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SITELLE is a novel integral field unit spectroscopy instrument that has animpressive spatial (11 by 11 arcmin), spectral coverage, and spectralresolution (R=1-20000). SIGNALS is anticipated to obtain deep observations(down to 3.6x10-17ergs s-1cm-2) of 40 galaxies, each needing complex andsubstantial time to extract spectral information. We present a method that usesConvolution Neural Networks (CNN) for estimating emission line parameters inoptical spectra obtained with SITELLE as part of the SIGNALS large program. Ouralgorithm is trained and tested on synthetic data representing typical emissionspectra for HII regions based on Mexican Million Models database(3MdB) BONDsimulations. The network's activation map demonstrates its ability to extractthe dynamical (broadening and velocity) parameters from a set of 5 emissionlines (e.g. H{\alpha}, N[II] doublet, and S[II] doublet) in the SN3 (651-685nm) filter of SITELLE. Once trained, the algorithm was tested on real SITELLEobservations in the SIGNALS program of one of the South West fields of M33. TheCNN recovers the dynamical parameters with an accuracy better than 5 km s-1 inregions with a signal-to-noise ratio greater than 15 over the H{\alpha}line.More importantly, our CNN method reduces calculation time by over an order ofmagnitude on the spectral cube with native spatial resolution when comparedwith standard fitting procedures. These results clearly illustrate the power ofmachine learning algorithms for the use in future IFU-based missions.Subsequent work will explore the applicability of the methodology to otherspectral parameters such as the flux of key emission lines.

Machine Learning Approach to Integral Field Unit Spectroscopy Observations: I. HII Region Kinematics