If it's only pure recorder and since it can only play one note per time, you could find the frequency using the fourier transform. Best if you use denoising before. Then you can just map the loudest frequencies to actual notes.

JOMAMA Classification Model 192

All you need to know over time is the pitch being played, which is a frequency. The audio file represents a waveform, and all you need to know is the frequency of that waveform over time. There's no need for anything sequential. 440Hz is "A" no matter where it comes in a sequence. It's A if it comes after C, and it's A if it comes after F#.

A sequential model might be useful for natural language, for example, because meaning is carried between words. "Very tall" and "Not Tall" are different things. "He was quite tall" and "No one ever accused him of not being tall" are remarkably similar things. Transcribing music is just charting the frequency over time.

That said, you cannot get the frequency from a single data point, so there is a somewhat sequential nature to things, but it's really just that you need to transform the positions of the waveform over time into frequency, which the fourier transform does. When music visualizations show you an EQ (equalizer) chart to go with your music, this is what they're doing - showing you how much of various frequencies are present at a given time in the music, using a FFT. A digital equalizer similarly transforms audio into a frequency spectrum, allows you to adjust the frequency spectrum, and then transforms back into a waveform.

Not really the music sheets, just the musical notes. I will display the proper finger position on a flute based on the musical notes predicted. I'm trying to create a learning tool.

Thank you so much for taking the time to answer my question. You're right on the first one, my goal is to transcribe music or flute music into notes. But I'm a little confused about why there's no need to use a deep learning model because I thought in the first place that I could also use sequential models. Could you elaborate on that for me? Thank you so much.

PS:I will surely look into your recommendation about FFT.

I'm a little unclear - there are three different things you might be trying to do here. The first would be transcription - taking an audio file and interpreting it into notes. That wouldn't typically require deep learning on it's own, just a fourier transform. The second would be isolating a specific instrument in an ensemble - finding just the recorder in a collection of different instruments all playing different things. The third would be generation, inferring unplayed future notes based on previous notes.

Are you wanting to transcribe, isolate, generate, or some combination?

I'm thinking you're wanting to transcribe. If that's the case, FFT (fast fourier transform) would be the algo to choose. If you google "FFT music transcription" you'll get a lot of info. https://ryan-mah.com/files/posts.amt_part_2.main.pdf

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Ligma Classification Network

This seems interesting. I'll give this a shot. Thanks!

I have got a quick guess here.. maybe can be of help to you.. take the n-1 layers weights of your first learned model (trained weights) then try finetuning with the 4 outputs and observe either your validation loss is improving.

If so, then later you can take the untrained initial weights of your first model (till n-1th layer) then trying converging them with 4 outputs. This step is mentioned such that you have got a model started training from scratch for 4 outputs but having the same initial weights for both the models.

Why am i saying this ?

Well. I think you could try in this way since you expect to keep maximum params esp. model parameters (weights) similar while running the comparision between them.

Same initialization but not the exact weights. However, I've run the experiments enough times with the same result for me to be sure that the initial weights aren't an issue.

Razer makes them with input from Lambda Labs, a deep learning company that hosts cloud GPUs and supports onsite builds as well. Lambda provides support and have been very helpful the few times I've needed to reach them. Base model (Linux) is $3.5k with a year warranty, $4.1k for the same with a 2 year warranty, and $5k for a dual Linux/windows machine that has a 3 year warranty. All machines have the same specs, so it's really support/warranty you're paying for.

Well I assumed that the network had more layers and so more parameters. More parameters can represent data much better and quicker. For example if you had a dataset with 30 features, and you use a Linear layer with 64 neurons, it should be able to represent each data point much quicker and easier than let's say a linear layer with 16 neurons. That's why I think the model would get converged quicker. But in OPs case his hidden layers are the same, only the output layer has more neurons. In that case we won't have a quick convergence.

Can you reason out why the model will get converged quicker ?

Did you keep same initial weights for both the networks ?

The simpler model had lower training loss with the same number of epochs. I tried training the second model until it had the same training loss as the first model, which took much longer. The validation did not improve and had a slight upward trend, which I know means that it's overfitting.

The most plausible explanation is overfitting. How do they compare in terms of error in the train set?

>The Tensorbook is only $3500 unless you're looking at the dual boot model

This is not a comment about you, it's just a general comment. Considering the fact that setting up a dual boot system takes minimal time and expertise, if someone decides to spend $500 for a dual boot model, do you really think they have the computer skills that would need a powerful laptop? I mean, they can't figure out how to dual boot but they want to do deep learning? lmao

Something tells me you don't do deep learning yet

Ideally it should. In that case you will have a worse performance for the second architecture. When you compare you'll have to say that. But it's pretty expected that the second architecture will not perform as well as the first one, so I'm not sure if there's much use comparing. But it's definitely doable.

I could add more linear layers and based on my experiments it would probably help but my intention is to compare my new model with the old one for which I presume the architecture should be as close as possible.

That shouldn't create a lot of difference but yes the performance should be worse than the first network in that case. It's far easier to predict two outputs than four. You can try increasing linear layers and using a slower learning rate to see if the model improves.

I wouldn't call it a bigger network necessarily. The second network has two more output neurons compared to the first. Rest are the same. How much difference that makes. Idk

That's strange. It could be a data quantity issue. Bigger networks typically will need more data to perform well.

Yes, I trained until the validation loss stopped improving, and then some more just to make sure.