DF/DN on FSDD¶
[1]:
# Import necessary packages
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
[2]:
# Define color palette
sns.set(color_codes=True, style="white", context="talk", font_scale=1.5)
[3]:
def load_result(filename):
"""
Loads results from specified file
"""
input = open(filename, "r")
lines = input.readlines()
ls = []
for line in lines:
ls.append(float(line.strip()))
return ls
def produce_mean(ls):
"""
Produces means from list of 6 results
"""
ls_space = []
for i in range(int(len(ls) / 6)):
l = ls[i * 6 : (i + 1) * 6]
ls_space.append(l)
return np.mean(ls_space, axis=0)
def plot_acc(col, accs, pos):
# Plot low alpha results
for k in range(45):
col.plot(
samples_space,
accs[pos][0][k * 6 : (k + 1) * 6],
color="#e41a1c",
alpha=0.1,
)
col.plot(
samples_space,
accs[pos][1][k * 6 : (k + 1) * 6],
color="#377eb8",
alpha=0.1,
)
col.plot(
samples_space,
accs[pos][2][k * 6 : (k + 1) * 6],
color="#377eb8",
linestyle="dashed",
alpha=0.1,
)
col.plot(
samples_space,
accs[pos][3][k * 6 : (k + 1) * 6],
color="#377eb8",
linestyle="dotted",
alpha=0.1,
)
col.plot(
samples_space,
accs[pos][4][k * 6 : (k + 1) * 6],
color="#4daf4a",
alpha=0.1,
)
if pos == 0:
# Plot mean results
col.plot(
samples_space,
produce_mean(accs[pos][1]),
linewidth=5,
color="#377eb8",
label="CNN-1L",
)
col.plot(
samples_space,
produce_mean(accs[pos][0]),
linewidth=5,
color="#e41a1c",
label="RF",
)
col.plot(
samples_space,
produce_mean(accs[pos][2]),
linewidth=5,
color="#377eb8",
linestyle="dashed",
label="CNN-2L",
)
col.plot(
samples_space,
produce_mean(accs[pos][4]),
linewidth=5,
color="#4daf4a",
label="ResNet-18",
)
col.plot(
samples_space,
produce_mean(accs[pos][3]),
linewidth=5,
color="#377eb8",
linestyle="dotted",
label="CNN-5L",
)
else:
col.plot(
samples_space,
produce_mean(accs[pos][1]),
linewidth=5,
color="#377eb8",
)
col.plot(
samples_space,
produce_mean(accs[pos][0]),
linewidth=5,
color="#e41a1c",
)
col.plot(
samples_space,
produce_mean(accs[pos][4]),
linewidth=5,
color="#4daf4a",
)
col.plot(
samples_space,
produce_mean(accs[pos][2]),
linewidth=5,
color="#377eb8",
linestyle="dashed",
)
col.plot(
samples_space,
produce_mean(accs[pos][3]),
linewidth=5,
color="#377eb8",
linestyle="dotted",
)
[4]:
# Define working directory
directory = "../benchmarks/audition/"
acc_3 = [
load_result(directory + "3_class/naive_rf.txt"),
load_result(directory + "3_class/cnn32.txt"),
load_result(directory + "3_class/cnn32_2l.txt"),
load_result(directory + "3_class/cnn32_5l.txt"),
load_result(directory + "3_class/resnet18.txt"),
]
acc_8 = [
load_result(directory + "8_class/naive_rf.txt"),
load_result(directory + "8_class/cnn32.txt"),
load_result(directory + "8_class/cnn32_2l.txt"),
load_result(directory + "8_class/cnn32_5l.txt"),
load_result(directory + "8_class/resnet18.txt"),
]
acc_3_mel = [
load_result(directory + "3_class_mel/naive_rf.txt"),
load_result(directory + "3_class_mel/cnn32.txt"),
load_result(directory + "3_class_mel/cnn32_2l.txt"),
load_result(directory + "3_class_mel/cnn32_5l.txt"),
load_result(directory + "3_class_mel/resnet18.txt"),
]
acc_8_mel = [
load_result(directory + "8_class_mel/naive_rf.txt"),
load_result(directory + "8_class_mel/cnn32.txt"),
load_result(directory + "8_class_mel/cnn32_2l.txt"),
load_result(directory + "8_class_mel/cnn32_5l.txt"),
load_result(directory + "8_class_mel/resnet18.txt"),
