Sale!

MP3: AoA

$30.00

Category:
Rate this product

ECE/CS 434 | MP3: AoA

Objective¶
In this MP, you will:

Implement algorithms to find angle of arrivals of voices using recordings from microphone arrays.
Perform triangulation over multiple AoAs to deduce user locations.
Optimize voice localization algorithms using tools from probability theory, or signal processing.
Imports & Setup
The following code cell, when run, imports the libraries you might need for this MP. Feel free to delete or import other commonly used libraries. Double check with the TA if you are unsure if a library is supported.

import numpy as np
import pandas as pd

if __name__ == ‘__main__’:
import matplotlib.pyplot as plt
plt.style.use(“seaborn”) # This sets the matplotlib color scheme to something more soothing
from IPython import get_ipython
get_ipython().run_line_magic(‘matplotlib’, ‘inline’)

# This function is used to format test results. You don’t need to touch it.
def display_table(data):
from IPython.display import HTML, display
html = “<table>”
for row in data:
html += “<tr>”
for field in row:
html += “<td><h4>%s</h4><td>”%(field)
html += “</tr>”
html += “</table>”
display(HTML(html))
Problem Description
Providing voice assistants with location information of the user can be helpful in resolving ambiguity in user commands. In this project, you will create a speaker localization algorithm using recordings from multiple voice assistant microphone arrays.

AoA Scenario
Figure 1: Application Scenario
Consider the following scenario: there are eight voice assistants around the user. We will provide you with the location of these eight devices  L0,L1,…,L7 , their microphone array configuration, and the recordings from each of these devices  D0,D1,…,D7 . Your algorithm should take  D0,D1,…D7  and  L0,L1,…L7  as input and output the location of the user  Lx .

You can tackle this problem by doing AoA on all eight devices and then use triangulation to find the user location.

Data Specification
Figure 3 shows the microphone array configuration. Each microphone array has 6 microphones indicated by green dots. They form a hexagon with mic #1 facing +x, mic #0 60 degrees counter-clockwise from mic #1, and so on. The diameter of the microphone array is  0.09218 m (the distance between mic #0 and mic #3). The sampling rate is  16000 Hz .

Four sets of data can be found in dataset#/:

├── dataset0
│   ├── 0.csv
│   ├── 1.csv
│   ├── …
│   ├── 7.csv
│   └── config.csv
├── dataset1
│   ├── …
├── dataset2
│   ├── …
└── dataset3
├── 0.csv
├── 1.csv
├── …
├── 7.csv
└── config.csv
In each directory, 0.csv through 7.csv contain data collected at each of the 8 microphone arrays. They each have 6 columns, corresponding to recorded samples from individual microphones on the mic array, with column number matching mic number. config.csv contains the microphone array coordinates. There are 8 comma-separated rows, corresponding to the (x, y) coodinates of the 8 microphone arrays. This is visualized in Figure 2 below. Note that the coordinates are in metres.

if __name__ == ‘__main__’:
array_locs = np.genfromtxt (‘dataset0/config.csv’, delimiter=”,”)
user_1_location = np.array((3.0, 1.0))

from matplotlib.patches import RegularPolygon, Circle
fig, ax = plt.subplots(2, 1, figsize=(10,16))
ax[0].set_title(“Figure 2: A visual of the setting for user 1″)
ax[0].grid(b=True, which=”major”, axis=”both”)
ax[0].set_xlim((-0.5, 6.5))
ax[0].set_xticks(np.arange(0, 7))
ax[0].set_xlabel(“x (m)”)
ax[0].set_ylim((-0.5, 5))
ax[0].set_yticks(np.arange(0, 5))
ax[0].set_ylabel(“y (m)”)
for (loc_num, (loc_x, loc_y)) in enumerate(array_locs, start=0):
ax[0].add_patch(RegularPolygon(
xy=(loc_x,loc_y),
numVertices=6,
radius=0.2,
orientation=np.pi/6
))
ax[0].text(
x=loc_x,
y=loc_y,
s=loc_num,
color=”white”,
horizontalalignment=”center”,
verticalalignment=”center”,
)
ax[0].add_patch(Circle(xy=user_1_location,radius=0.2, color=”#DB7093″))
ax[0].text(user_1_location[0], user_1_location[1], “user 1″, color=”white”, ha=”center”, va=”center”)
ax[1].set_title(“Figure 3: Microphone Array Configuration”)
ax[1].grid(b=True, which=”major”, axis=”both”)
ax[1].set_xlim((-1.5,1.5))
ax[1].set_xticks([0])
ax[1].set_ylim((-1.0,1.3))
ax[1].set_yticks([0])
ax[1].add_patch(RegularPolygon((0,0), 6, 1, np.pi/6))
for mic_i in np.arange(6):
mic_pos = np.e**(-1j * 2 * np.pi / 6 *  mic_i) \
* np.e**(1j * 2 * np.pi / 6)
ax[1].add_patch(Circle(
xy=(mic_pos.real, mic_pos.imag),
radius=0.1,
color=”#4c7d4c”
))
ax[1].text(
x=mic_pos.real,
y=mic_pos.imag,
s=mic_i,
color=”white”,
horizontalalignment=”center”,
verticalalignment=”center”,
)
ax[1].annotate(
“”,
xy=(0.42, -0.75),
xytext=(-0.42, 0.75),
arrowprops=dict(arrowstyle=”|-|”, color=”white”, lw=2)
)
ax[1].text(0.15, 0, “0.09218 m”, color=”white”, ha=”center”)
plt.show()
Your Implementation
Implement your localization algorithm in the function aoa_localization(mic_data_folder, FS, MIC_OFFSETS). Do NOT change its function signature. You are, however, free to define and use helper functions.

