Batches the input, adding padding for the receptive field.
For batches in the middle (not the first or last), we pad the beginning and end with values from the previous and following batches to cover the receptive field.
We can't just use zero padding for the first and last batch since bias will
be added to it, making it non-zero after the first convolution. This does
not match how same padding works, which is reset to 0 at each layer.
Instead we treat the first and last batch differently. The first batch has no
initial padding and we include extra padding from the second batch on the end
to make its length match. The final batch has no end padding and we include
extra padding from the previous batch to the beginning to make its length
match.
In most cases, the number of batches will equal ceil(input.shape[0] /
batchLength). However, in rare cases where the final batch would be shorter
than the receptive field, it is instead appended to the previous batch,
reducing the final batch size by 1.
The 2D input matrix, shaped [N, D].
The desired batch size (excluding receptive field padding). The final batch may be less or slightly more than this.
The 3D batched input, shaped [B, batchLength + RF_PAD * 2, D]
Converts the model predictions to a NoteSequence.
Probabilities of an active frame, shaped [frame,
pitch].
Probabilities of an onset, shaped [frame, pitch].
Predicted velocities in the range [0, 127], shaped
[frame, pitch].
A NoteSequence containing the transcribed piano performance.
Unbatches the input, reversing the procedure of batchInput.
The batched input matrix.
The desired batch size (excluding receptive field padding). The final batch may be less or slightly more than this.
The batched input, shaped [N, batchLength + RF_PAD * 2]
Generated using TypeDoc
Utility functions for Onsets and Frames models.
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