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@@ -33,3 +33,99 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+noise_test/AirConditioner_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirConditioner_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirConditioner_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirConditioner_9.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirportAnnouncements_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirportAnnouncements_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirportAnnouncements_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/AirportAnnouncements_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Babble_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Babble_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Babble_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Babble_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/CopyMachine_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/CopyMachine_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Munching_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Munching_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Munching_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Munching_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Munching_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Neighbor_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Neighbor_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Neighbor_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Neighbor_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Neighbor_6.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Neighbor_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/ShuttingDoor_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/ShuttingDoor_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Typing_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/Typing_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_test/VacuumCleaner_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirConditioner_10.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirConditioner_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirConditioner_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirConditioner_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirConditioner_9.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncement_11.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_10.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/AirportAnnouncements_9.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_10.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_11.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_6.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Babble_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Bus_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Cafe_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/CafeTeria_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Car_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/CopyMachine_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/CopyMachine_10.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/CopyMachine_6.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/CopyMachine_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/CopyMachine_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Field_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Hallway_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Kitchen_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/LivingRoom_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Metro_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Munching_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Munching_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Munching_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Munching_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Munching_9.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_10.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_12.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_5.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_6.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_8.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/NeighborSpeaking_9.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Office_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Park_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Restaurant_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/ShuttingDoor_10.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/ShuttingDoor_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/ShuttingDoor_7.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/Square_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/SqueakyChair_1.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/SqueakyChair_11.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/SqueakyChair_2.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/SqueakyChair_3.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/SqueakyChair_4.wav filter=lfs diff=lfs merge=lfs -text
+noise_train/SqueakyChair_8.wav filter=lfs diff=lfs merge=lfs -text
