| | --- |
| | title: Deep Audio Classifier Using CNN |
| | emoji: 🔉 |
| | colorFrom: green |
| | colorTo: blue |
| | sdk: gradio |
| | sdk_version: 6.5.1 |
| | app_file: app.py |
| | pinned: false |
| | license: mit |
| | short_description: Audio classification with Mel-spectrogram CNNs. |
| | models: |
| | - AIOmarRehan/CNN_Audio_Classification_Model_with_Spectrogram |
| | datasets: |
| | - AIOmarRehan/General_Audio_Dataset |
| | - AIOmarRehan/Mel_Spectrogram_Images_for_Audio_Classification |
| | --- |
| | |
| | [If you would like a detailed explanation of this project, please refer to the Medium article below.](https://medium.com/@ai.omar.rehan/building-a-clean-reliable-and-accurate-animal-classifier-using-inceptionv3-175f30fbe6f3) |
| |
|
| | --- |
| |
|
| | # Audio-Classification-Raw-Audio-to-Mel-Spectrogram-CNNs |
| | Complete end-to-end audio classification pipeline using deep learning. From raw recordings to Mel spectrogram CNNs, includes preprocessing, augmentation, dataset validation, model training, and evaluation, a reproducible blueprint for speech, environmental, or general sound classification tasks. |
| |
|
| | --- |
| |
|
| | # Audio Classification Pipeline - From Raw Audio to Mel-Spectrogram CNNs |
| |
|
| | > *“In machine learning, the model is rarely the problem, the data almost always is.”* |
| | > - A reminder I kept repeating to myself while building this project. |
| |
|
| | This repository contains a complete, professional, end-to-end pipeline for **audio classification using deep learning**, starting from **raw, messy audio recordings** and ending with a fully trained **CNN model** using **Mel spectrograms**. |
| |
|
| | The workflow includes: |
| |
|
| | * Raw audio loading |
| | * Cleaning & normalization |
| | * Silence trimming |
| | * Noise reduction |
| | * Chunking |
| | * Data augmentation |
| | * Mel spectrogram generation |
| | * Dataset validation |
| | * CNN training |
| | * Evaluation & metrics |
| |
|
| | It is a fully reproducible blueprint for real-world audio classification tasks. |
| |
|
| | --- |
| |
|
| | # Project Structure |
| |
|
| | Here is a quick table summarizing the core stages of the pipeline: |
| |
|
| | | Stage | Description | Output | |
| | | ----------------------- | -------------------------------------- | ---------------- | |
| | | **1. Raw Audio** | Unprocessed WAV/MP3 files | Audio dataset | |
| | | **2. Preprocessing** | Trimming, cleaning, resampling | Cleaned signals | |
| | | **3. Augmentation** | Pitch shift, time stretch, noise | Expanded dataset | |
| | | **4. Mel Spectrograms** | Converts audio → images | PNG/IMG files | |
| | | **5. CNN Training** | Deep model learns spectrogram patterns | `.h5` model | |
| | | **6. Evaluation** | Accuracy, F1, Confusion Matrix | Metrics + plots | |
| |
|
| | --- |
| |
|
| | # 1. Loading & Inspecting Raw Audio |
| |
|
| | The dataset is loaded from directory structure: |
| |
|
| | ```python |
| | paths = [(path.parts[-2], path.name, str(path)) |
| | for path in Path(extract_to).rglob('*.*') |
| | if path.suffix.lower() in audio_extensions] |
| | |
| | df = pd.DataFrame(paths, columns=['class', 'filename', 'full_path']) |
| | df = df.sort_values('class').reset_index(drop=True) |
| | ``` |
| |
|
| | During EDA, I computed: |
| |
|
