Submitted by NotSoChildishRubino t3_11addy8 in MachineLearning
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Comments
thecuteturtle t1_j9rkmwo wrote
There are some chemicals and mixtures whose bands could overlap and make it difficult to distinguish between active sites (IE multiple O-H bonds etc.). Still agree that ML with spectral analysis is unnecessary, but that is a possible niche it could have.
Ferocious_Armadillo t1_j9rlhzx wrote
I might be off base here but my first thought was there might be something there with integrating the full area of peaks and sorting out peaks from specific elements in the spectral analysis of a heterogeneous mixture (possibly through a Fourier transform or convolution? This is ringing bells for me as feeling similar to signal processing…)
NotSoChildishRubino OP t1_j9t91yd wrote
I was thinking the same way, but then came to me the idea of, once I have detected peaks in a spectrum, I could distinguish peaks of different nature (eg. gaussian vs lorentzian) knowing the peak symmetry, the FWMH, or similar characteristics. I wouldn't be able to quantify the elements but i could use ML at a certain point, i guess.
testuser514 t1_j9s60rt wrote
It really depends on what you’re trying to learn
PassionatePossum t1_j9t1e09 wrote
I have seen image classification networks being used to classify sounds via spectrograms. It is perfectly conceivable to use ML to analyze spectrograms or to manipulate them and turn them back into sounds. Of course you can also do that directly by using time-series models.
But as long as you have a problem that can be modeled mathematically, you are usually better off to stick to mathematical models. They are usually more computationally efficient and predictable.
CyberPun-K t1_j9rhbqm wrote
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
The NHITS model enhances the multi-step forecasting strategy by incorporating innovative hierarchical interpolation and multi-rate data sampling techniques inspired by wavelet analysis.
It assembles its predictions sequentially and emphasizes its components with different frequencies and scales. NHITS significantly improves accuracy in long-horizon forecasting tasks while reducing computation time by orders of magnitude compared to existing neural forecasting approaches.
bubudumbdumb t1_j9rfqhp wrote
Spectral analysis has established methods that are exact and won't benefit from ML. As far as I understand the field that studies approximated or constrained spectral analysis is compressed sensing : that might have overlaps with ML.