基于麦克风阵列的声源定位_python播放声音模块

基于麦克风阵列的声源定位_python播放声音模块上一篇文章说到odas_web界面非常难安装,并且运行也很卡。所以我自己用python写了一个界面程序,用来接收odas处理完的结果。这个界面程序与odas之间是通过socket连接的,界面作为服务器,odas作为客户端,由于有两路数据,所以各有两个服务器和客户端。但是实际绘制在界面上的是SSL的结果,不是SST的结果。其实我也试过SST的结果,从直观的感受而言,效果会比SSL差一些,实时性不是很高,我的理解SST的好处是可以跟踪音源是否有活动。

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上一篇文章说到odas_web界面非常难安装,并且运行也很卡。所以我自己用python写了一个界面程序,用来接收odas处理完的结果。

这个界面程序与odas之间是通过socket连接的, 界面作为服务器,odas作为客户端,由于有两路数据,所以各有两个服务器和客户端。但是实际绘制在界面上的是SSL的结果,不是SST的结果。其实我也试过SST的结果,从直观的感受而言,效果会比SSL差一些,实时性不是很高,我的理解SST的好处是可以跟踪音源是否有活动。 

另外,我也试过把这个代码在树莓派3上跑,性能会比odas_web好不少,但是还是不如笔记本电脑跑得流畅。在树莓派上先要装python3-opencv,然后用python3来运行这个界面程序。

安装命令和两个程序的运行命令可以参考如下:

sudo apt install python3-opencv
python3 DOA_sound.py
./bin/odaslive -c config/odaslive/shao.cfg

视频:

麦克风阵列 声源定位 定向拾音_哔哩哔哩_bilibili基于麦克风阵列的声源定位_python播放声音模块https://www.bilibili.com/video/BV1xu411B7s3

下面附上我写的界面程序:

#!/usr/bin/env python
import socket
import sys
import threading
import random
import os
import time
import struct
import cv2
import signal
import json
import ast
import numpy as np
stop = False
HOST = "0.0.0.0"
PORT = 9000
SOCK_ADDR = (HOST, PORT)
PORT2 = 9001
SOCK_ADDR2 = (HOST, PORT2)
def stop_handler(signum, frame):
global running
running = False
signal.signal(signal.SIGINT, stop_handler) 
spectrum_rgb3_lut = [
[   0,   0,   0 ],
[   0,   0,   3 ],
[   0,   0,   6 ],
[   0,   0,   9 ],
[   0,   0,  12 ],
[   0,   0,  15 ],
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[   0,   0,  87 ],
[   0,   0,  90 ],
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[ 240,  15,   0 ],
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[ 249,   6,   0 ],
[ 252,   3,   0 ],
[ 255,   0,   0 ]]
class SocketClientObject(object):
def __init__(self, socket, address ):
self.socket = socket
self.address = address
class ClientThread(threading.Thread):
def __init__(self, client_object):
threading.Thread.__init__(self)
self.client_object = client_object
def run(self):
global running
while running == True:
img = np.zeros((800,800,3),np.uint8)
data = self.client_object.socket.recv(1024)
data = data.decode("utf-8")
data = data.replace("\n", "")
try:
src = (data.split('[')[1]).split(']')[0]
items = src.split(",        ")
target = json.loads(items[0])
x = int(float(target["x"]) * 400) + 400
y = int(-float(target["y"]) * 400) + 400
energy = int(float(target["E"]) * 255)
if (energy > 80):
cv2.circle(img,  (x, y),  30,  (spectrum_rgb3_lut[255- energy][0], spectrum_rgb3_lut[255- energy][1], spectrum_rgb3_lut[255- energy][2]), -1)
cv2.imshow('pu', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except:
print "problem1"
cv2.destroyAllWindows()
self.client_object.socket.close()
class VideoThread(threading.Thread):
def __init__(self,dest_object):
threading.Thread.__init__(self)
self.dest_object=dest_object
def run(self):
global running
while running == True:
#img = np.zeros((800,800,3),np.uint8)
data = self.dest_object.socket.recv(1024)
data = data.decode("utf-8")
data = data.replace("\n", "")
try:
src = (data.split('[')[1]).split(']')[0]
items = src.split(",        ")
for item in items:
target = json.loads(item)
x = int(float(target["x"]) * 400) + 400
y = int(-float(target["y"]) * 400) + 400
activity = int(float(target["activity"]) * 255)
#if (activity > 100):
#    cv2.circle(img,  (x, y),  30, (0,255,0), -1)
#cv2.imshow('pu2', img)
#if cv2.waitKey(1) & 0xFF == ord('q'):
#break
except:
print "problem2"
#cv2.destroyAllWindows()
self.dest_object.socket.close()
def main():
global running
running = True
try:
sock1 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock1.bind(SOCK_ADDR)
sock1.listen(5)
sock2 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock2.bind(SOCK_ADDR2)
sock2.listen(2)
while running:
(clientsocket, address) = sock1.accept()
print " Accept client: ", address
ct = ClientThread(SocketClientObject(clientsocket, address))
ct.start()
(dst,dst_addr) = sock2.accept()
print "Destination Connected by", dst_addr
vt = VideoThread(SocketClientObject(dst,dst_addr))
vt.start()
except:
print "#! EXC: ", sys.exc_info()
sock1.close()
sock2.close()
print "THE END! Goodbye!"
if __name__ == "__main__":
main()

另外,我还附上我使用的shao.cfg文件:

