Thesis Story

[anthonio9]

No description provided.

[anthonio9]

Thesis story wrap-up:

• should contain a comparison to PENN when with polyphonic pitch audio to show that it does not work at all
• show that PENN with split hexaphonic input works quite well
• make a common metric for FretNet and PPN and hope for the best
• make a strong conclusion about the string confusion problem with PPN, compare the standard metrics with the Multi-Pitch specific string agnostic metrics (close the generalization gap). The monophonic audio example is great for showing the confusion.
• rethink the visualizations
  • consider making the lines NOT dotted, just make one of the lines thicker, green and yellow color scheme for the logits isn't the best, maybe try a gray scale.

[anthonio9]

FretNet has multiple evaluator classes like PitchListEvaluator or MultipitchEvaluator. Each class has an unpack() method and evaluate method. The idea now is to derive from PitchListEvaluator class, use the same unpack() function, but a completely different evaluate().

[anthonio9]

Why does the reference differ so much from estimated in time stamps, but only in the pitch_list?

[anthonio9]

As for the previous question, it seems that only every 4th timestamp is present in the predicted set. This means that FretNet is only made to handle larger buffers and latency is larger then what was designed in PPN.

[anthonio9]

Thesis story wrap-up:

* [ ]  should contain a comparison to PENN when with polyphonic pitch audio to show that it does not work at all

How should this be presented?

Plot of an example track with ground truth and predicted pitch, both over a spectrogram - This should be pretty good.
In addition to that a metric for FRMSE and FRPA would be a great addition.

[anthonio9]

Thesis Layout

Thesis should be 30-40-50 pages.

• including pictures

Introduction:

• General motivation
• Something about the results
• Introduction usually does not get into model details

Section 2:

• go over theory: CNN - methods section
• usually this is split into the background section and proposed method section
• the background section is more detailed then in papers
• background: explain the model of the original paper: PENN and FretNet
• simply discover the problem with the GuitarSet dataset

Section 3: Proposed method

• How is the model adapted to the polyphonic tracking
• Explain the RMSE / RPA - accuracy metrics, the string agnostic metrics

Section 4: experiments

• dataset explanation / description
• tell more about the problem with the GuitarSet datset

Results 5: results and analysis

• Conclusion
• Fully convolutional models or transformers
• New dataset with better ground truth is needed! [https://arxiv.org/abs/2309.09085](new dataset)

[anthonio9]

For the next meeting: the table of contents + anything extra is nice.

[anthonio9]

Main results table:
String Agnostic RMSE, String Agnostic RPA
Non-String Agnosic RMSE, Non-String Agnostic RPA,

if possible, copy String-Agnostic Note from FretNet

[anthonio9]

• methods and background section is START IT!

[anthonio9]

• explain the math behind everything that you use: Feed Forward NNs, CNNs, CQT. You don't have to talk about anything more, like transformers etc.
• background: Explain the architecture of MLP, CNNs
• Background: Explain how the network and classification of NNs, what is softmax, relu, binary cross entropy (piano roll model), categorical cross entropy (one-hot model)
• Periodicity and entropy
• Spend some time on the understanding, calculation and description of the receptive field