Kalman filter is a algorithm of predicting the future state of a system based on the previous ones.
In the presentation, I introduce to basic Kalman filtering step by step, with providing examples for better understanding.
Avionics 738 Adaptive Filtering at Air University PAC Campus by Dr. Bilal A. Siddiqui in Spring 2018. This lecture deals with introduction to Kalman Filtering. Based n Optimal State Estimation by Dan Simon.
Kalman Filter, also known as Linear Quadratic Estimation (LQE) is the algorithm that uses series of measurements that are observed over time and that contains statistical noise and other inaccuracies that are found in the given system. Copy the link given below and paste it in new browser window to get more information on Kalman Filter:- http://www.transtutors.com/homework-help/statistics/kalman-filter.aspx
A Kalman Filter is a more sophisticated smoothing algorithm that will actually change in real time as the performance of Various Sensors Change and become more or less reliable.What we want to do is filter out noise in our measurements and in our sensors and Kalman Filter is one way to do that reliably.It is based on Recursive Bayesian Filter
Kalman filter is a algorithm of predicting the future state of a system based on the previous ones.
In the presentation, I introduce to basic Kalman filtering step by step, with providing examples for better understanding.
Avionics 738 Adaptive Filtering at Air University PAC Campus by Dr. Bilal A. Siddiqui in Spring 2018. This lecture deals with introduction to Kalman Filtering. Based n Optimal State Estimation by Dan Simon.
Kalman Filter, also known as Linear Quadratic Estimation (LQE) is the algorithm that uses series of measurements that are observed over time and that contains statistical noise and other inaccuracies that are found in the given system. Copy the link given below and paste it in new browser window to get more information on Kalman Filter:- http://www.transtutors.com/homework-help/statistics/kalman-filter.aspx
A Kalman Filter is a more sophisticated smoothing algorithm that will actually change in real time as the performance of Various Sensors Change and become more or less reliable.What we want to do is filter out noise in our measurements and in our sensors and Kalman Filter is one way to do that reliably.It is based on Recursive Bayesian Filter
A KALMAN FILTERING TUTORIAL FOR UNDERGRADUATE STUDENTSIJCSES Journal
This paper presents a tutorial on Kalman filtering that is designed for instruction to undergraduate
students. The idea behind this work is that undergraduate students do not have much of the statistical and
theoretical background necessary to fully understand the existing research papers and textbooks on this
topic. Instead, this work offers an introductory experience for students which takes a more practical usage
perspective on the topic, rather than the statistical derivation. Students reading this paper should be able
to understand how to apply Kalman filtering tools to mathematical problems without requiring a deep
theoretical understanding of statistical theory.
This presentation speak's about Kalman Filter. In the presentation arithmetic behind Kalman filter is defined. The presentation is concluded with application example of Kalman filter.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
General Kalman Filter & Speech Enhancement for Speaker Identificationijcisjournal
Presence of noise increases the dimension of the information. A noise suppression algorithm is developed
with an idea of combining the General Kalman Filter and Estimate Maximization (EM) frame work.This
combination is helpful and effective in identifying noise characteristics of an acoustic environment.
Recursion between Estimate step and Maximization step enabled the algorithm to deal any model of noise.
The same Speech enhancement procedure in applied in the pre-processing stage of a conventional Speaker
identification method. Due to the non-stationary nature of noise and speech adaptive algorithms are
required. Algorithm is first applied for Speech enhancement problem and then extended to using it in the
pre-processing step of the Speaker identification. The present work is compared in terms of significant
metrics with existing and popular algorithms and results show that the developed algorithm is dominant
over them.
A KALMAN FILTERING TUTORIAL FOR UNDERGRADUATE STUDENTSIJCSES Journal
This paper presents a tutorial on Kalman filtering that is designed for instruction to undergraduate
students. The idea behind this work is that undergraduate students do not have much of the statistical and
theoretical background necessary to fully understand the existing research papers and textbooks on this
topic. Instead, this work offers an introductory experience for students which takes a more practical usage
perspective on the topic, rather than the statistical derivation. Students reading this paper should be able
to understand how to apply Kalman filtering tools to mathematical problems without requiring a deep
theoretical understanding of statistical theory.
