Monday, December 30, 2019

Breast Cancer Case Study - 929 Words

Discussion In the present study, we found that rate of pCR to neoadjuvant chemotherapy was 24.7% (n=25) in the whole cohort (n=101). This finding was consistent with the range of pCR rates to prospective randomized trials of preoperative chemotherapy of 15%-40% mentioned in the literature (Burstein et al., 2008). Moreover, high Ki-67 expression (cutoff 14%) was significantly correlated with achieving a pCR in the large cohort (p=0.016). This result matched with the finding of several studies that reported that high Ki-67 expression was associated with higher response rates to neoadjuvant chemotherapy in breast cancer (Keam et al., 2011). Also, our study revealed that TNBC patients accounted for about 23% of the whole cohort of invasive†¦show more content†¦Similarly, Liu et al. (2013) gave 7 cycles of platinum-based neo-adjuvant chemotherapy in 717 patients, of whom 31% were TNBC, and found that the clinical complete response (cCR) rate and the pathological complete response (pCR) rates were significantly higher in TNBC group than in non-TNBC one. However, the low pCR rate achieved by our TNBC cases was close to that reported in a small study at Dana Farber Cancer Institute, where 26 TNBC patients received six cycles of neoadjuvant single-agent cisplatin, and only4 (15%) achieved complete response (CR), after exclusion of two cases with BRCA mutation (Silver et al., 2010). Similarly, Bidard et al., reported a pCR rate of 17% in a relatively large cohort of TNBC group (n=120) receiving a combined neoadjuvant chemotherapy regimen composed of cyclophosphamide, epirubicin and 5-fluorouracil (CEF) for 4 to 6 cycles (Bidard et al., 2008). Our low pCR rate in TNBC subgroup might be also explained by the type of chemotherapeutic agents that had been used. Rocca and colleagues conducted a retrospective analysis of core biopsies of patients with breast cancer treated with neoadjuvant cisplatin-based chemotherapy in breast cancer and showed that administration of cisplatin without anthracyclines yielded a higher rate of pCR in patients with p63-positive tumors (Rocca et al., 2008). Again, our low pCR rate in TNBC might be also explained by the heterogeneousShow MoreRelatedBreast Cancer Case Study1234 Words   |  5 Pagesultimately lead to the progression of oral cancer. (Lee et al, 2010). c‑myc was among the first oncogenes found to be amplified in breast cancer, and it can contribute to many other forms of cancer (Victoria and Michael, 2007). It is a critical downstream effector of the Wnt/TCF pathway in colon cancer and activation of Myc might play a predominant role in the pathogenesis of tumors like pediatric hepatoblastoma (Sansom et al, 2007; Cairo et al, 2008). A study done in south Indian population has shownRead MoreBreast Cancer Case Study803 Words   |  4 Pagessecondary organs fibronectin expression is upregulated by primary tumors via resident fibroblasts, which serves as a docking site for VEGFR1+ hematopoietic progenitor cell (HPC) clusters and secondary seeding. During metastasis of breast cancer to lung, interaction of VCAM-1+ cancer cells with VLA-4-expressing macrophages, activates PI3K/Akt signaling in tumor cells, protecting them from caspase-induced apoptosis. Bone metastasis is also facilitated by interaction of VCAM1 with different integrin partnerRead MoreBreast Cancer Case Study1306 Words   |  6 Pagesmetastases have caused substantial mortality and morbidity in cancer patients. Approximately 15% of women with breast cancer will be diagnosed with brain metastases (Zakaria et al., 2014). With early diagnosis and appropriate treatment, the quality of the patient’s life could be enhanced. Therefore, it is vital for patients with a known primary tumour to undergo imaging studies once they develop neurological signs and symptoms. Imaging studies aid to accurately identify