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Sound Event Detection and Audio Classification tasks are traditionallyaddressed through time-frequency representations of audio signals such asspectrograms. However, the emergence of deep neural networks as efficientfeature extractors has enabled the direct use of audio signals forclassification purposes. In this paper, we attempt to recognize musicalinstruments in polyphonic audio by only feeding their raw waveforms into deeplearning models. Various recurrent and convolutional architecturesincorporating residual connections are examined and parameterized in order tobuild end-to-end classi-fiers with low computational cost and only minimalpreprocessing. We obtain competitive classification scores and usefulinstrument-wise insight through the IRMAS test set, utilizing a parallelCNN-BiGRU model with multiple residual connections, while maintaining asignificantly reduced number of trainable parameters.

Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms