diff --git a/requirements.txt b/requirements.txt
index 589adbe..af1627b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,19 +1,26 @@
 absl-py==2.1.0
+asttokens==3.0.0
 astunparse==1.6.3
 audioread==3.0.1
 certifi==2024.12.14
 cffi==1.17.1
 charset-normalizer==3.4.0
+contourpy==1.3.1
+cycler==0.12.1
 decorator==5.1.1
+executing==2.1.0
 flatbuffers==24.12.23
+fonttools==4.55.3
 gast==0.6.0
 google-pasta==0.2.0
 grpcio==1.68.1
 h5py==3.12.1
 idna==3.10
 iniconfig==2.0.0
+jedi==0.19.2
 joblib==1.4.2
 keras==3.7.0
+kiwisolver==1.4.8
 lazy_loader==0.4
 libclang==18.1.1
 librosa==0.10.2.post1
@@ -21,6 +28,7 @@ llvmlite==0.43.0
 Markdown==3.7
 markdown-it-py==3.0.0
 MarkupSafe==3.0.2
+matplotlib-inline==0.1.7
 mdurl==0.1.2
 ml-dtypes==0.4.1
 msgpack==1.1.0
@@ -30,21 +38,29 @@ numpy==2.0.2
 opt_einsum==3.4.0
 optree==0.13.1
 packaging==24.2
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.0.0
 platformdirs==4.3.6
 pluggy==1.5.0
 pooch==1.8.2
+prompt_toolkit==3.0.48
 protobuf==5.29.2
+ptyprocess==0.7.0
+pure_eval==0.2.3
 pycparser==2.22
 Pygments==2.18.0
+pyparsing==3.2.0
 pytest==8.3.4
+python-dateutil==2.9.0.post0
 requests==2.32.3
 rich==13.9.4
 scikit-learn==1.6.0
-scipy==1.14.1
 setuptools==75.6.0
 six==1.17.0
 soundfile==0.12.1
 soxr==0.5.0.post1
+stack-data==0.6.3
 tensorboard==2.18.0
 tensorboard-data-server==0.7.2
 tensorflow==2.18.0
@@ -52,8 +68,10 @@ tensorflow-hub==0.16.1
 termcolor==2.5.0
 tf_keras==2.18.0
 threadpoolctl==3.5.0
+traitlets==5.14.3
 typing_extensions==4.12.2
 urllib3==2.3.0
+wcwidth==0.2.13
 Werkzeug==3.1.3
 wheel==0.45.1
 wrapt==1.17.0
diff --git a/samples/requirements.txt b/samples/requirements.txt
index dc35a38..af1627b 100644
--- a/samples/requirements.txt
+++ b/samples/requirements.txt
@@ -17,7 +17,6 @@ grpcio==1.68.1
 h5py==3.12.1
 idna==3.10
 iniconfig==2.0.0
-ipython==8.31.0
 jedi==0.19.2
 joblib==1.4.2
 keras==3.7.0
@@ -29,7 +28,6 @@ llvmlite==0.43.0
 Markdown==3.7
 markdown-it-py==3.0.0
 MarkupSafe==3.0.2
-matplotlib==3.10.0
 matplotlib-inline==0.1.7
 mdurl==0.1.2
 ml-dtypes==0.4.1
@@ -58,7 +56,6 @@ python-dateutil==2.9.0.post0
 requests==2.32.3
 rich==13.9.4
 scikit-learn==1.6.0
-scipy==1.14.1
 setuptools==75.6.0
 six==1.17.0
 soundfile==0.12.1
diff --git a/src/preprocessing/classify.py b/src/preprocessing/classify.py
index a96ecf4..d1fc76c 100644
--- a/src/preprocessing/classify.py
+++ b/src/preprocessing/classify.py
@@ -1,13 +1,9 @@
 import tensorflow as tf
 import tensorflow_hub as hub
+import librosa
 import numpy as np
 import csv
 
-import matplotlib.pyplot as plt
-from IPython.display import Audio
-from scipy.io import wavfile
-from scipy import signal
-
 model = hub.load('https://tfhub.dev/google/yamnet/1')
 
 #Find the name of the class with the top score when mean-aggregated across frames.
@@ -24,27 +20,17 @@ def class_names_from_csv(class_map_scv_text):
 class_map_path = model.class_map_path().numpy()
 class_names = class_names_from_csv(class_map_path)
 
-# Resample audio to 16K
-def ensure_sample_rate(original_sample_rate, waveform, desired_sample_rate=16000):
-    """Resample waveform if required."""
-    if original_sample_rate != desired_sample_rate:
-        desired_length = int(round(float(len(waveform)) / original_sample_rate * desired_sample_rate))
-        waveform = signal.resample(waveform, desired_length)
-    return desired_sample_rate, waveform
-
-# wav_file_name = 'speech_whistling2.wav'
 wav_file_name = 'cafe_crowd_talk.wav'
-sample_rate, wav_data = wavfile.read(wav_file_name, 'rb')
-sample_rate, wav_data = ensure_sample_rate(sample_rate, wav_data)
+waveform, sample_rate = librosa.load(wav_file_name, sr=16000)
 
 # Show some basic information about the audio.
-duration = len(wav_data)/sample_rate
+duration = len(waveform)/sample_rate
 print(f'Sample rate: {sample_rate} Hz')
 print(f'Total duration: {duration:.2f}s')
-print(f'Size of the input: {len(wav_data)}')
+print(f'Size of the input: {len(waveform)}')
 
-# The wav_data needs to be normalized to values in [-1.0, 1.0]
-waveform = wav_data / tf.int16.max
+# The waveform needs to be normalized to values in [-1.0, 1.0] (librosa load already does this)
+# No need to do this as librosa already normalizes# The wav_data needs to be normalized to values in [-1.0, 1.0]
 
 # Execute the Model
 # Check the output.