Music Genre Classification Using RNN-LSTM

Developed a deep learning model using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units to classify music genres. The model was trained and evaluated on the GTZAN dataset, achieving an accuracy of 80%. The project focused on capturing temporal patterns in audio signals for effective genre identification. This work demonstrated the potential of sequence-based neural architectures in audio signal processing and music classification tasks.