the type, number, size, and locationRead MoreEssay on Breast Cancer Case Study642 Words   |  3 Pagesï » ¿Luis F Vega Jr NUR 1211 Breast Cancer Case Study Mrs. Thomas, a 57 year old married lawyer, was found to have a 4 X 6 cm firm, fixed mass in the upper, outer quadrant of the right breast during a routine physical examination, and a stereotactic core biopsy indicated a malignant tumor. Although the surgeon recommended a mastectomy because of the size of the tumor, Mrs. Thomas chose to have a lumpectomy. Now three weeks postoperative, she is scheduled for chemotherapy. Subjective Data NeverRead MoreAdult Case Study : Male Breast Cancer3050 Words   |  13 Pages Adult Case Study: Male Breast Cancer Carly Regan Loyola University Chicago Introduction Overview When the topic of breast cancer is discussed in conversation, most will think of it as a female diagnosis. While the greatest percentages of patients diagnosed with breast cancer are female, it is still possible that males can be diagnosed. It is a diagnosis that practitioners should always keep in the back of their minds as a differential when a male presentsRead MoreBreat Cancer Screening Essay1514 Words   |  7 PagesBREAST CANCER SCREENING DR. DANA MORTEZ ARLECIA PURVIS JULY 17, 2011 Current research shows mammograms are stronger than in the past. In a recent study it has confirmed that mammograms benefit for women in their forties and fifties. Women feel confident about the benefits that are associated with a regular mammograms for finding cancer early. However, mammograms can have their limitations. A mammogram will miss some cancers, and it sometimes can leadRead MoreThe Treatment Of Breast Cancer1309 Words   |  6 Pages Introduction: Breast cancer is the most common malignant disease occurring in women in Saudi society. After study and research, it found that two-thirds of the injuries in the Saudi society are diagnosed in advanced stages. The reasons for that are the lack of education for necessary of Self-examination and clinical examination annual, leading to the spread of the disease further. In addition to genetic changes, environmental pollution, bad lifestyle , Obesity ,Lack of exercise are also factorsRead MoreGenome Wide Association Studies Essay1142 Words   |  5 Pageswide association study has been very useful in the recent years. It has helped researchers to detect genes that are not detected that easily. With the detection, it helps the researchers to prevent and treat the particular disease. Breast cancer has been one of the most common cancer in the world and the most common in women. Even though, breast cancer is detected easily with mammograms or self examination there are times where it c an be hard to detect. Genome wide association studies help to identifyRead MoreBreast Cancer : A Malignant Tumor1203 Words   |  5 PagesChapter I - Introduction Breast cancer is defined as a malignant tumor in the cells of the breast. A cancerous tumor develops when a group of malignant cells invade the tissue surrounding the breast and can spread to other parts of the body (Cancer.org, 2015). This type of cancer is most common in women. However, men can also fall victim to the disease. In 2015, about 230,000 new cases of breast cancer will be diagnosed in women and approximately 40,000 women will die from this disease (CancerRead MoreStrength Of Association1146 Words   |  5 Pagesrule out causality and may still be of significant effect on the disease under study. This is applied in a case where the exposure is in a common population. For instance passive smoking and lung cancer (Risk Ratio: 1.3) (Morein Stuart, n.d). Consistency: Repeated observation of an association in a different population under different circumstances showing the same results suggests that the results of a single study are not due to chance. Criticism: This should be applied cautiously to avoid chances