]
acc_3_mfcc = [
load_result(directory + "3_class_mfcc/naive_rf.txt"),
load_result(directory + "3_class_mfcc/cnn32.txt"),
load_result(directory + "3_class_mfcc/cnn32_2l.txt"),
load_result(directory + "3_class_mfcc/cnn32_5l.txt"),
load_result(directory + "3_class_mfcc/resnet18.txt"),
]
acc_8_mfcc = [
load_result(directory + "8_class_mfcc/naive_rf.txt"),
load_result(directory + "8_class_mfcc/cnn32.txt"),
load_result(directory + "8_class_mfcc/cnn32_2l.txt"),
load_result(directory + "8_class_mfcc/cnn32_5l.txt"),
load_result(directory + "8_class_mfcc/resnet18.txt"),
]
accs = [acc_3, acc_8]
accs_mel = [acc_3_mel, acc_8_mel]
accs_mfcc = [acc_3_mfcc, acc_8_mfcc]
DF/DN with Spectrogram¶
[5]:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), constrained_layout=True)
fig.text(0.53, -0.05, "Number of Train Samples", ha="center")
samples_space = np.geomspace(10, 480, num=6, dtype=int)
xtitles = ["3 Classes", "8 Classes"]
yticks = [[0, 0.5, 1], [1e-1, 1e1, 1e3]]
for i, col in enumerate(ax):
col.set_xscale("log")
col.set_ylim([0, 1])
# Label x axis and plot figures
if i == 0:
col.set_ylabel("Accuracy")
col.set_title(xtitles[i])
plot_acc(col, accs, i)
if i == 0:
col.set_ylabel("Accuracy")
fig.align_ylabels(
ax[
:,
]
)
leg = fig.legend(
bbox_to_anchor=(0.53, -0.3),
bbox_transform=plt.gcf().transFigure,
ncol=3,
loc="lower center",
)
leg.get_frame().set_linewidth(0.0)
for legobj in leg.legendHandles:
legobj.set_linewidth(5.0)
plt.savefig("../paper/figures/spoken.pdf", transparent=True, bbox_inches="tight")
DF/DN with Mel-Spectrogram¶
[6]:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), constrained_layout=True)
fig.text(0.53, -0.05, "Number of Train Samples", ha="center")
samples_space = np.geomspace(10, 480, num=6, dtype=int)
xtitles = ["3 Classes", "8 Classes"]
ytitles = ["Accuracy", "Wall Time (s)"]
ylimits = [[0, 1], [1e-1, 1e7]]
yticks = [[0, 0.5, 1], [1e-1, 1e1, 1e3]]
for i, col in enumerate(ax):
col.set_xscale("log")
col.set_ylim(ylimits[0])
# Label x axis and plot figures
if i == 0:
col.set_ylabel("Accuracy")
col.set_title(xtitles[i])
plot_acc(col, accs_mel, i)
fig.align_ylabels(
ax[
:,
]
)
leg = fig.legend(
bbox_to_anchor=(0.53, -0.3),
bbox_transform=plt.gcf().transFigure,
ncol=3,
loc="lower center",
)
leg.get_frame().set_linewidth(0.0)
for legobj in leg.legendHandles:
legobj.set_linewidth(5.0)
plt.savefig("../paper/figures/mel.pdf", transparent=True, bbox_inches="tight")
DF/DN with MFCC¶
[7]:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), constrained_layout=True)
fig.text(0.53, -0.05, "Number of Train Samples", ha="center")
samples_space = np.geomspace(10, 480, num=6, dtype=int)
xtitles = ["3 Classes", "8 Classes"]
ytitles = ["Accuracy", "Wall Time (s)"]
ylimits = [[0, 1], [1e-1, 1e7]]
yticks = [[0, 0.5, 1], [1e-1, 1e1, 1e3]]
for i, col in enumerate(ax):
col.set_xscale("log")
col.set_ylim(ylimits[0])
# Label x axis and plot figures
if i == 0:
col.set_ylabel("Accuracy")
col.set_title(xtitles[i])
plot_acc(col, accs_mfcc, i)
fig.align_ylabels(
ax[
:,
]
)
leg = fig.legend(
bbox_to_anchor=(0.53, -0.3),
bbox_transform=plt.gcf().transFigure,
ncol=3,
loc="lower center",
)
leg.get_frame().set_linewidth(0.0)
for legobj in leg.legendHandles:
legobj.set_linewidth(5.0)
plt.savefig("../paper/figures/mfcc.pdf", transparent=True, bbox_inches="tight")
