Project 35 Recorder Web Application Overview 🎙️

Introduction 🚀

Greetings, we are Project 35, thrilled to showcase our Java project, Rekoda. Today, we'll delve into the purpose of this web application in relation to our capstone project, Sentience, a Unity-based game centering on mental illness with a unique feature—Speech Emotion Recognition (SER) powered by Python and Scikit Learn library.

Key Features 🌟

• Speech Emotion Recognition (SER) 🔊💖:

  Utilizing Python and Psychit Learn, our game captures players' emotions through voice, influencing in-game elements like weather. SER currently boasts a 74% accuracy with a dataset of 1000 voice samples.

• Challenge 🚧:

  The accuracy hurdle in SER prompted us to develop a web application. This Angular-based app collects voice samples from participants, enhancing SCR Engine's machine-learning dataset for more accurate emotion classifications.

Architecture 🏗️

• Data Flow 📤:

  The recording application captures sound using RecordRTC JavaScript Library, converts it to Base64, and passes it to the Spring Boot backend.

• Storage 💾:

  MongoDB stores the data, including audio files saved as WAV. The front end updates in real-time, displaying the latest recordings.

Demo 🎥

• Components 🧩:

  The web app comprises Record, Playlist, and Sign-In components, offering seamless recording, playback, and user interactions.

• Recording 🎙️:

  Utilizing Web Audio API, the app provides a real-time audio visualizer for users to record and play back their voice samples. The Base64 storage ensures efficient use of resources.

• Playlist ⏯️:

  Users can access a list of past recordings, play them, and delete if needed. The service TypeScript facilitates smooth interactions with the Spring Boot API.

• Sign-In 🌠:

  A component to gather user information for improved connectivity and interaction.

Next Steps 🚀

• Storage Enhancement 🗂️:

  Explore options like S3 bucket or Azure disk storage to persistently store user-uploaded audio samples.

• Security Implementation 🛡️:

  Allow users to choose whether to submit or delete their recordings, ensuring data privacy.

• TensorFlow Integration 🤖:

  Implement TensorFlow.js for improved quality control in recognizing speech and analyzing voice samples.

Closing Remarks 🌐

Project 35 Recorder is a testament to our commitment to overcoming challenges and contributing to the realm of full-stack development. We appreciate your time and welcome any questions or comments. Thank you for joining us on this journey.