Welcome to ML Fusion Labs! This project aims to provide an interactive platform where users can learn machine learning from scratch, explore projects, and contribute their own machine learning endeavors.
description
We need to create a machine learning model to classify urban sounds using deep learning techniques. This model will be used to identify sounds like sirens, car horns, and other urban noises. The project will involve audio feature extraction (e.g., MFCCs) and training a model to classify these features.
Tasks:
Set up development environment
Install required libraries: librosa, tensorflow, scikit-learn, matplotlib.
Set up a Python virtual environment for the project.
Explore and Load Dataset
Download the UrbanSound8K dataset.
Load metadata and audio files from the dataset.
Extract Audio Features
Use librosa to extract audio features like MFCCs for each sound file.
Write a function to handle feature extraction.
Build Deep Learning Model
Use tensorflow to build a Sequential model.
Add dense layers and compile the model.
Train the model on extracted features.
Model Evaluation and Optimization
Evaluate the model using test data.
Visualize model accuracy and loss over training epochs.
Experiment with optimizations such as CNNs or data augmentation techniques.
Model Deployment
Save the trained model.
Create a deployment script (e.g., using Flask) to serve the model for real-time classification.
@vivekv2810 can u please assign this to me under gssoc-ext, hacktoberfest accepted and level label?
description We need to create a machine learning model to classify urban sounds using deep learning techniques. This model will be used to identify sounds like sirens, car horns, and other urban noises. The project will involve audio feature extraction (e.g., MFCCs) and training a model to classify these features.
Tasks:
@vivekv2810 can u please assign this to me under gssoc-ext, hacktoberfest accepted and level label?