lenSig = np.shape(exc_i)[-1] #length signal + tq_dB, A, alpha = utils.load_sl_parameters() #load tq, A and alpha parameters + tq_dB = np.transpose(np.tile(tq_dB, (lenSig,1))) + tq = 10**(tq_dB / 10) + A = np.transpose(np.tile(A, (lenSig,1))) + alpha = np.transpose(np.tile(alpha, (lenSig,1))) + tq500_dB = 3.73 #threshold of quiet at 500Hz reference + G = 10**((tq500_dB - tq_dB)/10) #gain parameter + specific_loudness = exc_low = exc_mid = exc_high = np.zeros(np.shape(exc_i)) + less_than_tq = get_less_than_tq(exc_i, tq) #boolean array of elements less than or equal to the threshold of quiet + greater_than_tq = ~less_than_tq #boolean array of elements greater than the threshold of quiet + greater_than_tl = get_greater_than_tl(exc_i) #boolean array of elements greater than the loud threshold + gttq_lttl = greater_than_tq[~greater_than_tl] #boolean array of elements greater than the threshold of quiet but less than the threshold of loud - return + exc_low = exc_i[less_than_tq] + specific_loudness[less_than_tq] = get_specific_loudness_low(exc_low, G[less_than_tq], tq[less_than_tq], A[less_than_tq], alpha[less_than_tq]) + + if(inc_loud_region): + exc_mid = exc_i[gttq_lttl] + exc_high = exc_i[greater_than_tl] + specific_loudness[gttq_lttl] = get_specific_loudness_mid(exc_mid, G[gttq_lttl], A[gttq_lttl], alpha[gttq_lttl]) + specific_loudness[greater_than_tl] = get_specific_loudness_high(exc_high) + else: + exc_mid = exc_i[greater_than_tq] + specific_loudness[greater_than_tq] = get_specific_loudness_mid(exc_mid, G[greater_than_tq], A[greater_than_tq], alpha[greater_than_tq]) + + return specific_loudness -def get_excitation_i(input, fs, SPL, rectify=False): +def get_specific_loudness_low(exc_low, G, tq, A, alpha): + + C = 0.047 #constant parameter + specific_loudness_low = C * ((2*exc_low)/(exc_low+tq))**1.5 * ((G * exc_low + A)**alpha - A**alpha) + + return specific_loudness_low + +def get_specific_loudness_mid(exc_mid, G, A, alpha): + + C = 0.047 #constant parameter + specific_loudness_mid = C * ((G * exc_mid + A)**alpha - A**alpha) + + return specific_loudness_mid + +def get_specific_loudness_high(exc_high): + """ + + """ + + C = 0.047 #constant parameter + specific_loudness_high = C * (exc_high / (1.04 * 10**6))**0.5 + + return specific_loudness_high + +def get_greater_than_tl(exc_i): + """ + A function to return if each element of the excitation intensity is greater than the threshold of loud. + + Parameters: + * exc_i (type: array-like matrix of floats) - the input excitation intensity + + Returns: + * le_tq (type: array-like matrix of booleans) - a boolean array with dimensions equal to the input + specifying if the excitation intensity is greater than the threshold of loud + """ + + lenSig = np.shape(exc_i)[-1] + g_tl = exc_i[:,:]>np.transpose(np.tile(10**10,(lenSig,39))) + + return g_tl + +def get_less_than_tq(exc_i, tq): + """ + A function to return if each element of the excitation intensity is less than the threshold of quiet. + + Parameters: + * exc_i (type: array-like matrix of floats) - the input excitation intensity + * tq (type: array-like matrix of floats) - the threshold of quiet for each ERB. + + Returns: + * le_tq (type: array-like matrix of booleans) - a boolean array with dimensions equal to the input + specifying if the excitation intensity is less than the threshold of quiet + """ + + if (np.shape(exc_i)!=np.shape(tq)): + np.transpose(np.tile(exc_i,(np.shape(exc_i)[-1],1))) + + le_tq = exc_i<=tq + + return le_tq + +def get_excitation_i(input, fs, SPL, rectify=False, verbose = False): """ A function to calculate the excitation intensity of the input signal. @@ -57,7 +145,7 @@ input = np.array(input) inputOMFir = outMidFir(input) inputPa = v_Pascal(inputOMFir, SPL) - gtfs = gamma_tone_filter(inputPa, fs) + gtfs = gamma_tone_filter(inputPa, fs, verbose = verbose) if (rectify): gtfs = half_rectification(gtfs) gtfs = pa_i(gtfs) @@ -65,7 +153,7 @@ return gtfs -def plot_excitation_tf(fs = 44100, outMidFilt = True, xscale = 'log', yscale = 'log'): +def plot_excitation_response(input = 0, fs = 44100, outMidFilt = True, xscale = 'log', yscale = 'log'): """ A function that plots