This presentation speak's about Kalman Filter. In the presentation arithmetic behind Kalman filter is defined. The presentation is concluded with application example of Kalman filter.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
General Kalman Filter & Speech Enhancement for Speaker Identificationijcisjournal
Presence of noise increases the dimension of the information. A noise suppression algorithm is developed
with an idea of combining the General Kalman Filter and Estimate Maximization (EM) frame work.This
combination is helpful and effective in identifying noise characteristics of an acoustic environment.
Recursion between Estimate step and Maximization step enabled the algorithm to deal any model of noise.
The same Speech enhancement procedure in applied in the pre-processing stage of a conventional Speaker
identification method. Due to the non-stationary nature of noise and speech adaptive algorithms are
required. Algorithm is first applied for Speech enhancement problem and then extended to using it in the
pre-processing step of the Speaker identification. The present work is compared in terms of significant
metrics with existing and popular algorithms and results show that the developed algorithm is dominant
over them.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition TechniqueCSCJournals
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
We have proposed new approach for the speech recognition system by applying kernel adaptive filter for
speech enhancement and for the recognition, the hybrid HMM/DTW methods are used in this paper. Noise
removal is very important in many applications like telephone conversation, speech recognition, etc. In the
recent past, the kernel methods are showing good results for speech processing applications. The feature
used in the recognition process is MFCC features. It consists of a HMM system used to train the speech
features and for classification purpose used the DTW method. Experimental results show a relative
improvement of recognition rate compared to the traditional methods.
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
Comparison and Analysis Of LDM and LMS for an Application of a SpeechCSCJournals
Most of the automatic speech recognition (ASR) systems are based on Guassian Mixtures model. The output of these models depends on subphone states. We often measure and transform the speech signal in another form to enhance our ability to communicate. Speech recognition is the conversion from acoustic waveform into written equivalent message information. The nature of speech recognition problem is heavily dependent upon the constraints placed on the speaker, speaking situation and message context. Various speech recognition systems are available. The system which detects the hidden conditions of speech is the best model. LMS is one of the simple algorithm used to reconstruct the speech and linear dynamic model is also used to recognize the speech in noisy atmosphere..This paper is analysis and comparison between the LDM and a simple LMS algorithm which can be used for speech recognition purpose.
This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM, which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper
Emotion Recognition based on audio signal using GFCC Extraction and BPNN Clas...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Effect of Time Derivatives of MFCC Features on HMM Based Speech Recognition S...IDES Editor
In this paper, improvement of an ASR system for
Hindi language, based on Vector quantized MFCC as feature
vectors and HMM as classifier, is discussed. MFCC features
are usually pre-processed before being used for recognition.
One of these pre-processing is to create delta and delta-delta
coefficients and append them to MFCC to create feature vector.
This paper focuses on all digits in Hindi (Zero to Nine), which
is based on isolated word structure. Performance of the system
is evaluated by accurate Recognition Rate (RR). The effect of
the combination of the Delta MFCC (DMFCC) feature along
with the Delta-Delta MFCC (DDMFCC) feature shows
approximately 2.5% further improvement in the RR, with no
additional computational costs involved. RR of the system for
the speakers involved in the training phase is found to give
better recognition accuracy than that for the speakers who
were not involved in the training phase. Word wise RR is
observed to be good in some digits with distinct phones.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
Kalman filter
1. A Presentation On
A Fast Adaptive Kalman Filtering Algorithm
for Speech Enhancement
P.SHARFUDDIN (10731A0233)
P.MARUTHI BASKAR NAIDU (10731A0235)
M.RAGHAVA REDDY (10731A0223)
K.KHAMEER (10731A0219)
P.RAMA KRISHNA (10731A0237)
Presented BY
DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING
P.B.R VISVODAYA INSTITUTE OF TECHNOLOGYAND SCIENCE
(Affiliated to J.N.T.U, Anantapur)
KAVALI
2. Introduction
Speech
Speech is the process associated with the production and perception of the noises used
in the spoken language. A huge number of disciplines study the speech and the speech
sounds, including acoustic ,psychology, speech pathology, linguistic, cognitive science
and computer science.
