The neural network is trained on our database of 63 songs (these are sort of arbitrarily chosen, I went on a youtubeToWav converter downloading spree).<\/p>\n
To reiterate, the model is as follows: A song will be recorded in .wav format, then converted into a chroma feature (actually, a CENS format, which includes some more normalization and smoothing). This CENS is used to produce a cross-similarity matrix with every song in the model’s CENS. These matrices are all classified (the classifier outputs the probability that a matrix represents a match), and the highest-probability-of-match songs are ranked. Currently, the network’s mean squared error is about 1.72%.<\/p>\n
<\/p>\n
Before the demo, I’m cleaning up the integration and making sure that everything can smoothly connect with the visualization webapp and with anja’s dynamic time warping. Since my neural network is in python\/keras and my preprocessing is in MATLAB, I’m using MATLAB’s engine for python to integrate those.<\/p>\n","protected":false},"excerpt":{"rendered":"
The neural network is trained on our database of 63 songs (these are sort of arbitrarily chosen, I went on a youtubeToWav converter downloading spree). To reiterate, the model is as follows: A song will be recorded in .wav format, then converted into a chroma feature (actually, a CENS format, which includes some more normalization … <\/p>\n