You are encouraged to inspect, analyze and optimize your implementation’s intermediate results using plots and outputs. You may use the provided scratch notebook (scratch.ipynb) for this purpose, and then implement the relevant algorithm in the aoa_localization function (which will be used for grading). Your implementation for aoa_localization function should NOT output any plots or data. It should only return the user’s calculated location.

def aoa_localization(mic_data_folder, FS, MIC_OFFSETS):
“””AoA localization algorithm. Write your code here.

Args:
mic_data_folder: name of folder (without a trailing slash) containing
the mic datafiles `0.csv` through `7.csv` and `config.csv`.
FS: microphone sampling frequency – 16kHz.
MIC_OFFSETS: a list of tuples of each microphone’s location relative to the center of its mic array.
This list is calculated based on the diameter(0.09218m) and geometry of the microphone array.
For example, MIC_OFFSETS[1] is [0.09218*0.5, 0]. If the location of microphone array #i is
[x_i, y_i]. Then [x_i, y_i] + MIC_OFFSETS[j] yields the absolute location of mic#j of array#i.
This is provided for your convenience and you may choose to ignore.

Returns:
The user’s location in this format (in metres): (L_x, L_y)

“””

# Your return value should be the user’s location in this format (in metres): (L_x, L_y)
return (0.0, 1.0)
Running and Testing
Use the cell below to run and test your code, and to get an estimate of your grade.

def calculate_score(calculated, expected):
calculated = np.array(calculated)
expected = np.array(expected)
distance = np.linalg.norm(calculated – expected, ord=2)
score = max(1 – (distance-1)/3, 0)
return min(score, 1)

if __name__ == ‘__main__’:
test_folder_user_1 = ‘user1_data’
test_folder_user_2 = ‘user2_data’
groundtruth = [(3.0, 1.0), (4.0, 1.0), (3.0, 1.0), (4.0, 1.0)]
MIC_OFFSETS = [(0.023,0.0399), (0.0461,0), (0.0230,-0.0399), (-0.0230,-0.0399), (-0.0461,0), (-0.0230,0.0399)]
FS = 16000 # sampling frequency

output = [[‘Dataset’, ‘Expected Output’, ‘Your Output’, ‘Grade’, ‘Points Awarded’]]
for i in range(4):
directory_name = ‘dataset{}’.format(i)
student_loc = aoa_localization(directory_name, FS, MIC_OFFSETS)
score = calculate_score(student_loc, groundtruth[i])
output.append([
str(i),
str(groundtruth[i]),
str(student_loc),
“{:2.2f}%”.format(score * 100),
“{:1.2f} / 5.0”.format(score * 5),
])

output.append([
‘<i>👻 Hidden test 1 👻</i>’,
‘<i>???</i>’,
‘<i>???</i>’,
‘<i>???</i>’,
“<i>???</i> / 10.0”])
output.append([
‘<i>…</i>’,
‘<i>…</i>’,
‘<i>…</i>’,
‘<i>…</i>’,
“<i>…</i>”])
output.append([
‘<i>👻 Hidden test 6 👻</i>’,
‘<i>???</i>’,
‘<i>???</i>’,
‘<i>???</i>’,
“<i>???</i> / 10.0”])
display_table(output)
Rubric
You will be graded on the four datasets provided to you (5 points each) and six additional datasets under different settings(10 points each). Make sure you are not over-fitting to the provided data. We will use the same code from the Running and Testing section above to grade all 10 traces of data. You will be graded on the distance between your calculated user location and ground truth. An error of upto  1 m  is tolerated (and still awarded 100% of the grade). An error of  4 m  or above will be awarded a 0 grade. Grades for errors between  1 m  and  4 m  will be scaled proportionally.

Submission Guidlines
This Jupyter notebook (MP3.ipynb) is the only file you need to submit on Gradescope. As mentioned earlier, you will only be graded using your implementation of the aoa_localization function, which should only return the calculated NOT output any plots or data. If you are working in a pair, make sure your partner is correctly added on Gradescope and that both of your names are filled in at the top of this file.

Make sure any code you added to this notebook, except for import statements, is either in a function or guarded by __main__(which won’t be run by the autograder). Gradescope will give you immediate feedback using the provided test cases. It is your responsibility to check the output before the deadline to ensure your submission runs with the autograder.

MP3: AoA
$30.00
Open chat
Need help?
Hello
Can we help?