Spoken language is used to communicate information from a speaker to a listener.
Speech production and perception are both important components of the speech chain.
Speech perception
Speech perception refers to processes by which humans are able to interpret and
understand the sounds used in the language. The study of the speech perception is
closely linked to the phonetic field and phonology. Speech perception researches seek
to understand how the humans recognize the speech sounds and use this information
to understand the spoken language
3. Cont…
Speech communications
Speech is the most primary human communication. For that reason, it exists a big
trend to increase and improve telecommunications.
Nowadays, all the people use the communication devices almost as a primary good:
telephones, mobiles internet…and the customers demand a high coverage and
quality.
However, the background noise is an important handicap. If it is joined with other
distortions, it can seriously damage the service quality.
4. Speech Processing
Speech processing is the study of speech signals and the processing methods of
these signals. The signals are usually processed in a digital representation
whereby speech processing can be seen as the intersection of digital signal
processing and natural language processing.
Speech processing can be divided in the following categories:
Speech recognition, which deals with analysis of the linguistic content of a
speech signal.
Speaker recognition, where the aim is to recognize the identity of the
speaker.
Enhancement of speech signals (this is the area of this project)
Speech coding, a specialized form of data compression, which is important in
the telecommunication area.
Speech synthesis: the artificial synthesis of speech, which usually means
computer generated speech.
5. What is speech enhancement? Enhancement means the improvement in the
value or quality of something. When applied to speech, this simply means the
improvement in intelligibility and/or quality of a degraded speech signal by using
signal processing tools. By speech enhancement, it refers not only to noise
reduction but also to dereverberation and separation of independent signals.
This is a very difficult problem for two reasons. First, the nature and
characteristics of the noise signals can change dramatically in time and between
applications. It is also difficult to find algorithms that really work in different
practical environments. Second, the performance measure can also be defined
differently for each application
Speech Enhancement
6. There is an important algorithm for speech enhancement which belongs to the
group of parametric methods where the speech signal is modeled as an
autoregressive process embedded in Gaussian noise. Speech enhancement
algorithms belonging to this category consist of two steps:
· Estimation of the AR coefficients and noise variances.
· Application of the Kalman filtering using the estimated parameters to estimate
the clean speech from a sample of the noisy signal.
7. Speech Production
Speech begins with a thought and an intention to communicate in the brain,
which activates the muscular movements to produce speech sounds. A listener
receives a sound in the auditory system, processing it for a conversion to
neurological signals the brain can understand. The speaker continuously
monitors and controls the vocal organs by receiving his or her own speech as
feedback.
Speech signals are composed by analog units, which are the symbolic
representation of the spoken language: phonemes, syllables and words. But for
this project it is not necessary to go into this level of depth.
8. Linear Predictive Coding
(LPC)
Linear predictive coding (LPC) is a tool used, mainly, in the audio signal and
speech processing to represent the spectral envelop of a speech digital signal in
a compressed way (using the information of linear prediction model). This
technique is one of the most powerful to analyze the speech, and one of the
most useful methods for encoding with good quality at low rate.
LPC starts with the assumption that the speech signal is produced by a buzz at
the end of a tube, adding, sometimes, hissing and popping sounds. This model
is a good approximation to the reality.
