-
Notifications
You must be signed in to change notification settings - Fork 0
/
__mic_array__.py
149 lines (123 loc) · 5.17 KB
/
__mic_array__.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
from pydub import AudioSegment
import math
from __microphone__ import Microphone
class MicrophoneArray(object):
CHUNK_MILLES = 50
DEFAULT_WEIGHT = 15
AMP_THRES = 0.1
def __init__(self, mic_arr=[]):
self.microphoneArray = mic_arr
self.sounds = []
for mic in self.microphoneArray:
self.sounds.append(mic.signal)
def add(self, mic):
self.microphoneArray.append(mic)
def getMaxDBFS(self):
max = 0
for i in range(len(self.microphoneArray)):
if self.microphoneArray[i].getNormalizedDBFS() > self.microphoneArray[max].getNormalizedDBFS():
max = i
return max
'''
Splits the wav file into 250 millesecond chunks, finds the absolute value of max DB in that chunk,
and then divides by the number of chunks, resulting in the normalized mean amplitude of the wav file
audio - the AudioSegment object to be processed for the aforementioned
'''
def getNormalizedMeanAmplitude(self, audio):
sounds = list(audio[::self.CHUNK_MILLES])
normalizedMeans = []
for i in range(len(sounds)):
normalizedMeans.append(abs(sounds[i].dBFS))
if(len(normalizedMeans) == 0):
return 0
return sum(normalizedMeans)/len(normalizedMeans)
'''
Returns the index of the AudioSegment object that has the greatest normalizedMeanAmplitude
'''
def getMaxNormalizedAmplitude(self, audios):
max = 0
for i in range(len(audios)):
if self.getNormalizedMeanAmplitude(audios[i]) > self.getNormalizedMeanAmplitude(audios[max]):
max = i
return max
'''
Overlays all the sound signals together. Also uses the weighting function to reduce dB response from other wav files.
max - the index of the AudioSegment object with the greatest normalized mean amplitude. Does not negatively affect weighting
of this one.
'''
def overlaySounds(self, max, audio):
combinedSignals = audio[max]
i = 0
while i < len(audio):
if i is not max:
sound = audio[i]
sound = sound - self.weightedFunction(max , i)
combinedSignals = combinedSignals.overlay(sound)
i = i + 1
return combinedSignals
'''
Calculates the dB response to remove from the specified sound signel. This is for future reference.
'''
def weightedFunction(self, max, i):
return self.DEFAULT_WEIGHT
'''
'''
def testOverlaySignals(self):
slicedSounds = []
combinedSignals = AudioSegment.from_wav('wavfiles/silence.wav')
for sound in self.sounds:
slicedSound = sound[::self.CHUNK_MILLES]
slicedSound = list(slicedSound)
slicedSounds.append(slicedSound)
rangeSlicedSounds = slicedSounds[0]
for M in range(len(rangeSlicedSounds)):
slicedM = []
slicedM = list(slicedM)
for slicedSound in slicedSounds:
slicedSound = list(slicedSound)
slicedM.append(slicedSound[M])
max = self.getMaxNormalizedAmplitude(slicedM)
sound = self.overlaySounds(max, slicedM)
combinedSignals = combinedSignals + sound
return combinedSignals
def getEstimatedAngle(self, max):
return self.microphoneArray[max].angle
def distance(self, mic1, mic2):
return math.sqrt((mic2.x - mic1.x)**2 + (mic2.y - mic2.x)**2)
def overallAngleEstimation(self):
DOAs = []
slicedSounds = []
for i in range(len(self.microphoneArray)):
slicedSounds.append(self.microphoneArray[i].signal[::Microphone.CHUNK_MILLES])
for i in range(len(slicedSounds)):
iDBFS = []
for slicedSound in slicedSounds:
iDBFS.append(slicedSound[i])
max = 0
for i in range(len(iDBFS)):
if iDBFS[i].dBFS > iDBFS[max].dBFS:
max = i
self.quickSort(self.microphoneArray, 0 , len(self.microphoneArray) - 1, max)
DOAs.append((1.0 * self.getEstimatedAngle(0) + 0.8 * self.getEstimatedAngle(1) + 0.6 * self.getEstimatedAngle(2)) / 3)
return DOAs
def partition(self, arr, low, high, maxIdx):
i = (low-1) # index of smaller element
pivot = self.distance(arr[maxIdx] , arr[high]) # pivot
for j in range(low, high):
# If current element is smaller than or
# equal to pivot
if self.distance(arr[maxIdx] , arr[j]) <= pivot:
# increment index of smaller element
i = i+1
arr[i], arr[j] = arr[j], arr[i]
arr[i+1], arr[high] = arr[high], arr[i+1]
return (i+1)
def quickSort(self,arr,low,high,maxIdx):
if low < high:
# pi is partitioning index, arr[p] is now
# at right place
pi = self.partition(arr,low,high, maxIdx)
# Separately sort elements before
# partition and after partition
self.quickSort(arr, low, pi-1 , maxIdx)
self.quickSort(arr, pi+1, high , maxIdx)