Adding "Urban Sound Classification" tutorial on project page website

[Palmistry2310]

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?

[vivekvardhan2810]

@Palmistry2310 assigning this issue to you.

Make sure to complete this.