diff --git a/downsample.py b/downsample.py
new file mode 100644
index 0000000..47d1ba8
--- /dev/null
+++ b/downsample.py
@@ -0,0 +1,8 @@
+import librosa    
+import soundfile as sf
+
+y, sr = librosa.load('aeiou-espeak.wav', sr=22000)
+sf.write('downsample1.wav', y, sr)
+
+y, sr = librosa.load('aeiou-espeak.wav', sr=11000)
+sf.write('downsample2.wav', y, sr)
diff --git a/invert-phase.py b/invert-phase.py
new file mode 100644
index 0000000..22eb9c0
--- /dev/null
+++ b/invert-phase.py
@@ -0,0 +1,15 @@
+from pydub import AudioSegment
+from pydub.playback import play
+
+my_audio_file = "sine-50.wav"
+
+sound1 = AudioSegment.from_file(my_audio_file, format="wav")
+
+sound2 = sound1.invert_phase()
+sound2.export("wav/invert-phase.wav", format="wav")
+
+combined = sound1.overlay(sound2)
+combined.export("wav/output-mix-invert-phase.wav", format="wav")
+
+merged_audio = AudioSegment.from_wav("wav/output-mix-invert-phase.wav")
+play(merged_audio)
diff --git a/mix-audio.py b/mix-audio.py
new file mode 100644
index 0000000..c38e224
--- /dev/null
+++ b/mix-audio.py
@@ -0,0 +1,9 @@
+from pydub import AudioSegment
+
+my_audio_file = "wav/sine-300.wav"
+
+sound1 = AudioSegment.from_file(my_audio_file, format="wav")
+sound2 = AudioSegment.from_file(my_audio_file.replace('300', '305'), format="wav")
+
+combined = sound1.overlay(sound2)
+combined.export("wav/mixed-300-305.wav", format="wav")
diff --git a/spectrogram.py b/spectrogram.py
new file mode 100644
index 0000000..e4ebff6
--- /dev/null
+++ b/spectrogram.py
@@ -0,0 +1,26 @@
+import matplotlib.pyplot as plot
+from scipy.io import wavfile
+import argparse
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input", type=str, required=True)
+    args = parser.parse_args()
+
+    sampling_frequency, signal_data = wavfile.read(args.input)
+
+    plot.subplot(211)
+    plot.title('Waveform and spectrogram view')
+
+    plot.plot(signal_data)
+    plot.xlabel('Sample')
+    plot.ylabel('Amplitude')
+
+    plot.subplot(212)
+    plot.specgram(signal_data, Fs=sampling_frequency)
+    plot.xlabel('Time')
+    plot.ylabel('Frequency')
+    plot.show()
+
+if __name__ == "__main__":
+    main()
diff --git a/white-noise.py b/white-noise.py
new file mode 100644
index 0000000..bac144b
--- /dev/null
+++ b/white-noise.py
@@ -0,0 +1,9 @@
+from scipy.io import wavfile
+import numpy
+
+mean = 0
+std = 1 
+num_samples = 100000
+samples = numpy.random.normal(mean, std, size=num_samples)
+
+wavfile.write("white-noise.wav", 44100, samples)