Sunday, December 22, 2019

Custom Written Essays Contrasting Gertrude and Ophelia...

Contrasting the Ladies in Hamlet How can anyone view or read the Shakespearean tragedy of Hamlet without observing an obvious differentiation between the characters of the two female characters? And yet, not all critics agree on even the most salient features of this contrast. Quite opposite the criminality of the king’s wife is the innocence of Ophelia – this view is generally expressed among Shakespearean critics. Jessie F. O’Donnell expresses the total innocence of the hero’s girlfriend in â€Å"Ophelia,† originally appearing in The American Shakespeare Magazine: O broken lily! how shall one rightly treat of her loveliness, her gentleness and the awful pathos of her fate? Who shall dare to hint that she was†¦show more content†¦Yet no one who reads the first soliloquy in the Second Quarto text, with its illuminating dramatic punctuation, can doubt for one moment that Shakespeare wished here to make full dramatic capital out of Gertrude’s infringement of ecclesiastical law [. . .] . (39) In the Introduction to Twentieth Century Interpretations of Hamlet, David Bevington analyzes the dissimilarity: â€Å"Characters also serve as foils to one another as well as to Hamlet. Gertrude wishfully sees in Ophelia the blushing bride of Hamlet, innocently free from the compromises and surrenders which Gertrude has never mastered the strength to escape† (9). Ophelia is so despondent at the death of Polonius and the alienation of Hamlet that she slips into madness – something that would never happen to Gertrude at the loss of a man. The queen has difficulty empathizing with the masculine point of view, even with that of her own son. She sees him attending the courtly social gathering in black, and refuses to tolerate it: Dear Hamlet, cast thy nighted color off, And let thine eye look like a friend on Denmark. Do not for ever with thy vailed lids Seek for thy noble father in the dust. (1.2) Likewise she expresses her wishes that the prince â€Å"go not to Wittenberg.† Later, when the hero’s supposed â€Å"madness† is the big concern, Gertrude analyzes her son’s condition: â€Å"IShow MoreRelated Shakespeares Hamlet - The Character of Ophelia Essay3341 Words   |  14 PagesHamlet: The Character of Ophelia  Ã‚  Ã‚  Ã‚  Ã‚        Ã‚   Concerning the Ophelia of Shakespeare’s tragic drama Hamlet, is she an innocent type or not? Is she a victim or not? This essay will explore these and other questions related to this character.    Rebecca West in â€Å"A Court and World Infected by the Disease of Corruption† viciously, and perhaps unfoundedly, attacks the virginity of Ophelia:    There is no more bizarre aspect of the misreading of Hamlet’s character than the assumptionRead MoreEssay on Interpreting Hamlet’s Ophelia3518 Words   |  15 PagesHamlet’s Ophelia Was Ophelia in love with Hamlet, or did she have more feeling for her father than for her boyfriend? In Shakespeare’s Hamlet was Ophelia’s madness contributed to by the prince’s rejection of her? The answers to these and other questions about this tragic figure will be given. Rebecca West in â€Å"A Court and World Infected by the Disease of Corruption† argues that Ophelia has no love for Hamlet, but only for her father: For the myth which has been built round Hamlet is never