the transfer function of the outer middle ear and each gammatone filter. @@ -80,9 +168,10 @@ signal at each gammatone filter (and optionally, including the outer middle ear response). The response at the nth gammatone filter can be accessed by y[n]. """ - - input = np.zeros(np.ceil(fs)) - input[0] = 1 + + if ((np.shape(input)==())): + input = np.zeros(np.ceil(fs)) + input[0] = 1 if(outMidFilt): input = outMidFir(input) y = gamma_tone_filter(input, fs) @@ -91,7 +180,7 @@ plt.show() - return y + return fft.fft(y) def get_modulation_i(input, fs): """ @@ -109,7 +198,6 @@ be accessed with y[n]. """ - input = np.array(input) b = fir_antialias(fs) input_lp = sp.lfilter(b,(1),input_fr) @@ -138,7 +226,7 @@ return y -def gamma_tone_filter(input, fs): +def gamma_tone_filter(input, fs, verbose = False): """ A function to filter to decompose the input signal into 39 different gammatones filtered signals modelling the ERBs. @@ -153,7 +241,7 @@ input = np.array(input) fc, bw = utils.load_erb_data() - y = decomposition(input,fs,fc,bw) + y = decomposition(input,fs,fc,bw, verbose = verbose) return y @@ -172,7 +260,7 @@ """ input = np.array(input) - y = np.sign(input)*(0.00002*10**(np.log10(np.abs(input))+dBFS/20)) + y = np.sign(input)*(0.00002*10**(np.log10(np.abs(input))+SPL/20)) return y @@ -209,7 +297,7 @@ input = np.array(input) y = input / 1*(10**12) - return + return y def downsample(input, factor=100): """ @@ -299,7 +387,6 @@ signal of the nth filter can be accessed using y[n]. """ - input = np.array(input) nFreqs = len(fc) shape = (nFreqs,) + np.shape(input)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/erb.dat Sun Feb 02 22:35:48 2014 +0000 @@ -0,0 +1,39 @@ +20,26.828 +48.327,29.881 +79.877,33.281 +115.02,37.069 +154.16,41.287 +197.75,45.985 +246.3,51.217 +300.38,57.045 +360.61,63.536 +427.69,70.766 +502.41,78.819 +585.63,87.787 +678.32,97.777 +781.56,108.9 +896.54,121.29 +1024.6,135.1 +1167.3,150.47 +1326.1,167.59 +1503.1,186.66 +1700.2,207.9 +1919.7,231.56 +2164.2,257.91 +2436.5,287.26 +2739.8,319.94 +3077.6,356.35 +3453.8,396.9 +3872.9,442.06 +4339.6,492.36 +4859.5,548.39 +5438.5,610.79 +6083.4,680.29 +6801.7,757.7 +7601.7,843.92 +8492.8,939.95 +9485.2,1046.9 +10591,1166 +11822,1298.7 +13193,1446.5 +14720,1611.1
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tq.dat Sun Feb 02 22:35:48 2014 +0000 @@ -0,0 +1,1 @@ +37.038,26.436,17.727,12.824,9.8853,7.7397,6.1369,5.3208,4.712,4.0702,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73,3.73
--- a/utils.py Sat Jan 25 20:00:38 2014 +0000 +++ b/utils.py Sun Feb 02 22:35:48 2014 +0000 @@ -4,6 +4,7 @@ Packaged dependencies: * erb.dat * outMidFir.dat + * tq.dat External dependencies: * scipy @@ -32,7 +33,7 @@ Dependencies: * erb.dat (place in the same folder as utils.pyc) """ - # read our data from a text file + # read data from a text file data=np.loadtxt("erb.dat",delimiter=",") # load centre frequencies from the first column into fc @@ -57,21 +58,100 @@ * outMidFir.dat (place in the same folder as utils.pyc) """ - b=np.loadtxt("outMidFir.dat", delimiter=",") + b=np.array(np.loadtxt("outMidFir.dat", delimiter=",")) return b +def load_sl_parameters(): + """ + Loads the loudness parameters for each ERB fc into a tuple. A shortcut for load_tq_data(), load_A_data() and load_alpha_data(). + + Parameters: + NONE + + Returns: + * tq (type: numpy array of floats) - a vector of length 39 containing the threshold excitation + intensity in quietat each ERB centre frequency + * A (type: numpy array of floats) - a vector of length 39 containing the parameter A for each ERB fc + * alpha (type: numpy array of floats) - a vector of length 39 containing the parameter alpha for each ERB fc + + Dependencies: + * tq.dat (place in the same folder as utils.pyc) + * A.dat (place in the same folder as utils.pyc) + * alpha.dat (place in the same folder as utils.pyc) + """ + tq = load_tq_data() + A = load_A_data() + alpha = load_alpha_data() + + return tq, A, alpha + +def load_tq_data(): + """ + Loads and returns the excitation threshold of quiet for each ERB fc. + + Parameters: + NONE + + Returns: + * tq (type: numpy array of floats) - a vector of length 