Blind acoustic dereverberation

Title: A survey on methods for blind acoustic dereverberation.

Reverberation is a phenomenon in auditoriums such as concert halls and churches. Reverberation consists of a combination of multiple echoes, and its intensity and duration depend on factors such as the dimensions of the enclosure, materials used in construction and shape. Reverberation is desirable in music reproduction, however, it renders speech unintelligible. Thus there is a requirement to control reverberation of speech. This thesis work investigates the performances of different signal processing algorithms applied to suppress reverberation. Theoretical methods which have been verified with simulations are tested with real measurements. This gives a practical evaluation of the performance to be expected in the use of the algorithms.


1 Introduction
1.1 Reverberation
1.2 The basics of room acoustics
1.3 Reverberation Estimation
1.4 Example Reverberation Calculation
1.5 System types
1.5.1 Single Input Single Output System – SISO
1.5.2 Single Input Multiple Output (SIMO) Model
1.5.3 Multiple Input Single Output (MISO) Systems
1.5.4 Multiple Input Multiple Output (MIMO) Systems
1.6 Performance Criteria
1.6.1 Normalized Projection Misalignment
1.6.2 Signal to Deviation Ratio
2 Supervised Inverse filtering based dereverberation
2.1 The NLMS algorithm for a Single Input Single Output System Identification
2.1.1 Supervised inverse filtering
iii2.1.2 Impulse Response Measurements – Channel identification using the NLMS algorithm
2.2 Impulse Response Measurement Results
2.2.1 Inverse Filtering and Performance Evaluation
2.3 Inverse Filtering – Least squares method
2.4 Inverse Filtering – The Multichannel Inverse Theorem – MINT
3 Robust Inverse filtering with MINT
3.1 Generalized MINT performance
3.2 Regularization performance
3.3 Algorithm Optimization Procedure
3.3.1 Algorithm Optimization Procedure Step 1 – Delay
3.3.2 Algorithm Optimization Procedure Step 2 – Regularization
3.3.3 Algorithm Optimization Procedure Step 3 – Filter Length
3.3.4 Algorithm Optimization Results
4 Unsupervised Inverse filtering based dereverberation
4.1 Basic Principles
4.2 Identifiability Conditions
4.3 Algorithms
4.4 Constrained Time Domain Multichannel LMS
4.5 Constrained Time Domain Multichannel Newton Algorithm
4.6 Unconstrained Blind Multichannel LMS algorithm with Optimal Step Size control
4.7 Frequency Domain Normalized Multichannel LMS
4.8 Performance of Selected Blind Methods
5 Conclusion
6 Matlab Scripts
6.1 Two Channel Blind Identification : 3-tap channels
6.2 Identification with the NLMS Algorithm
6.3 Normalized LMS
6.4 Single Channel Dereverberation with NLMS
6.5 System Identification using NLMS
6.6 Blind SIMO LMS Well Conditioned Inputs
iv6.7 Blind SIMO LMS Bad Conditioned Inputs

Author: Isaac Osunkunle, Sayedali Shekarchi

Source: Blekinge Institute of Technology

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