This page presents the mini-project assignments, as they are assigned.
Mini Project 1: Analog Synthesizer Emulation
Assigned: Wed 2016-02-10
The goal of this miniproject is essentially to complete what we tried to do in the analog synthesizer practical - come up with a good emulation of an analog synth sound using Pd.
You should choose one of the sounds either from the Loomer Aspect demo or the Juno-106 examples page, and work to duplicate that sound with your Pd patch. You might want to look at the spectrogram and/or waveforms of the original sounds to figure out what's going on, and to validate your reproduction. Working with an actual voice from the Aspect synth will allow you to actually see how it's put together within that synthesizer, although there's still some detective work to be done to figure out exactly what it does internally.
The project is due by 5pm on Wednesday 2016-02-24. You should submit a report of 3 pages or so (as a PDF, please) explaining what you did and how it worked. You should also submit your patches.
Mini Project 2: Note Detection
Assigned: Wed 2016-03-09
You can make any enhancements or modifications you like, but here are some possible ideas:
Here is the Stevie Wonder example of vibrato that I played in class: superstition.wav.
The project is due by midnight on Sunday 2016-04-03. As before, you should submit a report of 3 pages or so (as a PDF, please) explaining what you did and how it worked. You should also submit your patch or patches.
Mini Project 3: Chord Recognition
Assigned: Wed 2016-04-06
This project is an extension of this week's practical. In the practical, you experimented with a trained chord recognition system, evaluated over a number of Beatles tracks for which we have manual ground-truth data. The mini project assignment is to spend some more time trying to improve the final accuracy number by whatever means you like.
Unlike most of the material in the course so far, this is a task with a clear, quantitative evaluation goal. This makes it much easier to tell how you are getting on - you can quickly check whether a change makes things better or not. However, it can also be a distraction - beware of spending too much time tuning parameters to achieve the single, absolute maximum value. It's often more important to step back and think of entirely different places you can make changes.
Things you can try could include the areas mentioned in the practical: looking at applying a compressive nonlinearity to the features (or other normalization -- maybe reducing the magnitude of the single largest value, since this is often dominated by the melody line, rather than the accompanying chord); experimenting with modifying the transition matrix, or the balance between the transition matrix probabilities and the probabilities evaluated by the Gaussians, since it is this balance between local match and sequential constraints that gives the HMM its power (raising the probabilities to a power before finding the Viterbi path may help).
You could also try "HPSS" harmonic-percussive separation as suggested in the LibROSA demo notebook.
For a long time, the only good ground-truth chord label collection was for the Beatles (done by Chris Harte at Queen Mary, London). However, there are now some other collections that have been transcribed in the same way; there are chord labels on the isophonics web site, although they don't distribute audio.
You can also try calculating beat-synchronous chroma features for your own audio using e4896_beat_sync_chroma.ipynb. You won't be able to train from that data (unless you create your own chord label data), but you can recognize the chords in other tracks.
I encourage you to try to analyze the kinds of errors that the current system is making by inspecting the confusion matrix, and perhaps the particularly difficult tracks, and seeing if that will guide the kinds of changes you choose to make.
Here are some related papers:
The project report is due by 5pm on Wednesday, 2016-04-20. Although we are interested to know what accuracies you obtain on the test set, your grade will not be based on this raw measure of achievement. Rather, we will make an assessment of how well you analyzed the situation, how skillfully and originally you approached it, etc.
This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.
Dan Ellis <firstname.lastname@example.org>
Last updated: Tue Apr 05 11:11:54 PM EDT 2016