Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

After having revolutionized image and speech processing, convolu- tional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples of the most prominent drum set instru- ments (bass, snare, hi-hat). We use the Mel scale log magnitudes (MLS) as a representation for the input of the CNN. We compare the classification results of 1) a CNN (3 conv/max-pool layers and 2 fully connected layers) without drop-out and batch normalization vs. three variants, 2) with drop-out, 3) with batch normalization (BN), and 4) with both drop-out and BN. The CNNs with BN yield the best classification results (97% accuracy).