This toolbox uses Bayesian Gaussian mixture models (BGMM) for mapping/regression. It is the Bayesian extension of the typically used GMM mapping learned with Expectation maximization[1]. Inference is performed using Variational Inference[2].
This also forms the source code related to our work
- Ana Ramirez Lopez, Shreyas Seshadri, Lauri Juvela, Okko Räsänen and Paavo Alku: "Speaking style conversion from normal to Lombard speech using a glottal vocoder and Bayesian GMMs", accepted for publication in Proc. Interspeech 2017, Stockholm, Sweden.
This algorithm is described in VBGMM_mapping.
Comments/questions are welcome! Please contact: shreyas.seshadri@aalto.fi
Last updated: 18.8.2017
Copyright (C) 2016 Shreyas Seshadri, Aalto University
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The source code must be referenced when used in a published work.
runBgmm.m: Script to run the toy data
bayesianGMM.m: function that returns conditional posterior predictive for a VB-GMM
multTPdf.m: return probablities for mutivariate student t
multTmm_prediction.m: Function to do get predicted target based on the posterior predictive distribution.
VB_gmms.m: Main function that performs the variational inference of the Bayesian mixture models
updateR.m: Function to do the E-step in the variational inference algorithm
postUpdate.m: Function to do the M-step in the variational inference algorithm
freeEnergyCalc.m: Function to calculate the free energy of the variational distribution
wishartEntropy.m: Function that calculates the entropy of the Wishart distribution
reorderFE.m: Function to reorder the clusters in descending order of the cluster occupancy. Used for the non-parametric weight priors.
logdet.m: Function to calculate the calculate the log determinant of x
logNormalize.m: Function to normalize the values given in log scale
structMerge.m: Function to merge the objects in 2 or 3 structures
d2_c_0.5_N2500_k10.mat : toy data
[1] Y. Stylianou, O . Cappé, and E. Moulines, “Continuous probabilistic transform for voice conversion,” IEEE Trans. on Speech and Audio Processing, vol. 6, no. 2, pp. 131–142, 1998
[2] C. M. Bishop, Pattern recognition and machine learning. Springer, 2006.