  Vocal Percussion Transcription (VPT) is concerned with the automatic
detection and classification of vocal percussion sound events, allowing music
creators and producers to sketch drum lines on the fly. Classifier algorithms
in VPT systems learn best from small user-specific datasets, which usually
restrict modelling to small input feature sets to avoid data overfitting. This
study explores several deep supervised learning strategies to obtain
informative feature sets for amateur vocal percussion classification. We
evaluated the performance of these sets on regular vocal percussion
classification tasks and compared them with several baseline approaches
including feature selection methods and a speech recognition engine. These
proposed learning models were supervised with several label sets containing
information from four different levels of abstraction: instrument-level,
syllable-level, phoneme-level, and boxeme-level. Results suggest that
convolutional neural networks supervised with syllable-level annotations
produced the most informative embeddings for classification, which can be used
as input representations to fit classifiers with. Finally, we used
back-propagation-based saliency maps to investigate the importance of different
spectrogram regions for feature learning.
