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from sklearn.utils import shuffle | |
from sklearn.model_selection import train_test_split | |
from sklearn.preprocessing import OneHotEncoder | |
enc = OneHotEncoder() | |
y_data = enc.fit_transform(y_data).toarray() | |
x_data, y_data = shuffle(x_data, y_data) | |
X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2,stratify=y_data,random_state=42) | |
X_train = X_train.reshape((-1,1025,87,1)) |
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import librosa | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import librosa.display | |
data, sr = librosa.load('Dataset/decrease_volume/1.wav') | |
D = np.abs(librosa.stft(data)) | |
plt.figure(figsize=(20,8)) | |
librosa.display.specshow(librosa.amplitude_to_db(D,ref=np.max),y_axis='log', x_axis='time') | |
plt.title('Spectrogram') |
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import numpy as np | |
import librosa | |
import os | |
peak_features = [] | |
peak_labels = [] | |
count = 0 | |
for dirc in sorted(os.listdir('./Dataset')): |
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import os | |
import librosa | |
import random | |
for files in os.listdir('./Dataset'): | |
count = 21 | |
for comm in os.listdir('./Dataset/'+str(files)): | |
sound = AudioSegment.from_file('./Dataset/'+str(files)+'/'+str(comm)) | |
halfway_point = len(sound) // 2 | |
for _ in range(10): |
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import pyaudio | |
import wave | |
import warnings | |
warnings.filterwarnings(action='ignore',category=FutureWarning) | |
CHUNK = 256 | |
FORMAT = pyaudio.paInt16 | |
CHANNELS = 2 |
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from google.colab import drive | |
drive.mount('/content/drive') |
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## K Nearest Neighbors | |
y_pred_knn = [] | |
## Iterate through each value in test data | |
for val in x_test: | |
euc_dis = [] | |
## Finding eucledian distance for all points in training data | |
for point in x_train: | |
euc_dis.append(((val[0]-point[0])**2+(val[1]-point[1])**2)**0.5) | |
temp_target = y_train.tolist() |
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from sklearn.neighbors import KNeighborsClassifier | |
from sklearn.metrics import accuracy_score | |
## We take a range of values for K(1 to 20) and find the accuracy | |
## so that we can visualize how accuracy changes based on value of K | |
accuracy = [] | |
for n in range(1,21): | |
clf = KNeighborsClassifier(n_neighbors = n) | |
clf.fit(x_train,y_train) | |
y_pred = clf.predict(x_test) |
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import numpy as np | |
from sklearn.cross_validation import train_test_split | |
## Retrieve features | |
X = df.values.tolist() | |
Y = [] | |
## Convert classes in Strings to Integers | |
for val in target: | |
if(val == 'Iris-setosa'): | |
Y.append(0) |
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import matplotlib.pyplot as plt | |
points_1 = df[0:50].values.tolist() | |
points_2 = df[50:100].values.tolist() | |
points_3 = df[100:].values.tolist() | |
points_1 = np.array(points_1) | |
points_2 = np.array(points_2) | |
points_3 = np.array(points_3) |