abishek-as/Audio-Classification-Deep-Learning
We'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. As a result, the accuracy, training time, and prediction time of each model are compared. This is explained by model deployment, which allows users to load the desired sound output for each model that is successfully deployed, as will be addressed in more depth later.
This project helps deaf or hard-of-hearing individuals understand their surroundings by analyzing audio recordings. Users can upload sound files (like .wav files), and the system classifies them into categories such as 'dog barking', 'sirens', or 'children playing'. The primary users are deaf people or their caregivers who want to interpret ambient sounds.
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Use this if you are a deaf individual or someone assisting them, and you want a tool to interpret unknown sounds from an audio file into clear, descriptive categories.
Not ideal if you are looking for real-time sound interpretation or a comprehensive, professional-grade sound analysis solution for very diverse or highly nuanced audio inputs.
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Mar 08, 2022
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