|
|
--- |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
## Mel-Spectrogram Image Dataset (Generated via Custom Pipeline) |
|
|
|
|
|
> **This dataset was fully generated through my notebook |
|
|
> *“Building an Audio Classification Pipeline with DL”* available on my profile.** |
|
|
> It represents a complete end-to-end transformation from raw audio to clean, balanced Mel-spectrogram images suitable for deep learning. |
|
|
|
|
|
--- |
|
|
|
|
|
### **Dataset Summary** |
|
|
|
|
|
| Property | Description | |
|
|
| ---------------------------- | --------------------------------------------- | |
|
|
| **Number of Classes** | 13 distinct audio categories | |
|
|
| **Original Audio per Class** | ~40 raw recordings | |
|
|
| **Average Duration** | ~5 seconds per audio file | |
|
|
| **Final Images per Class** | 125 Mel-spectrogram images | |
|
|
| **Final Dataset Size** | 13 × 125 = **1625 images** | |
|
|
| **Sampling Rate** | Standardized to **16 kHz** | |
|
|
| **Audio Length** | Uniform **5-second** fixed length | |
|
|
| **Spectrogram Type** | 128-Mel frequency bins, `melspectrogram → dB` | |
|
|
|
|
|
--- |
|
|
|
|
|
### **High-Level Processing Pipeline** |
|
|
|
|
|
The dataset was built using a **fully custom preprocessing, cleaning, and augmentation pipeline**, implemented step-by-step in the notebook. |
|
|
|
|
|
#### **1. Data Ingestion** |
|
|
|
|
|
* Loaded all raw audio files from 13 folders |
|
|
* Parsed metadata (sample rate, duration, amplitude, SNR, etc.) |
|
|
|
|
|
#### **2. Cleaning & Standardization** |
|
|
|
|
|
* Removed corrupt, silent, or unreadable audio files |
|
|
* Normalized peak amplitudes |
|
|
* Trimmed silence using `librosa.effects.trim` |
|
|
* Performed noise reduction (`noisereduce`) |
|
|
* Converted all audio to **mono** |
|
|
* Resampled to **16,000 Hz** |
|
|
* Ensured each sample is **exactly 5 seconds** |
|
|
|
|
|
#### **3. Dataset Balancing** |
|
|
|
|
|
* Used augmentation for minority classes |
|
|
* Used controlled undersampling or oversampling where necessary |
|
|
* Verified all classes contain equal counts |
|
|
|
|
|
#### **4. Audio Augmentation (Used for Balancing & Variability)** |
|
|
|
|
|
Augmentations built with **audiomentations**: |
|
|
|
|
|
* Time shift |
|
|
* Pitch shift |
|
|
* Time stretching |
|
|
* Gaussian noise injection |
|
|
* Random perturbations for robustness |
|
|
|
|
|
#### **5. Splitting & Chunking** |
|
|
|
|
|
* Long samples were split into 5-second chunks |
|
|
* Shorter samples padded to match target duration |
|
|
* Ensured strict uniformity before feature extraction |
|
|
|
|
|
#### **6. Mel-Spectrogram Generation** |
|
|
|
|
|
Converted all cleaned audio files into Mel-spectrogram images using: |
|
|
|
|
|
* `n_fft = 1024` |
|
|
* `hop_length = 512` |
|
|
* `n_mels = 128` |
|
|
* Converted to decibel scale (`power_to_db`) |
|
|
* Saved images in **RGBA format** to preserve color-mapped spectral information |
|
|
|
|
|
--- |
|
|
|
|
|
### **Final Technical Description** |
|
|
|
|
|
> **“The final dataset consists of 13 audio classes, each expanded to exactly 125 Mel-spectrogram images through a rigorous pipeline of cleaning, normalization, augmentation, noise reduction, resampling, duration standardization, and feature extraction. All processing steps were implemented in my notebook *‘Building an Audio Classification Pipeline with DL,’* where raw 5-second audio recordings were transformed into high-quality Mel-spectrogram images suitable for deep learning models.”** |
|
|
|
|
|
--- |
|
|
|
|
|
### **Examples of the Images** |
|
|
|
|
|
 |
|
|
|
|
|
.png?generation=1763570855911665&alt=media) |
