CNN Audio Classification Model With Spectrogram

--- language: en license: mit tags: - audio-classification - tensorflow - mel-spectrogram-images - audio-processing inference: true datasets: - 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-complete-audio-classification-pipeline-using-deep-learning-from-raw-audio-to-mel-9894bd438d85) --- [The project is also available for testing on Hugging Face.](https://huggingface.co/spaces/AIOmarRehan/Deep_Audio_Classifier_using_CNN) --- # 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 +...