  Sound Event Detection and Audio Classification tasks are traditionally
addressed through time-frequency representations of audio signals such as
spectrograms. However, the emergence of deep neural networks as efficient
feature extractors has enabled the direct use of audio signals for
classification purposes. In this paper, we attempt to recognize musical
instruments in polyphonic audio by only feeding their raw waveforms into deep
learning models. Various recurrent and convolutional architectures
incorporating residual connections are examined and parameterized in order to
build end-to-end classi-fiers with low computational cost and only minimal
preprocessing. We obtain competitive classification scores and useful
instrument-wise insight through the IRMAS test set, utilizing a parallel
CNN-BiGRU model with multiple residual connections, while maintaining a
significantly reduced number of trainable parameters.
