This is a SIGNALS tutorial to consolidate the material from Modules 1 and 2.
To prepare for the tutorial, go back over the Jupyter notebooks for those modules. Agree with your tutorial group a list of points that you would like to go over with your tutor. Carefully order your list (by topic and priority) and bring it to the tutorial.
Here are the key points that you need to understand from each notebook, so concentrate on these when writing your list:
- signals/slp-m1-1-sounds-signals – periodicity and pitch
- signals/slp-m1-2-digital-signals-complex-numbers – a phasor is a sinusoid with both magnitude and phase
- signals/slp-m1-3-sampling-sinusoids – sampling, Nyquist frequency, aliasing
- signals/slp-m1-4-discrete-fourier-transform – the DFT decomposes any signal into a series of basis functions; each basis function is a phasor (i.e., a sinusoid with magnitude and phase)
- signals/slp-m1-5-interpreting-the-dft – relating what you see in the time domain to what you see in the frequency domain
- signals/sp-m2-1-impulse-as-source – an impulse train has energy at every multiple of its fundamental frequency
- signals/sp-m2-2-fir-filters – FIR filters are little more than a moving average; an intuitive understanding that changing the filter coefficients changes the frequency response
- signals/sp-m2-3-iir-filters – IIR filters can exhibit resonance; the filter coefficients are not very intuitive; an IIR filter can impose a spectral envelope with resonances (formants) on its input signal; exciting an IIR filter with an impulse train can synthesise speech
Eventually, you may be able to understand a lot more of the material in the notebooks (so come back to them in a few weeks and try again), but the above is quite an achievement and is all you really need for the course.