End-to-end Optical Music Recognition system build on top of deep learning models and machine learning techniques. Able to transcribe on skewed and phone taken photos. The models were trained to identify Western Music Notation, which could mean the system will probably not work on transcribing hand-written scores or other notation types.
tabi.mp4
# Install from PyPi
pip install oemer
# (optional) Install the Tensorflow version.
pip install oemer[tf]
# (optional) Or install the newest updates directly from Github.
pip install git+https://github.com/BreezeWhite/oemer
# Run
oemer <path_to_image>The oemer command will output the transcribed MusicXML file and an image of analyzed elements to current directory.
With GPU, this usually takes around 3~5 minutes to finish. For the first time running, the checkpoints will be downloaded automatically and may take up to 10 minutes to download, depending on your connection speed. Checkpoints can also be manually downloaded from here. Put checkpoint files start with 1st_* to oemer/checkpoints/unet_big, 2nd_* to oemer/checkpoints/seg_net, and rename the files by removing the prefix 1st_, 2nd_.
Default to use Onnxruntime for inference. If you want to use Tensorflow for running the inference,
add --use-tf to the command and make sure there is TF installed.
Please follow the issue template, fill in all required information. Otherwise, the issue will be closed directly without further processing!
If you encounter errors, try adding --without-deskew first (see issue #9). If the problem still exists, file an issue and make sure following the template format.
usage: oemer [-h] [-o OUTPUT_PATH] [--use-tf] [--save-cache] [-d] img_path
End-to-end OMR command line tool. Receives an image as input, and outputs
MusicXML file.
positional arguments:
img_path Path to the image.
optional arguments:
-h, --help show this help message and exit
-o OUTPUT_PATH, --output-path OUTPUT_PATH
Path to output the result file. (default: ./)
--use-tf Use Tensorflow for model inference. Default is to use
Onnxruntime. (default: False)
--save-cache Save the model predictions and the next time won't
need to predict again. (default: False)
-d, --without-deskew Disable the deskewing step if you are sure the image
has no skew. (default: False)
@software{yoyo_2023_8429346,
author = {Yoyo and
Christian Liebhardt and
Sayooj Samuel},
title = {BreezeWhite/oemer: v0.1.7},
month = oct,
year = 2023,
publisher = {Zenodo},
version = {v0.1.7},
doi = {10.5281/zenodo.8429346},
url = {https://doi.org/10.5281/zenodo.8429346}
}
This section describes the detail techniques for solving the OMR problem. The overall flow can also be found in oemer/ete.py, which is also the entrypoint for oemer command.
Notice that all descriptions below are simplfied compared to the actual implementations. Only core concepts are covered.
There are two UNet models being used: one serves to separate stafflines and all other symbols, and the other for separating more detailed symbol types (see Model Prediction below). The training script is under oemer/train.py.
The two models use different datasets for training: CvcMuscima-Distortions for training the first model, and DeepScores-extended for the second model. Both trainings leverage multiple types of image augmentation techniques to enhance the robustness (see here).
To identify invidual symbol types on the predictions, SVM models are used. The data used to train SVM models are extracted from DeepScores-extended. There are three different SVM models that are used to classify symbols. More details can be found in oemer/classifier.py.
Oemer first predicts different informations with two image semantic segmentation models: one for predicting stafflines and all other symbols; and the second model for more detailed symbol informations, including noteheads, clefs, stems, rests, sharp, flat, natural.
Model one for predicting stafflines (red) and all other symbols (blue).
Model two for predicting noteheads (green), clefs/sharp/flat/natural (pink), and stems/rests (blue).
Before proceed to recognizing symbols, it is necessary to deskew the photo first, since later processes assume stafflines are all horizontally aligned, and also the position of noteheads, rests and all other things are all depending on this assumption.
For the dewarping process, it can be summarized imto six steps as shown in the below figure.
Steps to dewarp the curved image.
The dewarping map will be applied to all the predicted informations produced by the two NN models.
After dewarping, stafflines are being parsed first. This step plays the most important role
during the whole process, as this is the foundation to all later steps. Ths most important information is
unit_size, which is the interval between stafflines. It's obvious that all the size-related and
distance-related information in a music score, all relate to the interval size of stafflines.
