Deep Learning Meetup #5

This document summarizes deep learning research at Niland Music for music recommendation. It describes how Niland has moved from traditional music information retrieval techniques to deep learning approaches using convolutional neural networks. Key points include: - CNNs trained on mel-spectrograms of songs can achieve similar or better results than complex hand-engineered features and pooling techniques. - Simple pooling methods like mean, max and variance work well with CNNs, outperforming more complex approaches. - Training on larger datasets of 150k+ tracks improves results over smaller datasets. - Residual networks can further improve performance over plain convolutional networks. - More data, data augmentation, and semi-supervised techniques may provide additional gains