+Synthesizer_new_data_training.ipynb filter=lfs diff=lfs merge=lfs -text

Pre_processing.py

@@ -0,0 +1,179 @@
+import os
+import torchaudio
+import torch
+import numpy as np
+import soundfile
+class AudioLoader:
+    def __init__(self, sample_rate=16000):
+        self.sample_rate = sample_rate
+
+    def load_audio(self, file_path):
+        audio, sample_rate = torchaudio.load(file_path, backend='soundfile')
+        if sample_rate != self.sample_rate:
+            audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.sample_rate)(audio)
+        return audio.squeeze(0)
+
+class STFT:
+    def __init__(self, n_fft=1024, hop_length=512, win_length=1024):
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+
+    def compute_stft(self, signal):
+        return torch.stft(signal, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=torch.hamming_window(self.win_length), return_complex=True)
+
+class SpectrogramSaver:
+    @staticmethod
+    def save_spectrogram(spectrogram, save_path):
+        torch.save(spectrogram, save_path)
+
+# class Preprocessing:
+#     def __init__(self, sample_rate=16000, n_fft=1024, hop_length=512, win_length=1024):
+#         self.loader = AudioLoader(sample_rate)
+#         self.stft = STFT(n_fft, hop_length, win_length)
+#         self.saver = SpectrogramSaver()
+#         self.fixed_length = None
+
+#     def preprocess(self, signal):
+#         spectrogram = self.stft.compute_stft(signal)
+#         real = spectrogram.real
+#         imag = spectrogram.imag
+#         combined = torch.stack((real, imag), dim=-1)  # Shape: (num_frames, num_frequency_bins, 2)
+#         return combined
+
+#     def determine_fixed_length(self, noisy_dir, clean_dir):
+#         lengths = []
+#         noisy_files = [os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.wav')]
+#         clean_files = [os.path.join(clean_dir, f) for f in os.listdir(clean_dir) if f.endswith('.wav')]
+
+#         for noisy_file, clean_file in zip(noisy_files, clean_files):
+#             noisy_audio = self.loader.load_audio(noisy_file)
+#             clean_audio = self.loader.load_audio(clean_file)
+
+#             noisy_spectrogram = self.preprocess(noisy_audio)
+#             clean_spectrogram = self.preprocess(clean_audio)
+
+#             lengths.append(noisy_spectrogram.shape[1])
+#             lengths.append(clean_spectrogram.shape[1])
+
+#         self.fixed_length = int(np.median(lengths))
+#         print(f"Determined fixed length: {self.fixed_length}")
+
+#     def create_dataset(self, noisy_dir, clean_dir, save_dir):
+#         if self.fixed_length is None:
+#             self.determine_fixed_length(noisy_dir, clean_dir)
+
+#         noisy_save_dir = os.path.join(save_dir, 'noisy')
+#         clean_save_dir = os.path.join(save_dir, 'clean')
+        
+#         if not os.path.exists(noisy_save_dir):
+#             os.makedirs(noisy_save_dir)
+#         if not os.path.exists(clean_save_dir):
+#             os.makedirs(clean_save_dir)
+
+#         noisy_files = [os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.wav')]
+#         clean_files = [os.path.join(clean_dir, f) for f in os.listdir(clean_dir) if f.endswith('.wav')]
+
+#         for noisy_file, clean_file in zip(noisy_files, clean_files):
+#             noisy_audio = self.loader.load_audio(noisy_file)
+#             clean_audio = self.loader.load_audio(clean_file)
+
+#             noisy_spectrogram = self.preprocess(noisy_audio)
+#             clean_spectrogram = self.preprocess(clean_audio)
+
+#             noisy_spectrogram = self.pad_spectrogram(noisy_spectrogram)
+#             clean_spectrogram = self.pad_spectrogram(clean_spectrogram)
+
+#             noisy_save_path = os.path.join(noisy_save_dir, f"noisy_{os.path.basename(noisy_file).split('.')[0]}.pt")
+#             clean_save_path = os.path.join(clean_save_dir, f"clean_{os.path.basename(clean_file).split('.')[0]}.pt")
+
+#             self.saver.save_spectrogram(noisy_spectrogram, noisy_save_path)
+#             self.saver.save_spectrogram(clean_spectrogram, clean_save_path)
+
+#     def pad_spectrogram(self, spectrogram):
+#         pad_length = self.fixed_length - spectrogram.shape[1]
+#         if pad_length > 0:
+#             pad = torch.zeros((spectrogram.shape[0], pad_length, spectrogram.shape[2]))
+#             spectrogram = torch.cat((spectrogram, pad), dim=1)
+#         elif pad_length < 0:
+#             spectrogram = spectrogram[:, :self.fixed_length, :]
+#         return spectrogram
+class Preprocessing:
+    def __init__(self, sample_rate, n_fft, hop_length, win_length):
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.fixed_length = 86
+        self.stft = STFT(n_fft, hop_length, win_length)
+        self.loader = AudioLoader(sample_rate)
+
+    def preprocess(self, signal):
+        # print(f"Signal shape before STFT: {signal.shape}")  # Debug statement
+        if signal.shape[-1] == 0:
+            print("Encountered zero-length signal, skipping...")
+            return None  # Skip this signal
+        spectrogram = self.stft.compute_stft(signal)
+        real = spectrogram.real
+        imag = spectrogram.imag
+        return torch.stack((real, imag), dim=-1)
+