Stafflines are processed part-by-part horizontally, as shown below:
For each part, the algorithm finds the lines by accumulating positive pixels by rows. After summarizing the amounts for each row, we get the following statistics:
The algorithm then picks all the peaks and applies additional rules to filter out false positive peaks. The final picked true positive peaks (stafflines) are marked with red dots.
Another important information is tracks and groups. For a conventional piano score, there are two tracks, for left and right hand, respectively. The two tracks futher forms a group. For this information, the algorithm uses the symbol predictions and parse the barline information to infer possible grouping of tracks.
After extraction, the informations are stored into list of Staff instances. An example
Staff instance representation is as following:
# Example instance of oemer.staffline_extraction.Staff
Staff(
Lines: 5 # Contains 5 stafflines.
Center: 1835.3095048449181 # Y-center of this block of staff.
Upper bound: 1806 # Upper bound of this block of staff (originated from left-top corner).
Lower bound: 1865 # Lower bound of this block of staff (originated from left-top corner).
Unit size: 14.282656749749265 # Average interval of stafflines.
Track: 1 # For two-handed piano score, there are two tracks.
Group: 3 # For two-handed piano score, two tracks are grouped into one.
Is interpolation: False # Is this block of staff information interpolated.
Slope: -0.0005315575840202954 # Estimated slope
)The next step is to extract noteheads, which is the second important information to be parsed.
Steps to extract noteheads are breifly illustrated in the following figure:
One of the output channel of the second model predicts the noteheads map, as can be seen in the top-middle image. The algorithm then pre-processes it with morphing to refine the information. Worth noticing here is that the model was trained to predict 'hollow' notes as solid noteheads, which thus the empty noteheads won't be eliminated by the morphing.
Next, the algorithm detects the bounding box of each noteheads. Since the noteheads could
overlap with each other, the initial detection could contain more than one notehead.
To deal with such situation, the algorithm integrates the information unit_size to approximate
how many noteheads are actually there, in both horizontal and vertical directions. The result
is shown in the bottom-left figure.
As we force the model to predict both half and whole notes to be solid noteheads, we need to setup rules to decide whether they are actually half or whole notes. This could be done by simply compare the region coverage rate between the prediction and the original image. The result is shown in the bottom-middle figure.
Finally, the last thing to be parsed is the position of noteheads on stafflines. Index 0 originates from the bottom line space (D4 for treble clef, and F3 for bass clef), higher pitch having larger index number. There could also be negative numbers. In this step, noteheads are also assigned with track and group number, indicating which stave they belong to. The bottom-right figure shows the result.
# Example instance of oemer.notehead_extraction.NoteHead
Notehead 12 ( # The number refers to note ID
Points: 123 # Number of pixels included in this notehead.
Bounding box: [649 402 669 419] # xyxy
Stem up: None # Direction of the stem, will be infered in later steps.
Track: 1 # For a two-hand piano score, this represents the left hand track.
Group: 0 # The staring group of the score.
Pitch: None # Actual pitch in MIDI number, will be infered in later steps.
Dot: False # Whether the note contains a dot.
Label: NoteType.HALF_OR_WHOLE # Initial guess of the rhythm type.
Staff line pos: 4 # Position on stafflines. Counting from D4 for treble clef.
Is valid: True # Flag for marking if the note prediction is valid.
Note group ID: None # Note group ID this note belongs to. Will be infered in later steps.
Sharp/Flat/Natural: None # Accidental type of this note. Will be infered in later steps.
)
This step groups individual noteheads into chords that should be played at the same time.
A quick snippet of the final result is shown below:
The first step is to group the noteheads according mainly to their distance vertically, and then the overlapping and a small-allowed distance horizontally.
After the initial grouping, the next is to parse the stem direction and further use this
information to refine the grouping results. Since there could be noteheads that are vertically
very close, but have different directions of stems. This indicates that there are two
different melody lines happening at the same time. This is specifically being considered
in oemer and taken care of over all the system.
