问题
I need to align two sound signals in order to map one into the other (both signals corresponds to the same behavior). I try to implement the python code from: https://nipunbatra.github.io/blog/2014/dtw.html
as a function to be called by my code. An example:
#time warping sound function trial
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
my_path ='/home/...'
def time_warping (x,y,fs,name):
distances = np.zeros((len(y), len(x)))
accumulated_cost = np.zeros((len(y), len(x)))
accumulated_cost[0,0] = distances[0,0]
def distance_cost_plot(distances):
#function to visualize the distance matrix
im = plt.imshow(distances, interpolation='nearest', cmap='Reds')
plt.gca().invert_yaxis()
plt.xlabel("X")
plt.ylabel("Y")
plt.grid()
plt.colorbar();
#plt.show()
plt.close()
def path_cost(x, y, accumulated_cost, distances):
#this is like mlpy.dtw_std (I gues..)
path = [[len(x)-1, len(y)-1]]
cost = 0
i = len(y)-1
j = len(x)-1
while i>0 and j>0:
if i==0:
j = j - 1
elif j==0:
i = i - 1
else:
if accumulated_cost[i-1, j] == min(accumulated_cost[i-1, j-1], accumulated_cost[i-1, j], accumulated_cost[i, j-1]):
i = i - 1
elif accumulated_cost[i, j-1] == min(accumulated_cost[i-1, j-1], accumulated_cost[i-1, j], accumulated_cost[i, j-1]):
j = j-1
else:
i = i - 1
j= j- 1
path.append([j, i])
path.append([0,0])
for [y, x] in path:
cost = cost +distances[x, y]
return path, cost
#Here I apply the function over function x and y
path, cost = path_cost(x, y, accumulated_cost, distances)
for i in range(len(y)):
for j in range(len(x)):
distances[i,j] = (x[j]-y[i])**2
#Here I plot the distance
g=distance_cost_plot(distances)
accumulated_cost = np.zeros((len(y), len(x)))
accumulated_cost[0,0] = distances[0,0]
for i in range(1, len(y)):
accumulated_cost[i,0] = distances[i, 0] + accumulated_cost[i-1, 0]
for i in range(1, len(x)):
accumulated_cost[0,i] = distances[0,i] + accumulated_cost[0, i-1]
for i in range(1, len(y)):
for j in range(1, len(x)):
accumulated_cost[i, j] = min(accumulated_cost[i-1, j-1], accumulated_cost[i-1, j], accumulated_cost[i, j-1]) + distances[i, j]
#empy list for the maping
map_x_final =[]
map_y_final =[]
map_x_f_final =[]
map_y_f_final =[]
paths = path_cost(x, y, accumulated_cost, distances)[0] #no entiendo la sintaxis de esta linea
print 'path',paths
print 'accumulated_cost',accumulated_cost
print 'distances',distances
#print 'paths.shape',path.shape
plt.figure(figsize=(14,8)) # 8 plots in one
plt.subplot(2,1,1)
grid(True)
map_x_fx =[]
map_y_fy =[]
map_y_fy_newlist =[]
for [map_x, map_y] in paths:
#print map_x, x[map_x], ":", map_y, y[map_y]
plt.plot([map_x*float(1)/float(fs), map_y*float(1)/float(fs)], [x[map_x], y[map_y]], 'r')
#plt.plot([map_x, map_y], [x[map_x], y[map_y]], 'r')
#saving in empy list
map_x_fx.append([map_x,x[map_x]])
map_y_fy.append([map_x,y[map_y]])
map_x_final.append(map_x)
map_y_final.append(map_y)
map_x_f_final.append(x[map_x])
map_y_f_final.append(y[map_y])
dif_a_sumar = (map_y-map_x)*float(1)/float(fs)
map_x_final = np.asarray(map_x_final)
map_y_final = np.asarray(map_y_final)
map_x_f_final = np.asarray(map_x_f_final)
map_y_f_final = np.asarray(map_y_f_final)
####
map_x_final_vec = np.asarray(map_x_fx)
map_y_final_vec = np.asarray(map_y_fy)
#Erase the elements that has been alrady map
lista_aux=[]
for j,[a,b] in enumerate(map_y_fy):
print j,':', [a,b]
print len( map_x_final[:j])
if a not in map_x_final[:j]:
lista_aux.append([a,b])
else:
pass
print'++++++'
print'lista aux len: ',len(lista_aux)
map_y_final_vec_ =np.asarray(lista_aux)
print'++++'
print 'map_y_fy',len(map_y_fy)
print'*************************'
#print ' a veer map_x_fx: ',map_x_fx
#print ' a veer map_x_fx type: ',type(map_x_fx)
#print ' map_y_f_final_vec shape',map_y_f_final_vec.shape
#print ' a veer map_x_final_vec: ',map_x_final_vec
#print ' a veer map_x_final_vec[0]: ',map_x_final_vec[0]
print'*************************'
print 'x shape',x.shape
print 'y shape',y.shape
print 'map_x_f_final',map_x_f_final.shape
print 'map_y_f_final',map_y_f_final.shape
print 'map_y_final_vec shape',map_y_final_vec.shape
print 'map_y_final_vec_ shape',map_y_final_vec_.shape
print'*************************'
#print map_x_final.size, map_y_final.size, map_x_f_final.size, map_y_f_final.size
time_x = np.arange(x.size)*float(1)/float(fs)
time_y = np.arange(y.size)*float(1)/float(fs)
time_map_x = np.arange(map_x_f_final.size)*float(1)/float(fs)
time_map_y = np.arange(map_y_f_final.size)*float(1)/float(fs)
plt.plot(time_x,x, 'bo-',linewidth=1 ,label='funcion target: X ')#'bo-'
plt.plot(time_y,y, 'go-',linewidth=1,markersize=3, label = 'funcion a proyectar :Y')#'g^-'
plt.legend(fontsize= 'small')
plt.ylabel('Signal')
plt.xlabel('time [s]')
plt.subplot(2,1,2) #los graficos mapeados
grid(True)
plt.plot(time_x,x, 'b',linewidth=1 ,label='funcion target: X sonido-vs')#o-
plt.plot(time_y,y, 'g',linewidth=1,markersize=3, label = 'funcion a proyectar :Y sonido-p')#'g^-'
plt.plot(map_y_final_vec_[:, 0]*float(1)/float(fs), map_y_final_vec_[:,1],'yo-',markersize=5, label='funcion Y mapeada donde convergen con DTW sobre X')#'m^'
plt.ylabel('Signal')
plt.xlabel('time [s]')
plt.legend(fontsize= 'small')
figname = "%s.jpg"%('alineado_dtw_'+name)
plt.savefig(my_path+figname,dpi=200)
#plt.show()
plt.close()
mapeo_time = map_y_final_vec_[:, 0]*float(1)/float(fs)
mapeo_amplitude = map_y_final_vec_[:,1]
return mapeo_time, mapeo_amplitude
I am able to obtain the distance between both signals: But I'm not sure with the final mapping. Am I doing something wrong with my mapping? I need to project one signal over the other, rescaling the first one with the other. I also tried with these two real signals: I try to compare with:https://pypi.python.org/pypi/fastdtw and also with mlp library, but I get different signal mapping.
I also put everything on https://github.com/katejarne/dtw with the data set to generate the last figure and the mapping.
来源：https://stackoverflow.com/questions/36152229/dynamic-time-warping-with-python-final-mapping