+    def determine_fixed_length(self, noisy_dir, clean_dir):
+        lengths = []
+        for noisy_file, clean_file in zip(sorted(os.listdir(noisy_dir)), sorted(os.listdir(clean_dir))):
+            noisy_audio = self.loader.load_audio(os.path.join(noisy_dir, noisy_file))
+            clean_audio = self.loader.load_audio(os.path.join(clean_dir, clean_file))
+
+            # print(f"Noisy audio shape: {noisy_audio.shape}, Clean audio shape: {clean_audio.shape}")  # Debug statement
+
+            noisy_spectrogram = self.preprocess(noisy_audio)
+            clean_spectrogram = self.preprocess(clean_audio)
+
+            if noisy_spectrogram is None or clean_spectrogram is None:
+                continue  # Skip any zero-length signals
+
+            lengths.append(noisy_spectrogram.shape[1])
+            lengths.append(clean_spectrogram.shape[1])
+        
+        if lengths:
+            self.fixed_length = min(lengths)
+            print(f"Determined fixed length: {self.fixed_length}")  # Debug statement
+        else:
+            print("No valid spectrograms found.")  # If no valid data is found
+
+    def create_dataset(self, noisy_dir, clean_dir, save_dir):
+        if self.fixed_length is None:
+            self.determine_fixed_length(noisy_dir, clean_dir)
+
+        noisy_save_dir = os.path.join(save_dir, 'noisy')
+        clean_save_dir = os.path.join(save_dir, 'clean')
+        os.makedirs(noisy_save_dir, exist_ok=True)
+        os.makedirs(clean_save_dir, exist_ok=True)
+
+        for noisy_file, clean_file in zip(sorted(os.listdir(noisy_dir)), sorted(os.listdir(clean_dir))):
+            noisy_audio = self.loader.load_audio(os.path.join(noisy_dir, noisy_file))
+            clean_audio = self.loader.load_audio(os.path.join(clean_dir, clean_file))
+
+            noisy_spectrogram = self.preprocess(noisy_audio)
+            clean_spectrogram = self.preprocess(clean_audio)
+
+            if noisy_spectrogram is None or clean_spectrogram is None:
+                continue  # Skip any zero-length signals
+
+            noisy_spectrogram = noisy_spectrogram[:, :self.fixed_length, :]
+            clean_spectrogram = clean_spectrogram[:, :self.fixed_length, :]
+
+            torch.save(noisy_spectrogram, os.path.join(noisy_save_dir, os.path.basename(noisy_file).replace('.wav', '.pt')))
+            torch.save(clean_spectrogram, os.path.join(clean_save_dir, os.path.basename(clean_file).replace('.wav', '.pt')))
+
+            # print(f"Processed and saved {noisy_file} and {clean_file}")  # Debug statement
+
+
+# # Example usage
+# if __name__ == "__main__":
+#     noisy_dir = "/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/Babble_noise_speech_train"
+#     clean_dir = "/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/clean_train"
+#     save_dir = "/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/preprocessed_data"
+
+#     preprocessor = Preprocessing(sample_rate=16000, n_fft=1024, hop_length=512, win_length=1024)
+#     preprocessor.create_dataset(noisy_dir, clean_dir, save_dir)

Pre_processing_test.py

@@ -0,0 +1,89 @@
+import os
+import torchaudio
+import torch
+import numpy as np
+import soundfile
+class AudioLoader:
+    def __init__(self, sample_rate=16000):
+        self.sample_rate = sample_rate
+
+    def load_audio(self, file_path):
+        audio, sample_rate = torchaudio.load(file_path,backend='soundfile')
+        if sample_rate != self.sample_rate:
+            audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.sample_rate)(audio)
+        return audio.squeeze(0)
+
+class STFT:
+    def __init__(self, n_fft=1024, hop_length=512, win_length=1024):
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+
+    def compute_stft(self, signal):
+        return torch.stft(signal, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=torch.hamming_window(self.win_length), return_complex=True)
+
+class SpectrogramSaver:
+    @staticmethod
+    def save_spectrogram(spectrogram, save_path):
+        torch.save(spectrogram, save_path)
+
+class Preprocessing:
+    def __init__(self, sample_rate=16000, n_fft=1024, hop_length=512, win_length=1024):
+        self.loader = AudioLoader(sample_rate)
+        self.stft = STFT(n_fft, hop_length, win_length)
+        self.saver = SpectrogramSaver()
+        self.fixed_length = None
+
+    def preprocess(self, signal):
+        spectrogram = self.stft.compute_stft(signal)
+        real = spectrogram.real
+        imag = spectrogram.imag
+        combined = torch.stack((real, imag), dim=-1)  # Shape: (num_frames, num_frequency_bins, 2)
+        return combined
+
+    def determine_fixed_length(self, noisy_dir):
+        lengths = []
+        noisy_files = [os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.wav')]
+
+        for noisy_file in noisy_files:
+            noisy_audio = self.loader.load_audio(noisy_file)
+            noisy_spectrogram = self.preprocess(noisy_audio)
+            lengths.append(noisy_spectrogram.shape[1])
+
+        self.fixed_length = int(np.median(lengths))
+        print(f"Determined fixed length: {self.fixed_length}")
+
+    def create_dataset(self, noisy_dir, save_dir):
+        if self.fixed_length is None:
+            self.determine_fixed_length(noisy_dir)
+
