pyroomacoustics.recognition module¶
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class
pyroomacoustics.recognition.
CircularGaussianEmission
(nstates, odim=1, examples=None)¶ Bases:
object
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get_pdfs
()¶ Return the pdf of all the emission probabilities
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prob_x_given_state
(examples)¶ Recompute the probability of the observation given the state of the latent variables
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update_parameters
(examples, gamma)¶
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class
pyroomacoustics.recognition.
GaussianEmission
(nstates, odim=1, examples=None)¶ Bases:
object
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get_pdfs
()¶ Return the pdf of all the emission probabilities
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prob_x_given_state
(examples)¶ Recompute the probability of the observation given the state of the latent variables
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update_parameters
(examples, gamma)¶
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class
pyroomacoustics.recognition.
HMM
(nstates, emission, model='full', leftright_jump_max=3)¶ Bases:
object
Hidden Markov Model with Gaussian emissions
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K
¶ Number of states in the model
Type: int
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O
¶ Number of dimensions of the Gaussian emission distribution
Type: int
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A
¶ KxK transition matrix of the Markov chain
Type: ndarray
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pi
¶ K dim vector of the initial probabilities of the Markov chain
Type: ndarray
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emission
¶ An instance of emission_class
Type: (GaussianEmission or CircularGaussianEmission)
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model
¶ The model used for the chain, can be ‘full’ or ‘left-right’
Type: string, optional
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leftright_jum_max
¶ The number of non-zero upper diagonals in a ‘left-right’ model
Type: int, optional
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backward
(X, p_x_given_z, c)¶ The backward recursion for HMM as described in Bishop Ch. 13
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fit
(examples, tol=0.1, max_iter=10, verbose=False)¶ Training of the HMM using the EM algorithm
Parameters: - examples ((list)) – A list of examples used to train the model. Each example is an array of feature vectors, each row is a feature vector, the sequence runs on axis 0
- tol ((float)) – The training stops when the progress between to steps is less than this number (default 0.1)
- max_iter ((int)) – Alternatively the algorithm stops when a maximum number of iterations is reached (default 10)
- verbose (bool, optional) – When True, prints extra information about convergence
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forward
(X, p_x_given_z)¶ The forward recursion for HMM as described in Bishop Ch. 13
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generate
(N)¶ Generate a random sample of length N using the model
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loglikelihood
(X)¶ Compute the log-likelihood of a sample vector using the sum-product algorithm
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update_parameters
(examples, gamma, xhi)¶ Update the parameters of the Markov Chain
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viterbi
()¶
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