DeepAI AI Chat
Log In Sign Up

A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer

by   Hu Hu, et al.

We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledge transfer, to address acoustic mismatches between training and testing conditions. Instead of carrying out point estimation in conventional maximum a posteriori estimation with a risk of having a curse of dimensionality in estimating a huge number of model parameters, we focus our attention on estimating a manageable number of latent variables of DNNs via a VB inference framework. To accomplish model transfer, knowledge learnt from a source domain is encoded in prior distributions of latent variables and optimally combined, in a Bayesian sense, with a small set of adaptation data from a target domain to approximate the corresponding posterior distributions. Experimental results on device adaptation in acoustic scene classification show that our proposed VB approach can obtain good improvements on target devices, and consistently outperforms 13 state-of-the-art knowledge transfer algorithms.


Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment

Learning deep neural networks that are generalizable across different do...

Inferring Parameters and Structure of Latent Variable Models by Variational Bayes

Current methods for learning graphical models with latent variables and ...

Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction

The impact of machine learning models on healthcare will depend on the d...

Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching

The performance of machine learning algorithms is known to be negatively...

Graph Domain Adaptation: A Generative View

Recent years have witnessed tremendous interest in deep learning on grap...

Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

Brain-Machine Interfaces (BMIs) have recently emerged as a clinically vi...

Domain Mismatch Robust Acoustic Scene Classification using Channel Information Conversion

In a recent acoustic scene classification (ASC) research field, training...