+        noisy_save_dir = os.path.join(save_dir, 'noisy')
+        
+        if not os.path.exists(noisy_save_dir):
+            os.makedirs(noisy_save_dir)
+
+        noisy_files = [os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.wav')]
+
+        for noisy_file in noisy_files:
+            noisy_audio = self.loader.load_audio(noisy_file)
+            noisy_spectrogram = self.preprocess(noisy_audio)
+            noisy_spectrogram = self.pad_spectrogram(noisy_spectrogram)
+            noisy_save_path = os.path.join(noisy_save_dir, f"noisy_{os.path.basename(noisy_file).split('.')[0]}.pt")
+            self.saver.save_spectrogram(noisy_spectrogram, noisy_save_path)
+
+    def pad_spectrogram(self, spectrogram):
+        pad_length = self.fixed_length - spectrogram.shape[1]
+        if pad_length > 0:
+            pad = torch.zeros((spectrogram.shape[0], pad_length, spectrogram.shape[2]))
+            spectrogram = torch.cat((spectrogram, pad), dim=1)
+        elif pad_length < 0:
+            spectrogram = spectrogram[:, :self.fixed_length, :]
+        return spectrogram
+
+# # Example usage for training
+# if __name__ == "__main__":
+#     noisy_dir = "/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/Babble_noise_speech_train"
+#     save_dir = "/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/preprocessed_data"
+
+#     preprocessor = Preprocessing(sample_rate=16000, n_fft=1024, hop_length=512, win_length=1024)
+#     preprocessor.create_dataset(noisy_dir, save_dir)

Synthesizer_new_data_training.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33c7ea95b1a2718d934436ba2b59a8cab7d4b72b5275b6bc8265b4dbb46d707b
+size 12666312

Synthesizer_trim_test.ipynb

@@ -0,0 +1,1223 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5adb701c-46a0-409d-a8dc-f73106c0a5db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import soundfile as sf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f68e1cc5-7592-484f-9d41-7ffa97f23e05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_with_noise(clean_dir, noise_dir, output_dir, noise_type, snr=10, median_length=44000, prefix=\"\"):\n",
+    "    clean_files = [f for f in os.listdir(clean_dir) if f.endswith('.wav')]\n",
+    "    noise_files = [f for f in os.listdir(noise_dir) if noise_type in f and f.endswith('.wav')]\n",
+    "\n",
+    "    if not os.path.exists(output_dir):\n",
+    "        os.makedirs(output_dir)\n",
+    "\n",
+    "    for clean_file in clean_files:\n",
+    "        clean_path = os.path.join(clean_dir, clean_file)\n",
+    "        clean_signal, sr = sf.read(clean_path)\n",
+    "\n",
+    "        noise_file = random.choice(noise_files)\n",
+    "        noise_path = os.path.join(noise_dir, noise_file)\n",
+    "        noise_signal, _ = sf.read(noise_path)\n",
+    "\n",
+    "        if len(noise_signal) > len(clean_signal):\n",
+    "            noise_signal = noise_signal[:len(clean_signal)]\n",
+    "        else:\n",
+    "            clean_signal = clean_signal[:len(noise_signal)]\n",
+    "\n",
+    "        noise_power = np.mean(noise_signal ** 2)\n",
+    "        clean_power = np.mean(clean_signal ** 2)\n",
+    "        desired_noise_power = clean_power / (10 ** (snr / 10))\n",
+    "        scaling_factor = np.sqrt(desired_noise_power / noise_power)\n",
+    "        noisy_signal = clean_signal + scaling_factor * noise_signal\n",
+    "\n",
+    "        # Segment and save noisy signals\n",
+    "        num_segments = len(noisy_signal) // median_length\n",
+    "        remainder = len(noisy_signal) % median_length\n",
+    "\n",
+    "        for i in range(num_segments):\n",
+    "            segment = noisy_signal[i * median_length: (i + 1) * median_length]\n",
+    "            segment_filename = f\"{prefix}_{os.path.splitext(clean_file)[0]}_segment{i}.wav\"\n",
+    "            segment_filepath = os.path.join(output_dir, segment_filename)\n",
+    "            sf.write(segment_filepath, segment, sr)\n",
+    "\n",
+    "        if remainder > 0:\n",
+    "            remainder_segment = noisy_signal[-remainder:]\n",
+    "            segment_filename = f\"{prefix}_{os.path.splitext(clean_file)[0]}_segment{num_segments}.wav\"\n",
+    "            segment_filepath = os.path.join(output_dir, segment_filename)\n",
+    "            sf.write(segment_filepath, remainder_segment, sr)\n",
+    "\n",
+    "        print(f\"Processed and saved noisy speech segments for {clean_file}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f66ea6da-a8e2-4729-b33d-b8474418dd09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed and saved noisy speech segments for clnsp652.wav.\n",
+      "Processed and saved noisy speech segments for clnsp120.wav.\n",
+      "Processed and saved noisy speech segments for clnsp303.wav.\n",
+      "Processed and saved noisy speech segments for clnsp547.wav.\n",
+      "Processed and saved noisy speech segments for clnsp947.wav.\n",
+      "Processed and saved noisy speech segments for clnsp746.wav.\n",
+      "Processed and saved noisy speech segments for clnsp343.wav.\n",
+      "Processed and saved noisy speech segments for clnsp661.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1025.wav.\n",
