Audio signals are more frequently polluted with various types of realistic noises. So, periods ago in order to reduce the noise level, some filtering approach will be used. But, presently there are many transform based techniques to estimate the noisy audio signal. One of the transform technique known as wavelet transform will be used for denoising an audio signal from realistic noise. Predominantly, the objective of this proposed research is to characterise discrete wavelet transform (DWT) towards denoising a one dimensional audio signal from common realistic noise. Moreover, the idea is to implement the audio signal denoising techniques such as decomposition, thresholding (soft) and reconstruction in the MATLAB simulation software, and elaborate a comparative analysis based on choice of wavelet transform over Fourier transform. Likewise, for the different level of decomposition, signal to noise (SNR) will be estimated .To sum up, in this research, different circumstances has been measured to elect best wavelet function and its level, based on its response of signal to noise ratio (SNR) in denoising audio signal.
Table of Contents
Chapter 1: Introduction and Objectives of the Project
1.1 Introduction
1.2 Project Summary.
Chapter 2: Theoretical Examination of Wavelet Transform
2.1 Principle of Wavelet Transform
2.2 Development of Wavelet Transform
2.3 Wavelet transform
2.3.1 Continuous wavelet transform
2.3.2 Discrete wavelet transform
2.4 Multiresolution Analysis (MRA)
2.4.1 Philosophy of Multiresolution Analysis:
2.4.2 Features of MRA
2.4.3 Properties of Scale and Time- Frequency Resolution
Chapter 3: Literature review
3.1 Overview
3.2 Short time Fourier transform
3.3 From Fourier Transform to Wavelet Transform
3.4 Comparison of wavelet transforms with Fourier transform
3.5 Wavelet Functions (WF)
3.6 Applications of Wavelet Transform
3.7 Audio Signal Denoising Using Wavelet Transform
3.8 Examples of Wavelet Based Noise Analysis
Chapter 4: Audio Signal Denoising Using Wavelet Transform
4.1 Digital Audio Signal
4.2 Audio Signal Denoising
4.2.1 Decomposition
4.2.2 Threshold selection
4.2.3 Reconstruction
Chapter 5: Experimental Results
5.1 Noise Analysis Using MATLAB
5.2 Critical examination of results
Chapter 6: Conclusion
6.1 Conclusion and Observation
6.2 Further Development and Future Work
Project Objective and Themes
This research aims to perform a comparative analysis of wavelet transform techniques for denoising one-dimensional audio signals corrupted by realistic noise. The primary objective is to evaluate the effectiveness of different wavelet functions and decomposition levels in improving the signal-to-noise ratio (SNR) using MATLAB simulation software.
- Performance characterization of Discrete Wavelet Transform (DWT) for audio denoising.
- Implementation of signal decomposition, thresholding (soft/hard), and reconstruction phases.
- Comparative performance evaluation of Wavelet Transform versus Fourier Transform.
- Optimization of wavelet function selection and decomposition levels for maximal noise reduction.
Excerpt from the Book
3.8 Examples of Wavelet Based Noise Analysis
Noise is formaly defined as an undesirable signal that terminates the measurement of the original message. Moreover, the noise will contain some source of unwanted information depending on the environment through which it is propagated(Luna et al. n.d.).
There are many kinds of noises; they could be categorised as:
The electronic noise which is thermal noise and shot noise(Rahate et al. 2015) (Luna et al. n.d.).
Acoustic noise is the which can be coming from automobiles, spinning engine, wind and r a i n .in deed these noises might come from striking sources or vibrating(Rahate et al. 2015) (Luna et al. n.d.).
Electromagnetic noise is which occur over radio frequency spectrum during transmission and reception of speech(Rahate et al. 2015) (Luna et al. n.d.).
Damage of data packets due to network blocking are caused because of the quantization noise. Further, this is classified in to different types such as white noise, narrowband noise, colour noise, impulsive noise and bandlimited white noise(Rahate et al. 2015) (Luna et al. n.d.).
Electrostatic noise is one which is generated because of high voltage(Rahate et al. 2015) (Luna et al. n.d.).
Summary of Chapters
Chapter 1: Introduction and Objectives of the Project: Introduces the role of wavelet transform in signal processing and outlines the specific research goals regarding audio noise reduction.
Chapter 2: Theoretical Examination of Wavelet Transform: Details the mathematical principles, development, and properties of wavelets and Multiresolution Analysis (MRA).
Chapter 3: Literature review: Provides a comprehensive overview of existing studies comparing wavelet and Fourier transforms, as well as various wavelet functions and their applications.
Chapter 4: Audio Signal Denoising Using Wavelet Transform: Describes the practical phases of denoising an audio signal, specifically decomposition, threshold selection, and reconstruction.
Chapter 5: Experimental Results: Presents the MATLAB-based implementation and the critical examination of SNR results for various wavelets and decomposition levels.
Chapter 6: Conclusion: Summarizes the research findings, highlighting the optimal wavelet configuration for audio signal denoising and suggesting future research directions.
Keywords
Wavelet Transform, Audio Denoising, Discrete Wavelet Transform, Multiresolution Analysis, Signal to Noise Ratio, MATLAB, Signal Reconstruction, Thresholding, Daubechies, Coiflet, Symlet, Noise Reduction, Non-stationary Signals, Fourier Transform, Decomposition
Frequently Asked Questions
What is the core focus of this research?
The research focuses on the application and comparative analysis of wavelet transform techniques to remove realistic noise from one-dimensional audio signals.
What are the central thematic fields?
The core themes include digital signal processing, wavelet analysis, multiresolution decomposition, noise thresholding methods, and SNR performance evaluation.
What is the primary research question?
The primary goal is to determine which wavelet functions and decomposition levels provide the highest improvement in signal quality when denoising audio signals against realistic noise.
Which scientific methodology is utilized?
The study employs a comparative experimental approach, utilizing MATLAB to simulate signal processing chains involving decomposition, thresholding (soft/hard), and reconstruction.
What topics are covered in the main section?
The main part covers the theoretical foundations of wavelets, a literature review of signal processing techniques, detailed implementation steps of the denoising algorithm, and an empirical analysis of experimental results.
Which keywords best characterize this work?
Key terms include Wavelet Transform, Denoising, SNR, MATLAB, Decomposition, Thresholding, and Multiresolution Analysis.
Why is Wavelet Transform preferred over Fourier Transform in this study?
Wavelet transform is highlighted for its ability to handle non-stationary signals efficiently and provide better time-frequency resolution compared to the fixed-window limitations of Fourier transform.
What is the significance of the "Level 3 Decomposition" results?
The results show that Level 3 decomposition, specifically using the 'db4' wavelet, yields the highest signal-to-noise ratio in the performed experiments.
How is the thresholding process managed?
The study utilizes several threshold estimation methods, including Minimax, Rigrsure, and Sqtwolog, implemented via MATLAB's wavelet toolbox to effectively reduce noise coefficients.
- Citar trabajo
- Bharath Munegowda (Autor), 2016, Denoising Audio Signal from Various Realistic Noise using Wavelet Transform, Múnich, GRIN Verlag, https://www.grin.com/document/334133
