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Table of contents
Abstract
List of Abbreviations
List of Figures
List of Tables
1 Introduction
1.1 Evolution of communication systems
1.1.1 Analog and digital telephony
1.1.2 Wireless cellular networks
1.2 Speech production
1.2.1 Speech sounds
1.2.2 Spectral characteristics of speech sounds
1.2.3 Effect of bandwidth on speech quality and intelligibility
1.3 Speech coding
1.3.1 Narrowband coding
1.3.2 Wideband coding
1.3.3 Super-wideband or full band coding
1.4 Artificial bandwidth extension
1.4.1 Non-blind methods
1.4.2 Blind methods
1.4.3 Motivation and applications
1.5 Super-wide bandwidth extension
1.6 Contributions
1.7 Outline of the thesis
2 Literature survey
2.1 Non-model based ABE approaches
2.2 ABE approaches based on source-filter model
2.2.1 Extension of spectral envelope
2.2.2 Extension of excitation
2.3 ABE approaches based on direct modelling of spectra
2.4 End-to-end approaches to ABE
2.5 ABE with modified loss functions
2.6 Feature selection and memory inclusion for ABE
2.6.1 Feature selection
2.6.2 Memory inclusion
2.7 Evaluation of speech quality
2.7.1 Assessement of different ABE algorithms
2.8 Approaches to super-wide bandwidth extension (SWBE)
2.8.1 SWBE for audio signals (speech and music)
2.8.2 SWBE for speech only
2.9 Summary
3 Baseline, databases and metrics
3.1 ABE algorithm
3.1.1 Training
3.1.2 Estimation
3.1.3 Resynthesis
3.2 Databases
3.2.1 TIMIT
3.2.2 TSP speech database
3.2.3 CMU-Arctic database
3.2.4 3GPP database
3.3 Data pre-processing and distribution
3.3.1 Data pre-processing
3.3.2 Training, validation and test data
3.4 Performance assessment
3.4.1 Subjective assessment
3.4.2 Objective assessment metrics
3.4.3 Mutual information assessment
4 ABE with explicit memory inclusion
4.1 Memory inclusion for ABE
4.2 Brief overview of memory inclusion for ABE via delta features: Past work
4.2.1 Memory inclusion scenarios
4.2.2 Highband certainty
4.2.3 Analysis and results
4.2.4 Discussion
4.3 Assessing the benefit of explicit memory to ABE
4.3.1 Analysis
4.3.2 Findings
4.3.3 Need for dimensionality reduction
4.4 ABE with explicit memory inclusion
4.4.1 Training
4.4.2 Estimation
4.4.3 Resynthesis
4.5 Experimental setup and results
4.5.1 Implementation details and baseline
4.5.2 Objective assessment
4.5.3 Subjective assessment
4.5.4 Mutual information assessment
4.5.5 Discussion
4.6 Summary
5 ABE with memory inclusion using semi-supervised stacked autoencoders
5.1 Unsupervised dimensionality reduction
5.1.1 Principal component analysis
5.1.2 Stacked auto-encoders
5.2 ABE using semi-supervised stacked auto-encoders
5.2.1 Semi-supervised stacked auto-encoders
5.2.2 Application to ABE
5.3 Experimental setup
5.3.1 SSAE training, configuration and optimisation
5.3.2 Databases and metrics
5.4 Results
5.4.1 Speech quality assessment
5.4.2 Mutual information assessment
5.5 Summary
6 Latent representation learning for ABE
6.1 Variational auto-encoders
6.1.1 Variational lower bound
6.1.2 Reparameterisation trick
6.1.3 Relation to conventional auto-encoders
6.1.4 VAEs for real valued Gaussian data
6.2 Conditional variational auto-encoders
6.3 Application to ABE
6.3.1 Motivation
6.3.2 Extracting latent representations
6.3.3 Direct estimation using CVAE-DNN
6.4 Experimental setup and results
6.4.1 CVAE configuration and training
6.4.2 Analysis of weighting factor
6.4.3 Objective assessment
6.4.4 Subjective assessment
6.5 Summary
7 Super-wide bandwidth extension
7.1 Motivation
7.2 Past work
7.3 Super-wide bandwidth extension (SWBE)
7.3.1 High frequency component estimation
7.3.2 Low frequency component upsampling
7.3.3 Resynthesis
7.4 Spectral envelope extension
7.4.1 Effect of sampling frequency
7.4.2 Extension
7.4.3 Comparison
7.5 Experimental setup and results
7.5.1 Databases
7.5.2 Data pre-processing
7.5.3 Assessment and baseline algorithm
7.5.4 Objective assessment
7.5.5 Subjective assessment
7.5.6 Discussion
7.6 Summary
8 Conclusions and future directions
8.1 Contributions and conclusions
8.2 Future directions
Bibliography