+      "Processed and saved noisy speech segments for clnsp754.wav.\n",
+      "Processed and saved noisy speech segments for clnsp972.wav.\n",
+      "Processed and saved noisy speech segments for clnsp640.wav.\n",
+      "Processed and saved noisy speech segments for clnsp923.wav.\n",
+      "Processed and saved noisy speech segments for clnsp338.wav.\n",
+      "Processed and saved noisy speech segments for clnsp507.wav.\n",
+      "Processed and saved noisy speech segments for clnsp634.wav.\n",
+      "Processed and saved noisy speech segments for clnsp335.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1068.wav.\n",
+      "Processed and saved noisy speech segments for clnsp215.wav.\n",
+      "Processed and saved noisy speech segments for clnsp123.wav.\n",
+      "Processed and saved noisy speech segments for clnsp58.wav.\n",
+      "Processed and saved noisy speech segments for clnsp75.wav.\n",
+      "Processed and saved noisy speech segments for clnsp268.wav.\n",
+      "Processed and saved noisy speech segments for clnsp952.wav.\n",
+      "Processed and saved noisy speech segments for clnsp515.wav.\n",
+      "Processed and saved noisy speech segments for clnsp558.wav.\n",
+      "Processed and saved noisy speech segments for clnsp598.wav.\n",
+      "Processed and saved noisy speech segments for clnsp581.wav.\n",
+      "Processed and saved noisy speech segments for clnsp816.wav.\n",
+      "Processed and saved noisy speech segments for clnsp505.wav.\n",
+      "Processed and saved noisy speech segments for clnsp366.wav.\n",
+      "Processed and saved noisy speech segments for clnsp525.wav.\n",
+      "Processed and saved noisy speech segments for clnsp743.wav.\n",
+      "Processed and saved noisy speech segments for clnsp633.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1079.wav.\n",
+      "Processed and saved noisy speech segments for clnsp962.wav.\n",
+      "Processed and saved noisy speech segments for clnsp26.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1034.wav.\n",
+      "Processed and saved noisy speech segments for clnsp605.wav.\n",
+      "Processed and saved noisy speech segments for clnsp490.wav.\n",
+      "Processed and saved noisy speech segments for clnsp259.wav.\n",
+      "Processed and saved noisy speech segments for clnsp651.wav.\n",
+      "Processed and saved noisy speech segments for clnsp282.wav.\n",
+      "Processed and saved noisy speech segments for clnsp175.wav.\n",
+      "Processed and saved noisy speech segments for clnsp92.wav.\n",
+      "Processed and saved noisy speech segments for clnsp773.wav.\n",
+      "Processed and saved noisy speech segments for clnsp144.wav.\n",
+      "Processed and saved noisy speech segments for clnsp399.wav.\n",
+      "Processed and saved noisy speech segments for clnsp444.wav.\n",
+      "Processed and saved noisy speech segments for clnsp321.wav.\n",
+      "Processed and saved noisy speech segments for clnsp553.wav.\n",
+      "Processed and saved noisy speech segments for clnsp726.wav.\n",
+      "Processed and saved noisy speech segments for clnsp169.wav.\n",
+      "Processed and saved noisy speech segments for clnsp845.wav.\n",
+      "Processed and saved noisy speech segments for clnsp619.wav.\n",
+      "Processed and saved noisy speech segments for clnsp141.wav.\n",
+      "Processed and saved noisy speech segments for clnsp253.wav.\n",
+      "Processed and saved noisy speech segments for clnsp790.wav.\n",
+      "Processed and saved noisy speech segments for clnsp756.wav.\n",
+      "Processed and saved noisy speech segments for clnsp504.wav.\n",
+      "Processed and saved noisy speech segments for clnsp256.wav.\n",
+      "Processed and saved noisy speech segments for clnsp728.wav.\n",
+      "Processed and saved noisy speech segments for clnsp529.wav.\n",
+      "Processed and saved noisy speech segments for clnsp714.wav.\n",
+      "Processed and saved noisy speech segments for clnsp242.wav.\n",
+      "Processed and saved noisy speech segments for clnsp788.wav.\n",
+      "Processed and saved noisy speech segments for clnsp555.wav.\n",
+      "Processed and saved noisy speech segments for clnsp112.wav.\n",
+      "Processed and saved noisy speech segments for clnsp842.wav.\n",
+      "Processed and saved noisy speech segments for clnsp713.wav.\n",
+      "Processed and saved noisy speech segments for clnsp766.wav.\n",
+      "Processed and saved noisy speech segments for clnsp317.wav.\n",
+      "Processed and saved noisy speech segments for clnsp306.wav.\n",
+      "Processed and saved noisy speech segments for clnsp354.wav.\n",
+      "Processed and saved noisy speech segments for clnsp762.wav.\n",
+      "Processed and saved noisy speech segments for clnsp883.wav.\n",
+      "Processed and saved noisy speech segments for clnsp353.wav.\n",
+      "Processed and saved noisy speech segments for clnsp255.wav.\n",
+      "Processed and saved noisy speech segments for clnsp542.wav.\n",
+      "Processed and saved noisy speech segments for clnsp196.wav.\n",
+      "Processed and saved noisy speech segments for clnsp585.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1013.wav.\n",
+      "Processed and saved noisy speech segments for clnsp38.wav.\n",
