Bayesian Nonparametric Matrix Factorization for Recorded Music Reading Group Presenter: Shujie Hou Cognitive Radio Institute Friday, October 15, 2010 Authors:

Bayesian Nonparametric Matrix Factorization for Recorded Music

Reading Group Presenter:

Shujie Hou

Cognitive Radio Institute

Friday, October 15, 2010

Authors: Matthew D. Hoffman, David M. Blei, Perry R. cook

Princeton University, Department of Computer Science, 35 olden St., Princeton, NJ, 08540 USA

Outline

■ Introduction■ Terminology■ Problem statement and contribution of this paper

■ Gap-NMF Model(Gamma Process Nonnegative Matrix Factorization )

■ Variational Inference■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Terminology(1)

■ Nonparametric Statistics:□ The term non-parametric is not meant to imply that such models

completely lack parameters but that the number and nature of the parameters are flexible and not fixed in advance.

■ Nonnegative Matrix Factorization:□ Non-negative matrix factorization (NMF) is a group of algorithms

in multivariate analysis and linear algebra where a matrix, is factorized into (usually) two matrices with all elements are greater than or equal to 0 WHX

The above two definitions are cited from Wikipedia

Terminology(2)

■ Variational Inference:□ Variational inference approximates the posterior distribution with

a simpler distribution, whose parameters are optimized to be close to the true posterior.

■ Mean-field Variational Inference:□ In mean-field variational inference, each variable is given an

independent distribution, usually of the same family as its prior.

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Problem Statement and Contribution

■ Research Topic:□ Breaking audio spectrograms into separate sources of sound

using latent variable decompositions. E.g., matrix factorization.

■ A potential problem :□ The number of latent variables must be specified in advance

which is not always possible.

■ Contribution of this paper□ The paper develops Gamma Process Nonnegative Matrix

Factorization (GaP-NMF), a Bayesian nonparametric approach to decompose spectrograms.

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Dataset on GaP-NMF Model

■ What are given is a M by N matrix

in which is the power of audio signal at time window n and frequency bin m.

If the number of latent variable is specified in advance:■ Assuming the audio signal is composed of K static sound

sources. The problem is to decompose , in which is M by K matrix, is K by N matrix. In which cell is the average amount of energy source k exhibits at frequency m. cell is the gain of source k at time n.

■ The problem is solved by

X

mnX

WHX

H

W

knH

mkW

GaP-NMF Model

If the number of latent variable is not specified in advance:

■ GaP-NMF assumes that the data is drawn according to the following generative process:

Based on the formula that(Abdallah&Plumbley (2004))

GaP-NMF Model

If the number of latent variable is not specified in advance:

■ GaP-NMF assumes that the data is drawn according to the following generative process:

The overall gain of the corresponding source l

Based on the formula that(Abdallah&Plumbley (2004))

Used to control the number of latent variables

GaP-NMF Model

■ The number of nonzero is the number of the latent variables K.

■ If L increased towards infinity, the nonzero L which expressed by K is finite and obeys:

Kingman ,1993

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Definition of Variational Inference

■ Variational inference approximates the posterior distribution with a simpler distribution, whose parameters are optimized to be close to the true posterior.

■ Under this paper’s condition:

Posterior Distribution

What measured

Definition of Variational Inference

■ Variational inference approximates the posterior distribution with a simpler distribution, whose parameters are optimized to be close to the true posterior.

■ Under this paper’s condition:

Variational distribution assumption with free parameters

Variational Distribution Posterior Distribution

Approximates

What measured

Definition of Variational Inference

■ Variational inference approximates the posterior distribution with a simpler distribution, whose parameters are optimized to be close to the true posterior.

■ Under this paper’s condition:

Variational distribution assumption with free parameters

Variational Distribution Adjust Parameters Posterior Distribution

Approximates

What measured

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Variational Objective Function

■ Assume each variable obeys the following Generalized Inverse-Gaussian (GIG) family:

Variational Objective Function

■ Assume each variable obeys the following Generalized Inverse-Gaussian (GIG) family:

It is Gamma family

Variational Objective Function

■ Assume each variable obeys the following Generalized Inverse-Gaussian (GIG) family:

Denotes a modified Bessel function of the second kind

It is Gamma family

Deduction(1)

■ The difference between the left and right sides is the Kullback-Leibler divergence between the true posterior and the variational distribution q.

■ Kullback-Leibler divergence : for probability distributions P and Q of a discrete random variable their K–L divergence is defined to be

From Jordan et al., 1999

Deduction(2)

Deduction(2)

Using Jensen’s inequality

Objective function

■ L=

■ The objective function becomes

Bounded by

+

■ Maximize the objective function defined above with the corresponding parameters.

■ The distribution is obtained:

■ Because these three distributions are independent, we gain

approximates

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Coordinate Ascent Algorithm(1)

■ The derivative of the objective function with respect to variational parameters equals to zero to obtain:

■ Similarly:

Coordinate Ascent Algorithm(2)

■ Using Lagrange multipliers, then the bound parameters become

■ Then updating bound parameters and variational parameters according to equations 14,15,16,17 and18 to ultimately reaching a local minimum.

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches■ Evaluation

Other Approaches

■ Finite Bayesian Model ( also called GIG-NMF).■ Finite Non-Bayesian Model.■ EU-Nonnegative Matrix Factorization.■ KL-Nonnegative Matrix Factorization.

Outline

■ Introduction■ Terminology■ Problem statement and Contribution of this Paper

■ Gap-NMF Model■ Variational Inference

■ Definition■ Variational Objective Function■ Coordinate Ascent Optimization

■ Other Approaches

■ Evaluation

Evaluation on Synthetic data(1)

■ The data is generated according to the following model:

Evaluation on Synthetic data(2)

Evaluation on Recorded Music

Conclusion

■ Gap-NMF model is capable of determining the number of latent source automatically.

■ The key step of the paper is to use variational distribution to approximate posterior distribution.

■ Gap-NMF can work well on analyzing and processing recorder music, it can be applicable to other types of audio.

■Thank you!