FAQ

Frequently asked questions

Pixel

IC

The Fast Pixelwise Analysis of Music Document, Classifying job is failing.

The most probable thing happening is that your number of output ports doesn't match your number of input ports. If you have five input ports (Image, Model 1, Model 2, Model 3, Background Model), you should have four output ports (Layer 1, Layer 2, Layer 3, Background layer). Even if you're only using Layer 1 in the Interactive Classifier workflow, the job will fail if it doesn't have the correct number of output ports.

I've finalized one run of classification and am using that data to classify another folio, but none of my previously classified glyphs are showing up in the 'Classified Glyphs' tab.

Two possible things could be happening:

• You haven't added the 'Gamera XML - Training data' input port to the Interactive Classifier job. Add that port and assign it the 'Training data' file from your last IC workflow. That should fix the problem!
• You're mistakenly using your 'Classified Glyphs' in the 'Gamera XML - Training data' input port. Use the 'Training data' file instead and the 'Classified Glyphs' tab should be full!

Weirdly shaped glyphs are being classified as neumes that they really don't look like.

The first thing to do here is to check the glyphs in the 'Classified Glyphs' tab to make sure that you haven't mistakenly classified a weird glyph as a neume. If all the classified neumes in that specific category look pristine, then there are two things that can help:

• One possible issue is that that specific neume class is just too big. If one neume category has significantly more neumes in it than the others (like if you have, say, 100 examples of puncta, but only 5 examples of podati), the IC will tend to gravitate towards that "heavier" category if it's not sure. It's best to try to keep all the neume categories as equal as possible.
• Another potential problem is that the weirdly shaped glyphs have nowhere to go. The IC has to classify every glyph it encounters; it doesn't know to dismiss a weird neume unless we teach it to. The way to do this is to create 'skip' classes. You can create several: skip.dot for dots, skip.patch for patches, skip.line for horizontal lines. Unlike the neume classes, where the name is extremely important, you can name the skip classes whatever you want! The whole point of them is that they don't have an assigned code in the MEI encoding file, so they'll disappear. After all this, it's important to remember that the IC will eventually reach a point where it can't do better, regardless of how much more data you give it. When you recognize that you've reached that point, you'll know it's time to stop. Future mistakes can be fixed in Neon!

End-to-end OMR

The Fast Pixelwise Analysis of Music Document, Classifying job is failing.

The most probable thing happening is that your number of output ports doesn't match your number of input ports. If you have five input ports (Image, Model 1, Model 2, Model 3, Background Model), you should have four output ports (Layer 1, Layer 2, Layer 3, Background layer). Even if you're only using Layer 1 in the Interactive Classifier workflow, the job will fail if it doesn't have the correct number of output ports.

Some of the layers coming out of Fast Pixelwise Analysis of Music Document, Classifying are completely empty.

The first thing to do is check that the output ports of the Fast Pixelwise job are correctly assigned to each column: layer 1 should go in the column on the left, layer 2 should go in the middle column, and layer 3 should go in the column on the right. The background layer shouldn't connect to anything. If the ports are correctly assigned, then things might be trickier. One common problem is if the specific folio you're using is particularly damaged or faded; that can make it very difficult for the layer separator to do a good job. Check if other folios also produce empty layers; if not, then you might just have to correct the faded folio manually in Neon. If other folios are also not working, then either your layer separation training data isn't sufficient, or there's a problem with the job. Please report all issues here.

When I upload the file to Neon, there are a lot of glyphs on the margins of the page, or in other places that are not on the staff.

This is happening because "noise" on the page, like stains or bleed-through or creases, is being included in your layer 1 and then classified as neumes by the NIC. The longer solution is to go back to the Interactive Classifier step and refine the training data to exclude the noise as much as possible (this other FAQ could be useful). If you prefer to stay within the end-to-end OMR stage (fair), then you can change the settings of the Despeckle job of the left-most column. The purpose of that job is to reject any element of layer 1 that is smaller than a number of pixels you specify. The default number is 1, which means that every element, no matter how small, makes it through to the NIC. If you make that number higher, you'll be getting rid of a lot of small elements of noise. The ideal number really varies from manuscript to manuscript, so we recommend starting at 25 and going from there.