+      "Processed and saved noisy speech segments for clnsp989.wav.\n",
+      "Processed and saved noisy speech segments for clnsp219.wav.\n",
+      "Processed and saved noisy speech segments for clnsp318.wav.\n",
+      "Processed and saved noisy speech segments for clnsp994.wav.\n",
+      "Processed and saved noisy speech segments for clnsp180.wav.\n",
+      "Processed and saved noisy speech segments for clnsp698.wav.\n",
+      "Processed and saved noisy speech segments for clnsp665.wav.\n",
+      "Processed and saved noisy speech segments for clnsp758.wav.\n",
+      "Processed and saved noisy speech segments for clnsp674.wav.\n",
+      "Processed and saved noisy speech segments for clnsp452.wav.\n",
+      "Processed and saved noisy speech segments for clnsp94.wav.\n",
+      "Processed and saved noisy speech segments for clnsp314.wav.\n",
+      "Processed and saved noisy speech segments for clnsp49.wav.\n",
+      "Processed and saved noisy speech segments for clnsp986.wav.\n",
+      "Processed and saved noisy speech segments for clnsp829.wav.\n",
+      "Processed and saved noisy speech segments for clnsp258.wav.\n",
+      "Processed and saved noisy speech segments for clnsp624.wav.\n",
+      "Processed and saved noisy speech segments for clnsp3.wav.\n",
+      "Processed and saved noisy speech segments for clnsp231.wav.\n",
+      "Processed and saved noisy speech segments for clnsp573.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1046.wav.\n",
+      "Processed and saved noisy speech segments for clnsp809.wav.\n",
+      "Processed and saved noisy speech segments for clnsp84.wav.\n",
+      "Processed and saved noisy speech segments for clnsp527.wav.\n",
+      "Processed and saved noisy speech segments for clnsp965.wav.\n",
+      "Processed and saved noisy speech segments for clnsp583.wav.\n",
+      "Processed and saved noisy speech segments for clnsp682.wav.\n",
+      "Processed and saved noisy speech segments for clnsp716.wav.\n",
+      "Processed and saved noisy speech segments for clnsp612.wav.\n",
+      "Processed and saved noisy speech segments for clnsp687.wav.\n",
+      "Processed and saved noisy speech segments for clnsp59.wav.\n",
+      "Processed and saved noisy speech segments for clnsp825.wav.\n",
+      "Processed and saved noisy speech segments for clnsp903.wav.\n",
+      "Processed and saved noisy speech segments for clnsp548.wav.\n",
+      "Processed and saved noisy speech segments for clnsp245.wav.\n",
+      "Processed and saved noisy speech segments for clnsp371.wav.\n",
+      "Processed and saved noisy speech segments for clnsp233.wav.\n",
+      "Processed and saved noisy speech segments for clnsp827.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1023.wav.\n",
+      "Processed and saved noisy speech segments for clnsp336.wav.\n",
+      "Processed and saved noisy speech segments for clnsp236.wav.\n",
+      "Processed and saved noisy speech segments for clnsp873.wav.\n",
+      "Processed and saved noisy speech segments for clnsp646.wav.\n",
+      "Processed and saved noisy speech segments for clnsp170.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1021.wav.\n",
+      "Processed and saved noisy speech segments for clnsp940.wav.\n",
+      "Processed and saved noisy speech segments for clnsp733.wav.\n",
+      "Processed and saved noisy speech segments for clnsp30.wav.\n",
+      "Processed and saved noisy speech segments for clnsp637.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1011.wav.\n",
+      "Processed and saved noisy speech segments for clnsp736.wav.\n",
+      "Processed and saved noisy speech segments for clnsp784.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1017.wav.\n",
+      "Processed and saved noisy speech segments for clnsp410.wav.\n",
+      "Processed and saved noisy speech segments for clnsp799.wav.\n",
+      "Processed and saved noisy speech segments for clnsp915.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1035.wav.\n",
+      "Processed and saved noisy speech segments for clnsp679.wav.\n",
+      "Processed and saved noisy speech segments for clnsp82.wav.\n",
+      "Processed and saved noisy speech segments for clnsp603.wav.\n",
+      "Processed and saved noisy speech segments for clnsp602.wav.\n",
+      "Processed and saved noisy speech segments for clnsp283.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1056.wav.\n",
+      "Processed and saved noisy speech segments for clnsp358.wav.\n",
+      "Processed and saved noisy speech segments for clnsp105.wav.\n",
+      "Processed and saved noisy speech segments for clnsp91.wav.\n",
+      "Processed and saved noisy speech segments for clnsp377.wav.\n",
+      "Processed and saved noisy speech segments for clnsp854.wav.\n",
+      "Processed and saved noisy speech segments for clnsp13.wav.\n",
+      "Processed and saved noisy speech segments for clnsp731.wav.\n",
+      "Processed and saved noisy speech segments for clnsp294.wav.\n",
+      "Processed and saved noisy speech segments for clnsp620.wav.\n",
+      "Processed and saved noisy speech segments for clnsp115.wav.\n",
+      "Processed and saved noisy speech segments for clnsp331.wav.\n",
