Unsupervised Music Speech Classification using Gaussian Mixture Models (Expectation Maximization algorithm)
Problem Description -
A set of training and test examples of music and speech are provided. www.leap.ee.iisc.ac.in/sriram/teaching/MLSP21/assignments/speechMusicData.tar.gz
Using these examples, a) Generate spectrogram features - Use the log magnitude spectrogram as before with a 64 component magnitude FFT (NFFT). In this case, the spectrogram will have dimension 32 times the number of frames (using 25 ms with a shift of 10 ms). b) Train two GMM models with K-means initialization (for each class) separately each with 5-mixture components with diagonal/full covariance respectively on this data. Plot the log-likelihood as a function of the EM iteration. c) Classify the test samples using the built classifiers and report the performance in terms of error rate (percentage of mis-classified samples) on the text data.
The iterative algorithm used for training the GMMs was implemented from scratch using only numpy and without using sklearn library functions.
Performance analysis -
| Number of GMM Components | Type of Covariance | Accuracy(in %) |
|---|---|---|
| 2 | full | 83.33 |
| 2 | diagonal | 70.83 |
| 5 | full | 97.92 |
| 5 | diagonal | 64.58 |