---
title: Deep Audio Classifier Using CNN
emoji: π
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 6.0.0
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
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
---
# 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 β a reproducible blueprint for speech, environmental, or general sound classification tasks.
---
# Audio Classification Pipeline β From Raw Audio to Mel-Spectrogram CNNs
> *βIn machine learning, the model is rarely the problem β the data almost always is.β*
> β 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 β 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 β **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: **128Γ128 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 projectβs results.
> **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)