| | * Duration |
| | * Sample rate |
| | * Peak amplitude |
| |
|
| | And visualized duration distribution: |
| |
|
| | ```python |
| | plt.hist(df['duration'], bins=30, edgecolor='black') |
| | plt.xlabel("Duration (seconds)") |
| | plt.ylabel("Number of recordings") |
| | plt.title("Audio Duration Distribution") |
| | plt.show() |
| | ``` |
| |
|
| | --- |
| |
|
| | # 2. Audio Cleaning & Normalization |
| |
|
| | Bad samples were removed, silent files filtered, and amplitudes normalized: |
| |
|
| | ```python |
| | peak = np.abs(y).max() |
| | if peak > 0: |
| | y = y / peak * 0.99 |
| | ``` |
| |
|
| | This ensures consistency and prevents the model from learning from corrupted audio. |
| |
|
| | --- |
| |
|
| | # 3. Advanced Preprocessing |
| |
|
| | Preprocessing included: |
| |
|
| | * Silence trimming |
| | * Noise reduction |
| | * Resampling → **16 kHz** |
| | * Mono conversion |
| | * 5-second chunking |
| |
|
| | ```python |
| | TARGET_DURATION = 5.0 |
| | TARGET_SR = 16000 |
| | TARGET_LENGTH = int(TARGET_DURATION * TARGET_SR) |
| | ``` |
| |
|
| | Every audio file becomes a clean, consistent chunk ready for feature extraction. |
| |
|
| | --- |
| |
|
| | # 4. Audio Augmentation |
| |
|
| | To improve generalization, I applied augmentations: |
| |
|
| | ```python |
| | augment = Compose([ |
| | Shift(min_shift=-0.3, max_shift=0.3, p=0.5), |
| | PitchShift(min_semitones=-2, max_semitones=2, p=0.5), |
| | TimeStretch(min_rate=0.8, max_rate=1.25, p=0.5), |
| | AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5) |
| | ]) |
| | ``` |
| |
|
| | Every augmented file receives a unique name to avoid collisions. |
| |
|
| | --- |
| |
|
| | # 5. Mel Spectrogram Generation |
| |
|
| | Each cleaned audio chunk is transformed into a **Mel spectrogram**: |
| |
|
| | ```python |
| | S = librosa.feature.melspectrogram( |
| | y=y, sr=SR, |
| | n_fft=N_FFT, |
| | hop_length=HOP_LENGTH, |
| | n_mels=N_MELS |
| | ) |
| | S_dB = librosa.power_to_db(S, ref=np.max) |
| | ``` |
| |
|
| | * Output: **128×128 PNG images** |
| | * Separate directories per class |
| | * Supports both original & augmented samples |
| |
|
| | These images become the CNN input. |
| |
|
| | ### ***Example of Mel Spectrogram Images*** |
| |
|
| |  |
| |
|
| | .png?generation=1763570855911665&alt=media) |
| |
|
| | --- |
| |
|
| | # 6. Dataset Validation |
| |
|
| | After spectrogram creation: |
| |
|
| | * Corrupted images removed |
| | * Duplicate hashes filtered |
| | * Filename integrity checked |
| | * Class folders validated |
| |
|
| | ```python |
| | df['file_hash'] = df['full_path'].apply(get_hash) |
| | duplicate_hashes = df[df.duplicated(subset=['file_hash'], keep=False)] |
| | ``` |
| |
|
| | This step ensures **clean, reliable** training data. |
| |
|
| | --- |
| |
|
| | # 7. Building TensorFlow Datasets |
| |
|
| | The dataset is built with batching, caching, prefetching: |
| |
|
| | ```python |
| | train_ds = tf.data.Dataset.from_tensor_slices((train_paths, train_labels)) |
| | train_ds = train_ds.map(load_and_preprocess, num_parallel_calls=AUTOTUNE) |
| | train_ds = train_ds.shuffle(1024).batch(batch_size).prefetch(AUTOTUNE) |
| | ``` |
| |
|
| | I used a simple image-level augmentation pipeline: |
| |
|
| | ```python |
| | data_augmentation = tf.keras.Sequential([ |