9. Kalman filtering
The filter has its origin in a Kalman’s document (1960) where it is described as a
recursive solution for the linear filtering problem for discrete data. The research was
in a wide context of state – space models, where the point is the estimation through
the recursive least squares. Since that moment, due to the development of digital
calculation, Kalman filter has been researched and applied, particularly in self and
assisted navigation, missiles search and economy. The study of Kalman filter is based
on Wiener filter.
The filter is a mathematical procedure which operates through a prediction and
correction mechanism. In essence, this algorithm predicts a new state from its
previous estimation by adding a correction term proportional to the predicted error.
In this way, this error is statistically minimized. This filter is the main algorithm to
estimate dynamic systems specified in state-space form.
A Kalman filter is simply an optimal recursive data processing algorithm
10. The Kalman filter is the main algorithm to estimate dynamics systems
represented as state-space. In this representation the system is described by a
set of variables denominated of state. The state contains all the information to
do with a certain point in time. This information must permit the deduction of
the past system behavior, with the goal of predicting its future behavior.
11. The algorithm
The Kalman filter estimates the previous process using a feedback control, that
is, it estimates the process to a moment over the time and then it gets the
feedback through the observed data.
From the equation point of view that is used to derivate the Kalman filter, it is
possible to separate them into two groups:
· Those which update the time or prediction equations
· Those which update the observed data or update equations.
12. The first group of equations has to throw the state to the n moment taking as
reference the state on n-1 moment and the intermediate update of the
covariance matrix of the state. The second group of equations has to take care
of the feedback; they add new information inside the previous estimation to
achieve an improved estimation of the state.
The equations which update the time can be seen as prediction equations, while
the equations which add new information can be seen as correction equations.
Exactly, the final estimation algorithm can be defined as a prediction-correction
algorithm to solve many problems.
13. Advantages of Kalman Filter
Below are some advantages of the Kalman filter, comparing with another famous
filter known as the Wiener Filter.
1. The Kalman filter algorithm is implementable on a digital computer, which this
was replaced by analog circuitry for estimation and control when Kalman filter was
first introduced.
2. Stationary properties of the Kalman filter are not required for the deterministic
dynamics or random processes. Many applications of importance include
nonstationary stochastic processes.
14. Cont…
3. The Kalman filter is compatible with state-space formulation
of optimal controllers for dynamic systems. It proves useful
towards the 2 properties of estimation and control for these
systems.
4. The Kalman filter requires less additional mathematical
preparation to learn for the modern control engineering
student.
5. Necessary information for mathematically sound, statistically-
based decision methods for detecting and rejecting
anomalous measurements are provided through the use of
Kalman filter.
15. Kalman Filter Working
Firstly, it estimates a process by using a form of feedback control loop
whereby the filter estimates the process state at some time and then
obtains feedback in the form of (noisy) measurements.
As such, these equations for the Kalman filter fall into two groups: “Time
Update equations” and “Measurement Update equations”
The time update equations can also be thought of as “predictor” equations,
while the measurement update equations can be thought of as “corrector”
equations.
17. Implementation of Kalman
filter to Speech
The idea of this project is to reconstruct a speech signal using the
Kalman filter technique.
This speech signal will be modeled as an AR process and
represented in the state-space domain.
In striving for this goal, certain parameters have to be taken into
consideration.
18. Implementation
The all pole, or autoregressive (AR), signal model is
often used for speech. The AR signal model is
introduced as:
Above equation can also be written in this form as
shown below:
19. Cont…
In order to apply Kalman filtering to the speech expression shown
above, it must be expressed in state space form as
20. Cont…
The Kalman filter functions in a looping method
here we denote the following steps within the
loop of the filter. Define matrix HT
k-1 as the row
vector
and zk = yk
where Xk will always be updated according to the
number of iterations, k.
22. Conclusion
In this project, an implementation of employing Kalman
filtering to speech processing had been developed.
The purpose of this approach is to reconstruct an output
speech signal by making use of the accurate estimating
ability of the Kalman filter.
Furthermore, the results have also shown that Kalman
filter could be tuned to provide optimal performance.