Post-grad speech, audio processing & recognition coursework - Hidden Markov Model invesigations. Achieved 98%
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2021-01-03 21:14:25 +00:00 |
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.vscode
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report outline, added state transitions to constants
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2020-12-10 22:59:55 +00:00 |
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report
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added a bit to report, bit of notebook formatting
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2021-01-03 21:14:25 +00:00 |
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.gitignore
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finished code, beginning writeup
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2020-12-31 19:30:39 +00:00 |
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constants.py
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beginning markov code
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2020-12-11 21:33:20 +00:00 |
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markov.ipynb
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added a bit to report, bit of notebook formatting
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2021-01-03 21:14:25 +00:00 |
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markov.py
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gathered resources, working on report
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2021-01-01 18:53:11 +00:00 |
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markovlog.py
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finished code, beginning writeup
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2020-12-31 19:30:39 +00:00 |
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maths.py
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beginning markov code
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2020-12-11 21:33:20 +00:00 |
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notebook.py
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gathered resources, working on report
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2021-01-01 18:53:11 +00:00 |
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README.md
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added a bit to report, bit of notebook formatting
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2021-01-03 21:14:25 +00:00 |
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requirements.txt
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report outline, added state transitions to constants
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2020-12-10 22:59:55 +00:00 |
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scratchpad.ipynb
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gathered resources, working on report
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2021-01-01 18:53:11 +00:00 |
Hidden Markov Models
Speech recognition coursework focusing on training and analysing hidden markov models, main dev notebook here.
Probability density functions with provided observations marked
Occupation likelihoods for each state through time
Output Gaussian functions through 5 iterations of Baum-Welch training