| | tf.keras.layers.InputLayer(input_shape=(231, 232, 4)), |
| | tf.keras.layers.RandomFlip("horizontal"), |
| | tf.keras.layers.RandomRotation(0.1), |
| | tf.keras.layers.RandomZoom(0.1), |
| | ]) |
| | ``` |
| |
|
| | --- |
| |
|
| | # 8. CNN Architecture |
| |
|
| | The CNN captures deep frequency-time patterns across Mel images. |
| |
|
| | Key features: |
| |
|
| | * Multiple Conv2D + BatchNorm blocks |
| | * Dropout |
| | * L2 regularization |
| | * Softmax output |
| |
|
| | ```python |
| | model = Sequential([ |
| | data_augmentation, |
| | Conv2D(32, (3,3), padding='same', activation='relu', kernel_regularizer=l2(weight_decay)), |
| | BatchNormalization(), |
| | MaxPooling2D((2,2)), |
| | Dropout(0.2), |
| | # ... more layers ... |
| | Flatten(), |
| | Dense(num_classes, activation='softmax') |
| | ]) |
| | ``` |
| |
|
| | --- |
| |
|
| | # 9. Training Strategy |
| |
|
| | ```python |
| | reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10) |
| | early_stopping = EarlyStopping(monitor='val_loss', patience=40, restore_best_weights=True) |
| | |
| | history = model.fit( |
| | train_ds, |
| | validation_data=val_ds, |
| | epochs=50, |
| | callbacks=[reduce_lr, early_stopping] |
| | ) |
| | ``` |
| |
|
| | The model converges smoothly while avoiding overfitting. |
| |
|
| | --- |
| |
|
| | # 10. Evaluation |
| |
|
| | Performance is evaluated using: |
| |
|
| | * Accuracy |
| | * Precision, recall, F1-score |
| | * Confusion matrix |
| | * ROC/AUC curves |
| |
|
| | ```python |
| | y_pred = np.argmax(model.predict(test_ds), axis=1) |
| | print(classification_report(y_true, y_pred, target_names=le.classes_)) |
| | ``` |
| |
|
| | Confusion matrix: |
| |
|
| | ```python |
| | sns.heatmap(confusion_matrix(y_true, y_pred), annot=True, cmap='Blues') |
| | plt.title("Confusion Matrix") |
| | plt.show() |
| | ``` |
| |
|
| | --- |
| |
|
| | # 11. Saving the Model & Dataset |
| |
|
| | ```python |
| | model.save("Audio_Model_Classification.h5") |
| | shutil.make_archive("/content/spectrograms", 'zip', "/content/spectrograms") |
| | ``` |
| |
|
| | The entire spectrogram dataset is also zipped for sharing or deployment. |
| |
|
| | --- |
| |
|
| | # Final Notes |
| |
|
| | This project demonstrates: |
| |
|
| | * How to clean & prepare raw audio at a professional level |
| | * Audio augmentation best practices |
| | * How Mel spectrograms unlock CNN performance |
| | * A full TensorFlow training pipeline |
| | * Proper evaluation, reporting, and dataset integrity |
| |
|
| | If you're working on sound recognition, speech tasks, or environmental audio detection, this pipeline gives you a **complete production-grade foundation**. |
| |
|
| | --- |
| |
|
| | # **Results** |
| | > **Note:** Click the image below to view the video showcasing the project’s results. |
| | <a href="https://files.catbox.moe/suzziy.mp4"> |
| | <img src="https://images.unsplash.com/photo-1611162616475-46b635cb6868?q=80&w=1974&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" width="400"> |
| | </a> |
| |
|
| | <hr style="border-bottom: 5px solid gray; margin-top: 10px;"> |
| |
|
| | > **Note:** If the video above is not working, you can access it directly via the link below. |
| |
|
| | [Watch Demo Video](Results/Spectrogram_CNN_Audio_Classification.mp4) |