+      "Processed and saved noisy speech segments for clnsp946.wav.\n",
+      "Processed and saved noisy speech segments for clnsp214.wav.\n",
+      "Processed and saved noisy speech segments for clnsp765.wav.\n",
+      "Processed and saved noisy speech segments for clnsp43.wav.\n",
+      "Processed and saved noisy speech segments for clnsp764.wav.\n",
+      "Processed and saved noisy speech segments for clnsp950.wav.\n",
+      "Processed and saved noisy speech segments for clnsp387.wav.\n",
+      "Processed and saved noisy speech segments for clnsp545.wav.\n",
+      "Processed and saved noisy speech segments for clnsp103.wav.\n",
+      "Processed and saved noisy speech segments for clnsp954.wav.\n",
+      "Processed and saved noisy speech segments for clnsp315.wav.\n",
+      "Processed and saved noisy speech segments for clnsp463.wav.\n",
+      "Processed and saved noisy speech segments for clnsp1043.wav.\n",
+      "Processed and saved noisy speech segments for clnsp584.wav.\n",
+      "Processed and saved noisy speech segments for clnsp246.wav.\n",
+      "Processed and saved noisy speech segments for clnsp905.wav.\n",
+      "Processed and saved noisy speech segments for clnsp831.wav.\n",
+      "Processed and saved noisy speech segments for clnsp786.wav.\n",
+      "Processed and saved noisy speech segments for clnsp232.wav.\n",
+      "Processed and saved noisy speech segments for clnsp782.wav.\n",
+      "Processed and saved noisy speech segments for clnsp701.wav.\n",
+      "Processed and saved noisy speech segments for clnsp293.wav.\n",
+      "Processed and saved noisy speech segments for clnsp560.wav.\n",
+      "Processed and saved noisy speech segments for clnsp34.wav.\n",
+      "Processed and saved noisy speech segments for clnsp721.wav.\n",
+      "Processed and saved noisy speech segments for clnsp197.wav.\n",
+      "Processed and saved noisy speech segments for clnsp457.wav.\n",
+      "Processed and saved noisy speech segments for clnsp751.wav.\n",
+      "Processed and saved noisy speech segments for clnsp848.wav.\n",
+      "Processed and saved noisy speech segments for clnsp672.wav.\n",
+      "Processed and saved noisy speech segments for clnsp237.wav.\n",
+      "Processed and saved noisy speech segments for clnsp861.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp481.wav.\n",
+      "Processed and saved noisy speech segments for clnsp388.wav.\n",
+      "Processed and saved noisy speech segments for clnsp475.wav.\n",
+      "Processed and saved noisy speech segments for clnsp780.wav.\n",
+      "Processed and saved noisy speech segments for clnsp385.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp814.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp1096.wav.\n",
+      "Processed and saved noisy speech segments for clnsp723.wav.\n",
+      "Processed and saved noisy speech segments for clnsp189.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp569.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp917.wav.\n",
+      "Processed and saved noisy speech segments for clnsp629.wav.\n",
+      "Processed and saved noisy speech segments for clnsp161.wav.\n",
+      "Processed and saved noisy speech segments for clnsp895.wav.\n",
+      "Processed and saved noisy speech segments for clnsp891.wav.\n",
+      "Processed and saved noisy speech segments for clnsp521.wav.\n",
+      "Processed and saved noisy speech segments for clnsp37.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp537.wav.\n",
+      "Processed and saved noisy speech segments for clnsp486.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp896.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp939.wav.\n",
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+      "Processed and saved noisy speech segments for clnsp502.wav.\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    clean_dir = \"/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/clean_test\"\n",
+    "    noise_dir = \"/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/noise_test\"\n",
+    "    noisy_output_dir = \"/home/siddharth/Myprojects/ASR_project/Hybrid_CRN_SFANC-FxNLMS/Babble_noise_speech_test_trim\"\n",
+    "    noise_type = \"Babble\"\n",
+    "    prefix = \"Babble\"\n",
+    "\n",
+    "    median_length = 44000\n",
+    "    merge_with_noise(clean_dir, noise_dir,noisy_output_dir,noise_type,10,median_length, prefix=\"Babble\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b486102-c944-47cd-a2e0-bee00d44fe21",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

classification_network.py

@@ -0,0 +1,131 @@
+import torch
+
+class CNN2D(torch.nn.Module):
+    
+    def __init__(self, channels, conv_kernels, conv_strides, conv_padding, pool_padding, num_classes=15):
+        assert len(conv_kernels) == len(channels) == len(conv_strides) == len(conv_padding)
+        super(CNN2D, self).__init__()
+        
+        # Create convolutional blocks
+        self.conv_blocks = torch.nn.ModuleList()
+        prev_channel = 1  # Assuming the input is a grayscale image, modify if using more channels
+        
+        for i in range(len(channels)):
+            # Add stacked conv layers
+            block = []
+            for j, conv_channel in enumerate(channels[i]):
