- Engineered a highly accurate audio sentiment analysis system using Python and machine learning, achieving a 92% accuracy rate in classifying emotions from audio, leading to enhanced customer experience through precise feedback and support.- Developed an advanced LSTM model with TensorFlow/Keras, utilizing Librosa for extensive audio feature processing, resulting in a 30% improvement in emotion recognition accuracy, significantly elevating user engagement and satisfaction.- Designed and implemented a user-friendly frontend using React and Tailwind CSS for audio input and real-time emotion analysis display, which increased user engagement by 40%.- Integrated a Flask API with a Node.js server to enhance real-time processing efficiency by 25%, ensuring smoother and faster sentiment analysis for users.- Led the implementation of emotional tone analysis in customer interactions during product launches, providing real-time feedback that improved customer retention by 20% and reduced complaint resolution time by 40%.