# Example instance of oemer.note_group_extraction.NoteGroup
Note Group No. 0 / Group: 0 / Track: 0 :(
Note count: 1
Stem up: True
Has stem: True
)After noteheads being extracted, there remains other important musical annotations need to be parsed, such as keys, accidentals, clefs, and rests. As mentioned before, the second model predicts different pairs of symbols in the same channel for the ease of training. Additional separation of the information is thus required.
For the clefs/sfn (short for sharp, flat, natural) pair, the initial intention for grouping them together, is that it's easier to distinguish the difference through their size and the region coverage rate (tp_pixels / bounding_box_size). This is exactly what the algorithm being implemented to recognize them. After the clef/sfn classification, Further recognition leverages SVM models to classify them into the correct symbol types (e.g. gclef, sharp, flat).
# Example instance of oemer.symbol_extraction.Clef
Clef: F_CLEF / Track: 1 / Group: 1
# Example instance of oemer.symbol_extraction.Sfn
SFN: NATURAL / Note ID: 186 / Is key: False / Track: 0 / Group: 0Extracts barlines using both models' output. The algorithm first uses the second model's prediction, the channel contains rests and 'stems' (which should be 'straight lines' actually). Since the previous step while extracting note groups has already used the 'stem' information, so the rest part of unused 'stems' should be barlines. However, due to some bugs of the training dataset, the model always predicts barlines, that should be longer than stems, into the same length of stems. It is thus the algorithm needs the first model's output to extract the 'actual' barlines with real lengths. By overlapping the two different information, the algorithm can easily filter out most of non-barline objects in the prediction map. Further extraction applies additional rules to estimate barlines. The result can be seen as follow:
And the representation of a barline instance:
# Example instance of oemer.symbol_extraction.Barline
Barline / Group: 3There is no track information of barline since one barline is supposed to occupy multiple tracks.
Having used all the 'stems' information in the output channel during the last few steps, the rest symbols should be 'rests'. List of rules are also applied to filter the symbols. The recognition of the rest types are done by using trained SVM model. As a result, above process outputs the following result:
Representation of the rest instance:
# Example instance of oemer.symbol_extraction.Rest
Rest: EIGHTH / Has dot: None / Track: 1 / Group: 1This is probably the most time consuming part except for the model inference. There are two things that effect the rhythm: dot and beams/flags. The later two (beams, flags) are considered the same thing in the extraction. In this step, model one's prediction is used, including both channels (stafflines, symbols). This process updates attributes in-place.
The algorithm first parse the information of dot for each note. The symbols map is first subtracted by other prediction maps (e.g. stems, noteheads, clefs, etc.), and then use the remaining part for scanning the dots. Since the region of a dot is small, the algorithm morphs the map first. After amplifying the dot information, the algorithm scans a small region nearby every detected noteheads, calculate the ratio of positive samples to the region, and determine whether there is a dot by a given certain threshold.
Here comes the most difficult and critical part amongst all steps, since rhythm hugely influence the listening experience. Few steps are included to extract beams/flags:
- Initial parsing
- Check overlapping with noteheads and stems
- Correlate beams/flags to note groups
- Assign rhythm types to note groups and update the note grouping when neccessary.
Brief summary of these steps are illustrated as below:
The first step is, as mentioned before, to distill beams/flags from all the symbols predicted by model one. By subtracting with the second model's output, and apply some simple filtering rules, we get the top-left figure.
Next, the algorithm picks the regions that overlap with known noteheads and stems. We also get an initial relation between note groups and beams/flags. Both information are kept for later usage. As a result, the algorithm generates the top-right figure.
The third step is to refine the relation between note groups and beams. Since there could be stem of one note group that doesn't overlap with the beam above/below it, and thus not being included in the same bounding box. Here, bounding box includes both note group and beams/flags. This can be adjusted by further scans the region under the bounding box, check if there contains unknown note groups, and update the relation. Figure is shown in bottom-left.
