If you like this presentation – show it...
Experiments with Machine Learning Yuriy Guts Solutions Architect
First Things First What Is Machine Learning?
“ A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. — Tom M. Mitchell
Categories of Machine Learning 1. Supervised Learning. 2. Unsupervised Learning. 3. Reinforcement Learning.
Regression Predict a continuous dependent variable based on independent predictors
Classification Assign an observation to some category from a known discrete list of categories
Logistic Regression hypothesis = 1 / (1 + exp(-‐theta' * x));
Logistic Regression: Cost Function hypotheses = sigmoid(X * theta); cost = (1 / m) * (-‐y' * log(hypotheses) -‐ (1 -‐ y)' * log(1 -‐ hypotheses));
Let’s classify human speech! Decide whether a spoken phrase contains the word ‘Google’ or not
‘Google’ Detector: Feature Mapping Input: Output: Audio file (WAV, 16 bit mono, 44.1 kHz) 1 if it contains the word ‘Google’, otherwise 0 Options for building X[ ]: 1. Use raw waveform as a feature vector. But: will have 66150 features for a 1.5 second file. Kinda scary, and easy to overfit. 2. Use Mel-Frequency Cepstral Coefficients (MFCC). Believed to be closer to human auditory response. Depending on parameters, can give about 80 features per file.
[cepstra, aSpectrum, pSpectrum] = MFCC(waveform); x = [cepstra(1); cepstra(2); ...; cepstra(n)];
Let’s code it up MATLAB, logistic regression with conjugate gradient optimization
Q&A yuriy . guts @ gmail . com linkedin . com / in / yuriyguts github.com/YuriyGuts/gdg-speech-classifier