+                block.append(torch.nn.Conv2d(in_channels=prev_channel, out_channels=conv_channel, kernel_size=conv_kernels[i], stride=conv_strides[i], padding=conv_padding[i]))
+                prev_channel = conv_channel
+                # Add batch normalization
+                block.append(torch.nn.BatchNorm2d(prev_channel))
+                # Add ReLU activation
+                block.append(torch.nn.ReLU())
+            self.conv_blocks.append(torch.nn.Sequential(*block))
+
+        # Create pooling blocks
+        self.pool_blocks = torch.nn.ModuleList()
+        for i in range(len(pool_padding)):
+            # Adding Max Pool (reduces dimensions)
+            self.pool_blocks.append(torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=pool_padding[i]))
+
+        # Global pooling
+        self.global_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
+        self.linear = torch.nn.Linear(prev_channel, num_classes)
+
+    def forward(self, inwav):
+        for i in range(len(self.conv_blocks)):
+            # Apply convolutional layer
+            inwav = self.conv_blocks[i](inwav)
+            # Apply max pooling
+            if i < len(self.pool_blocks): inwav = self.pool_blocks[i](inwav)
+        # Apply global pooling
+        out = self.global_pool(inwav).squeeze()  # [batch_size, 256, 1, 1] -> [batch_size, 256]
+        out = self.linear(out)  # [batch_size, num_classes]
+        return out
+    
+class ResBlock2D(torch.nn.Module):
+    
+    def __init__(self, prev_channel, channel, conv_kernel, conv_stride, conv_pad):
+        super(ResBlock2D, self).__init__()
+        self.res = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels=prev_channel, out_channels=channel, kernel_size=conv_kernel, stride=conv_stride, padding=conv_pad),
+            torch.nn.BatchNorm2d(channel),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(in_channels=channel, out_channels=channel, kernel_size=conv_kernel, stride=conv_stride, padding=conv_pad),
+            torch.nn.BatchNorm2d(channel),
+        )
+        self.bn = torch.nn.BatchNorm2d(channel)
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        identity = x
+        x = self.res(x)
+        if x.shape[1] == identity.shape[1]:
+            x += identity
+        elif x.shape[1] > identity.shape[1]:
+            if x.shape[1] % identity.shape[1] == 0:
+                x += identity.repeat(1, x.shape[1]//identity.shape[1], 1, 1)
+            else:
+                raise RuntimeError("Dims in ResBlock needs to be divisible on the previous dims!!")
+        else:
+            if identity.shape[1] % x.shape[1] == 0:
+                identity += x.repeat(1, identity.shape[1]//x.shape[1], 1, 1)
+            else:
+                raise RuntimeError("Dims in ResBlock needs to be divisible on the previous dims!!")
+            x = identity
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+    
+class CNNRes2D(torch.nn.Module):       
+        
+    def __init__(self, channels, conv_kernels, conv_strides, conv_padding, pool_padding, num_classes=15):
+        assert len(conv_kernels) == len(channels) == len(conv_strides) == len(conv_padding)
+        super(CNNRes2D, self).__init__()
+        
+        # Create initial convolutional block
+        prev_channel = 1  # Assuming input has 1 channel, modify if needed
+        self.conv_block = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels=prev_channel, out_channels=channels[0][0], kernel_size=conv_kernels[0], stride=conv_strides[0], padding=conv_padding[0]),
+            torch.nn.BatchNorm2d(channels[0][0]),
+            torch.nn.ReLU(),
+            torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=pool_padding[0]),
+        )
+        
+        # Create residual blocks
+        prev_channel = channels[0][0]
+        self.res_blocks = torch.nn.ModuleList()
+        for i in range(1, len(channels)):
+            block = []
+            for j, conv_channel in enumerate(channels[i]):
+                block.append(ResBlock2D(prev_channel, conv_channel, conv_kernels[i], conv_strides[i], conv_padding[i]))
+                prev_channel = conv_channel
+            self.res_blocks.append(torch.nn.Sequential(*block))
+
+        # Create pooling blocks
+        self.pool_blocks = torch.nn.ModuleList()
+        for i in range(1, len(pool_padding)):
+            self.pool_blocks.append(torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=pool_padding[i]))
+
+        # Global pooling
+        self.global_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
+        self.linear = torch.nn.Linear(prev_channel, num_classes)
+
+    def forward(self, inwav):
+        inwav = self.conv_block(inwav)
+        for i in range(len(self.res_blocks)):
+            inwav = self.res_blocks[i](inwav)
+            if i < len(self.pool_blocks): inwav = self.pool_blocks[i](inwav)
+        out = self.global_pool(inwav).squeeze()
+        out = self.linear(out)
+        return out
+
+
+# # Example instantiation of the network
+# cnn2d_res = CNNRes2D(
+#     channels=[[128], [128]*2],
+#     conv_kernels=[(3, 3), (3, 3)],
+#     conv_strides=[(1, 1), (1, 1)],
+#     conv_padding=[(1, 1), (1, 1)],
+#     pool_padding=[(0, 0), (0, 0)]
+# )