-
Notifications
You must be signed in to change notification settings - Fork 0
/
1_noise_reduction.py
52 lines (41 loc) · 1.85 KB
/
1_noise_reduction.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
import os
from pydub import AudioSegment
import numpy as np
from scipy.signal import butter, lfilter
def butter_lowpass(cutoff, fs, order=3):
nyquist = 0.5 * fs
normal_cutoff = cutoff / nyquist
b, a = butter(order, normal_cutoff, btype='low', analog=False)
return b, a
def apply_lowpass_filter(data, cutoff_freq, fs, order=3):
b, a = butter_lowpass(cutoff_freq, fs, order=order)
y = lfilter(b, a, data)
return y.astype(np.int16)
def noise_reduction_with_lowpass(audio_path, output_path, cutoff_freq=2000, order=3):
# Create output folder if it doesn't exist
if not os.path.exists(output_path):
os.makedirs(output_path)
# List all audio files in the input folder
audio_files = [f for f in os.listdir(audio_path) if f.endswith(('.wav', '.mp3', '.ogg'))]
for file in audio_files:
# Load audio file
audio = AudioSegment.from_file(os.path.join(audio_path, file))
# Convert audio to numpy array
audio_data = np.array(audio.get_array_of_samples())
# Apply low-pass filter for noise reduction
filtered_audio_data = apply_lowpass_filter(audio_data, cutoff_freq, audio.frame_rate, order)
# Convert back to audio
cleaned_audio = AudioSegment(
filtered_audio_data.tobytes(),
frame_rate=audio.frame_rate,
sample_width=audio.sample_width,
channels=audio.channels
)
# Save cleaned audio in the output folder
cleaned_audio.export(os.path.join(output_path, f"cleaned_{file}"), format="wav")
print(f"Cleaned audio saved to {os.path.join(output_path, f'cleaned_{file}')}")
# Example usage
if __name__ == "__main__":
audio_folder = "D:/MCA/4th sem/SER3/dataset/Actor_01"
output_folder = "D:/MCA/4th sem/SER3/output/Actor_01"
noise_reduction_with_lowpass(audio_folder, output_folder)