39 containing the threshold excitation + intensity in quietat each ERB centre frequency + + Dependencies: + * tq.dat (place in the same folder as utils.pyc) + """ + + tq = np.array(np.loadtxt("tq.dat",delimiter=",")) + + return tq + +def load_A_data(): + """ + Loads and returns the excitation A parameters for each ERB fc. + + Parameters: + NONE + + Returns: + * A (type: numpy array of floats) - a vector of length 39 containing the parameter A for each ERB fc + + Dependencies: + * A.dat (place in the same folder as utils.pyc) + """ + + A = np.array(np.loadtxt("A.dat",delimiter=",")) + + return A + +def load_alpha_data(): + """ + Loads and returns the excitation alpha parameters for each ERB fc. + + Parameters: + NONE + + Returns: + * alpha (type: numpy array of floats) - a vector of length 39 containing the parameter alpha for each ERB fc + + Dependencies: + * alpha.dat (place in the same folder as utils.pyc) + """ + + alpha = np.array(np.loadtxt("alpha.dat",delimiter=",")) + + return alpha + def exp_sequence(start, stop, n, base=2): """ Creates a linear sequence with n points starting from start and ending at stop. For each - element in the sequence, e, the output sequence is 2^e, generating an exponential sequence + element in the sequence, i, the output sequence is 2**i, generating an exponential sequence with base 2. Parameters: * start (type: numerical int) - determines the first element of the sequence, i.e., - base^start. (Required) + base**start. (Required) * stop (type: numerical int) - determines the last element of the sequence, i.e., - base^stop. (Required) + base**stop. (Required) * n (type = numerical int) - determines the number of elements in the sequence. (Required) * base (type: numerical) - determines the exponential base. (Optional; Default = 2) @@ -97,7 +177,7 @@ """ fs,data = wave.read(file) - data = np.array(int16_to_nfloat(data)) + data = np.array(int_to_nfloat(data)) return fs,data @@ -105,7 +185,7 @@ """ Unnormalises the audio data to a specified integer precision and converts to an int, then writes the audio data to a wav file. (E.g., if 16-bit precision, then highest amplitude - is equal to 2^16). + is equal to 2**16). Parameters: * file (type: string) - the name of the wav file to write to. (Required) @@ -154,8 +234,9 @@ """ if(xscale.lower() == "log"): plt.gca().set_xscale('log') - if(xscale.lower() == "log"): plt.gca().set_yscale('log') - + if(yscale.lower() == "log"): plt.gca().set_yscale('log') + + x = np.array(x) fftx = np.absolute(fft.fft(x)) plt.plot(range(np.shape(x)[0]),fftx) if(show): @@ -165,7 +246,29 @@ return -def int_to_nfloat(input, type=np.float32): +def plot_waveform(x, show = True): + """ + Plots the waveform of signal x. If the figure is not shown, the current plot is held to allow other plots to + be added to the same figure. + + Parameters: + * x (type: array-like matrix of floats) - the signal to be plotted. (Required) + * show (type: boolean) - specifies whether the figure should be shown. If False, the current plot will be held so + other plots can be added to the figure. (Optional; Default = True) + + Returns: + NONE + """ + + x = np.array(x) + plt.plot(x) + + if(show): plt.show() + else: plt.hold(True) + + return + +def int_to_nfloat(input, outputtype=np.float32): """ Convert integer with to floating point with a range from -1 to 1. @@ -180,9 +283,9 @@ input = np.array(input) assert input.dtype.kind == 'i', "'input' must be an array-like matrix of integers." - type = np.dtype(type); + outputtype = np.dtype(outputtype) inputdtype = np.dtype(type(input[0])) - input = input.astype(type) + input = input.astype(outputtype) input[input > 0] = input[input > 0] / np.iinfo(inputdtype).max input[input < 0] = input[input < 0] / -np.iinfo(inputdtype).min @@ -208,8 +311,8 @@ input = np.array(input) assert input.dtype.kind == 'f', "'input' must be an array of floats!" - input[input > 0] = input[ input > 0 ] * np.iinfo(np.int16).max - input[input < 0] = input[ input < 0 ] * -np.iinfo(np.int16).min + input[input > 0] = input[ input > 0 ] * np.iinfo(type).max + input[input < 0] = input[ input < 0 ] * -np.iinfo(type).min y = input.astype(type)