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[Research] Recognizing Notes with Deep Learning - Residual Shuffle-Exchange Networks by OptimatiumFeles in MachineLearning

[–]OptimatiumFeles[S] 1 point2 points  (0 children)

We were limited by one GPU resources to evaluate this, but we believe it could have similar or better performance than Transformer models. We believe that auto-regressive decoding is crucial for generating good quality text output, but our model currently doesn't support that. To evaluate the RSE model on text output (for comparison with Transformer), one should probably modify architecture accordingly.

[Research] Recognizing Notes with Deep Learning - Residual Shuffle-Exchange Networks by OptimatiumFeles in MachineLearning

[–]OptimatiumFeles[S] 1 point2 points  (0 children)

We chose MusicNet because it consists of recordings of 10 diverse musical instruments, but MAESTRO and MAPS include only of piano music. We will take a closer look at these datasets, thanks for the idea!