Finally, the algorithm has all neccessary information to conclude the rhythm types for each note group now. The algorithm scans a small region for counting how many beams/flags there are. The region is bounded by the center of the x-axis of the note group, with extention to both left and right side; the y-axis by the bounding box and the boundary of the note in the note group that closest to the beams (depending on the direction of the stem). Figure on the bottom-right shows the region of bounding boxes (green), the scanning range (blue), and the final number of beams/flags detected by the algorithm. Numeber of rules are also applied to refine the counting result.
In the last step, there is another important mission is to update the note grouping, which
means further check the legitmacy of each note group, and separate them into upper and lower
part if neccessary. Since oemer takes multi-melody line into consideration, it is not
possible until we collect all the fundamental information to finally determine there is indeed multiple
melody lines in the note group. That is why in the last step here, the algorithm
checks the grouping again.
The process of building MusicXML document follows the event-based (objective used in oemer
is 'action') mechanism, which essentially means there are different event types, and each
has their own attributes and differently behaviors when being triggered.
The process goes to construct a sequence of events first, and trigger them one-by-one later.
This eventually yields a series of XML strings. A global context is shared across each events,
which plays a key role for holding the music context while decoding.
A brief summary of steps are listed:
- Sort symbols first by groups, then x-axis position.
- Initialize the first measure with clef and key information.
- Determine the alignment between notes/rests in different tracks.
- Adjust the rhythm of notes/rests or adding rests to make sure the aligned symbols are at the same beat position.
- Decode the sequence and generate the MusicXML document.
Sort all the instances previously generated by their groups and x-axis, then cluster them into measures. It's obvious this step is to mitigate how human interpret a music sheet. The status of accidentals are reset for each measure, rhythm types, chord prgression, etc.
The initial state of clef type for each track and the key type. This step includes an important algorithm: key finding. The algorithm can be split down into few steps:
-
Decide if the current measure contains key.
Check the first few occurance of symbols that are instance of
Sfn. If there isn't any, return key type of C-major. If yes, then go to the next step. -
Define the scan range.
If the current measure is at the beginning of that row (track), then the first track_nums of symbols types should be
Clef, then comes the key. Then the end of the scanning, since there are at most 6 sharps/flats of the key (ignoring some special cases that the key changes after the double barlines, which may contain naturals), this offset plus 4 as the tolerance are added to the beginning index. -
Count occurance
Count number of occurance of predicted
Sfntypes. Store this information for later process. -
Check the validity
Checks if all tracks have the same label (i.e. all flats, all sharps). If not, count the most occurance of
Sfntypes. Use this as the label type (i.e. sharp or flat). There are more advanced rules being applied in this process. Please check the source code for the details. -
Return key type
Count the occurance of
Sfninstances, use the sharp/flat information, and combine the two to determine the final key type.
Determine the alignment between different notes in different tracks. Notes being paired together (horizontally) are considered at the same beat position in that measure. In other words, notes within the same beat should have the same accumulated beats beforehand across parts. We thus can further use this assumption to adjust the rhythm type of the previous notes.
Below shows a graph of alignment results. The number means the minimum detected beat length of the track in that beat position. The commented numbers after each row (beat position) are accumulated length difference.
# Min durations of each position of the measure
# Tracks Accum. Diff.
# T1 T2
[[ 8., 24.], # 16
[ 8., 0.], # 8
[ 8., 0.], # 0
[ 0., 24.], # 24 <- need to insert an eighth rest to balance the rhythm
[ 8., 0.], # 16 ↑
[ 8., 0.], # 8 │ find that
[ 4., 4.]] # 0 <- checkpoint No.2 ┘Checkpoints occur at the row which both have number (meaning both tracks have notes). In the given example, the checkpoints will occur at row 1 and 7. Also, there be a 'mark' to indicate the rhythm in that beat position should be adjusted. The mark will point to where both tracks have number, or the next row after the accumulated difference becomes zero. In above case, the mark will point to row 1, then row 4, then row 7.
At the checkpoint (both have notes), the accumulated difference should be zero. This can be inferred easily from our assumption described in the first paragraph. If the difference is not zero, then the makred position by the 'mark' should adjust their rhythm type or adding rests to make sure the difference go down to zero. Therefore, according to the rule, the total beats in a measure will only increase, since the accumulated difference is always positive number and thus we can only 'add' beat to balance the system.