# Configuration file for ReSpeaker USB 4 Mic Array (ReSpeaker USB Mic Array v2.0)
version = "2.1";
# Raw
raw: 
{
fS = 16000;
hopSize = 128;
nBits = 16;
nChannels = 6; 
# Input with raw signal from microphones
interface: {
type = "soundcard";
card = 2;
device = 0;
}
}
# Mapping
mapping:
{
map: (2, 3, 4, 5);
}
# General
general:
{
epsilon = 1E-20;
size: 
{
hopSize = 128;
frameSize = 256;
};
samplerate:
{
mu = 16000;
sigma2 = 0.01;
};
speedofsound:
{
mu = 343.0;
sigma2 = 25.0;
};
mics = (
# Microphone 2
{ 
mu = ( -0.032, +0.000, +0.000 ); 
sigma2 = ( +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000 );
direction = ( +0.000, +0.000, +1.000 );
angle = ( 80.0, 100.0 );
},
# Microphone 3
{ 
mu = ( +0.000, -0.032, +0.000 ); 
sigma2 = ( +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000 );
direction = ( +0.000, +0.000, +1.000 );
angle = ( 80.0, 100.0 );
},
# Microphone 4
{ 
mu = ( +0.032, +0.000, +0.000 ); 
sigma2 = ( +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000 );
direction = ( +0.000, +0.000, +1.000 );
angle = ( 80.0, 100.0 );
},
# Microphone 5
{ 
mu = ( +0.000, +0.032, +0.000 ); 
sigma2 = ( +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000, +0.000 );
direction = ( +0.000, +0.000, +1.000 );
angle = ( 80.0, 100.0 );        
}
);
# Spatial filter to include only a range of direction if required
# (may be useful to remove false detections from the floor)
spatialfilters = (
{
direction = ( +0.000, +0.000, +1.000 );
angle = (80.0, 100.0);
}    
);
nThetas = 181;
gainMin = 0.25;
};
# Stationnary noise estimation
sne:
{
b = 3;
alphaS = 0.1;
L = 150;
delta = 3.0;
alphaD = 0.1;
}
# Sound Source Localization
ssl:
{
nPots = 4;
nMatches = 10;
probMin = 0.5;
nRefinedLevels = 1;
interpRate = 4;
# Number of scans: level is the resolution of the sphere
# and delta is the size of the maximum sliding window
# (delta = -1 means the size is automatically computed)
scans = (
{ level = 2; delta = -1; },
{ level = 4; delta = -1; }
);
# Output to export potential sources
potential: {
# format = "undefined";
format = "json";
interface: {
#type = "blackhole";
type = "socket"; ip = "127.0.0.1"; port = 9000;
#type = "terminal";
};
};
};
# Sound Source Tracking
sst:
{  
# Mode is either "kalman" or "particle"
mode = "kalman";
# Add is either "static" or "dynamic"
add = "dynamic";    
# Parameters used by both the Kalman and particle filter
active = (
{ weight = 1.0; mu = 0.4; sigma2 = 0.0025 }
);
inactive = (
{ weight = 1.0; mu = 0.25; sigma2 = 0.0025 }
);
sigmaR2_prob = 0.0025;
sigmaR2_active = 0.0225;
sigmaR2_target = 0.0025;
Pfalse = 0.1;
Pnew = 0.1;
Ptrack = 0.8;
theta_new = 0.9;
N_prob = 5;
theta_prob = 0.8;
N_inactive = ( 250, 250, 250, 250 );
theta_inactive = 0.9;
# Parameters used by the Kalman filter only
kalman: {
sigmaQ = 0.001;
};
# Parameters used by the particle filter only
particle: {
nParticles = 1000;
st_alpha = 2.0;
st_beta = 0.04;
st_ratio = 0.5;
ve_alpha = 0.05;
ve_beta = 0.2;
ve_ratio = 0.3;
ac_alpha = 0.5;
ac_beta = 0.2;
ac_ratio = 0.2;
Nmin = 0.7;
};
target: ();
# Output to export tracked sources
tracked: {
format = "json";
interface: {
#type = "file"; path = "tracks.txt";
type = "socket"; ip = "127.0.0.1"; port = 9001;
#type = "terminal";
};
};
}
sss:
{
# Mode is either "dds", "dgss" or "dmvdr"
mode_sep = "dds";
mode_pf = "ms";
gain_sep = 1.0;
gain_pf = 10.0;
dds: {
};
dgss: {
mu = 0.01;
lambda = 0.5;
};
dmvdr: {
};
ms: {
alphaPmin = 0.07;
eta = 0.5;
alphaZ = 0.8;        
thetaWin = 0.3;
alphaWin = 0.3;
maxAbsenceProb = 0.9;
Gmin = 0.01;
winSizeLocal = 3;
winSizeGlobal = 23;
winSizeFrame = 256;
};
ss: {
Gmin = 0.01;
Gmid = 0.9;
Gslope = 10.0;
};
separated: {
fS = 16000;
hopSize = 128;
nBits = 16;        
interface: {
type = "file";
path = "separated.raw";
};        
};
postfiltered: {
fS = 16000;
hopSize = 128;
nBits = 16;        
gain = 10.0;
interface: {
type = "file";
path = "postfiltered.raw";
};        
};
};
classify:
{
frameSize = 4096;
winSize = 3;
tauMin = 88;
tauMax = 551;
deltaTauMax = 20;
alpha = 0.3;
gamma = 0.05;
phiMin = 0.5;
r0 = 0.2;    
category: {
format = "undefined";
interface: {
type = "blackhole";
}
}
}

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