Name
Title | ||
|---|---|---|
DEEP CONVOLUTIONAL AND RECURRENT NETWORKS FOR POLYPHONIC INSTRUMENT CLASSIFICATION FROM MONOPHONIC RAW AUDIO WAVEFORMS |
Abstract | ||
|---|---|---|
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 classifiers with low computational cost and only minimal preprocessing. We obtain competitive classification scores and useful instrumen-wise insight through the IRMAS test set, utilizing a parallel CNNBiGRU model with multiple residual connections, while maintaining a significantly reduced number of trainable parameters. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413479 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Raw Waveforms, End-to-End Learning, Polyphonic Music, Instrument Classification | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kleanthis Avramidis | 1 | 0 | 1.01 |
| Agelos Kratimenos | 2 | 0 | 0.34 |
| Christos Garoufis | 3 | 1 | 2.39 |
| Athanasia Zlatintsi | 4 | 8 | 2.57 |
| Petros Maragos | 5 | 3733 | 591.97 |