This project trains neural networks on audio samples to classify 8 emotional states (neutral, calm, happy, sad, angry, fearful, disgust, surprised) from speech and song recordings. It uses the RAVDESS and TESS datasets totaling 5252 samples, processing audio with librosa for feature extraction and building models with Keras and TensorFlow. The classifier achieves an 80% F1 score across all emotion classes.