Saturday, December 14, 2019

Positive Psychology in the Workplace Free Essays

Positive Psychology in the Workplace Sandie Tharp University of Charleston English II Nada Najjar March 30th, 2013 Positive Psychology in the Workplace It has been proven that positive psychology in the workplace can improve and enhance workplace satisfaction because employees are more confident and are better able to manage stress and adversity. Employees that are happier with their work environment are typically more motivated and are more likely to pursue growth and development. Job satisfaction is an engine that drives organizations to success and keeps turnover rate to a minimum. We will write a custom essay sample on Positive Psychology in the Workplace or any similar topic only for you Order Now An article from Harvard Business Review states that â€Å"a decade of research proves that happiness raises nearly every business and educational outcome: raising sales by 37%, productivity by 31%, and accuracy on tasks by 19%, as well as a myriad of health and quality of life improvements† (2011, Archor). Additionally, the author Archor asks if there is anything that a company can do to affect employee happiness and should a company invest resources into happiness. One avenue to happiness is using training as a tool that can be used to help employees to learn to manage stress. The first step is explaining the goal of the organization and how the employee contributes to that goal. Clear goals and expectations set out the mile post marking the marathon, by having clearly marked points of achievement for individuals or teams giving them a clear map to their success. It’s the manager’s responsibility to verify the goals of the team members, identify the common goal, and verify that they are in alignment with the organization goals. The King James Version of the Bible states â€Å"train up a child in the way he should go: and when he is old, he will not depart from it† (Proverbs. 2:6- King James Version). On the job training is the adult equivalent to raising a child. Putting subordinates on the path of success by providing the focus of the organizational instead of having the employee assuming or guessing about what to do causing stress and uncertainty. The economics of happiness in the workplace means that happy employees can help achieve organizational goals more easily because they are ready to give their best. Companies like Google and DreamWorks empower employees to use creative freedom in every day decision making. Google was a company that had to come up with a quick plan to reduce the turnover rate of their women employees because the turnover rate was affecting Google’s bottom line. In response, Google implemented a 5 month paid maternity plan, which gave new mothers the ability to take all the time upfront or to divide the time as needed. This new incentive plan reduced turnover by 50 percent among women and cost the firm no more than hiring a new employee. Google’s President Laszlo Bock, states â€Å"that if you factor in the savings in recruitment costs, granting mothers five months of leave doesn’t cost Google any more money† (2013,Manjool). The new maternity policy that Google has implemented exemplifies why Google has become one of the best employers in the world, taking a major cause of employee turnover from a crippling weakness to a world class strength. Carolinas Healthcare is a system that contains 32 affiliated hospitals in North and South Carolina and is one of the leading healthcare systems in the southeast; Carolinas Healthcare employs over 44,000 employees. Managing such a large number of employees would be a challenge for any organization, but CHS has stepped up to the plate and has met that challenge and is leading the way for positive work environment. During a recent employee survey conducted by Morehead and Associates, CHS ranked in the 90th percentile in the national ranking of employee satisfaction. Carolinas Healthcare System already has a reputation of being â€Å"An Employer of Choice,† recognized by the JD Power and Associates in 2011† (2012,Tarwater). This recognition has helped attracted top doctors and other medical professionals from all over the world. CHS concentrates on growth and expansion which helps generate excitement throughout the organization. Carolinas HealthCare System ecognizes that the employees are its most valuable asset and is dedicated to the growth and contentment of its employees. CHS recognizes that encouraging employees to continue educational growth will result in higher functioning, more competent workforce. Currently CHS offers an educational plan that reimburses employees for covered educational expenses and flexible scheduling around educational opportunities. Like Google with its maternity benefit, CHS has recognized that employees need and desire a clear path to growth and addresses this need with the educational assistance plan. Many organizations recognize the value of multiple bottom lines beyond monetary profit. Today the focus is on providing an environment based on positive interactions, outcomes and sustainability of the workforce. Organizations have come to realize that emotional intelligence and understanding is what allows employees to create positive interactions with customers and co-workers. According to Psych Central â€Å"research has shown that employees who are fully engaged in the work they do, and who have a sense of intrinsic motivation, are likely to perform better and a have better work outcomes† (2011, Wilner). To achieve these outcomes it is up to the organization to involve the employees and allow them to uncover their individual gifts and have the independence to use them for the common goals. Allowing employees to be involved gives them accountability for the success of the organization and a sense of pride, happiness and fulfillment. Positive psychology in the workplace can improve and enhance workplace satisfaction and have a positive impact on the organization’s bottom line. Companies like Google and Carolinas Healthcare understand the importance of a positive work environment. Both have provided pathways to fulfillment, whether through providing enhancement programs or through fostering positive interaction. Firms must continually look for avenues to promote growth, fulfillment and ultimately employee satisfaction if they are continue to improve the bottom line. It has been said that cheaper isn’t better; better is better. Today a better work force is cheaper than a cheap one. By continuing to harvest the fruits of a cohesive team built over time, an organization creates synergies that have a much greater return than the firm invests to create them. References Archor, S. (2011). â€Å"The Happiness Dividend†. Retrieved from http://blogs. hbr. org/cs/2011/06/the_happiness_dividend. html Bible – King James Version (1997). Proverbs 22:6. Hendrickson Publishers: Peabody. MA. Manjool, F. (2013). â€Å"Here’s How Google Became Such A Great Place To Work. Retrieved from http://www. huffingtonpost. com/2013/01/22/working-at-google_n_2526889. html Tarwater, M. (2012). â€Å"Employee Satisfaction†. Retrieved from www. carolinashealthcare. org Wilner, J. (2011). â€Å"5 Ways Positive Psychology can Improve the Workplace†. Retrieved from http://blogs. psychcentral. com/positive-psychology/2011/11/5-ways-positive-psychology-can-improve-the-workplace/ How to cite Positive Psychology in the Workplace, Papers

Thursday, December 5, 2019

Speech Enhancement Techniques and Their Comparison free essay sample

The implementation of the code is done using Graphic User Interface on MATLAB. Keywords— Speech enhancement, FFT, Spectral subtraction, Kalman filter, Wiener filter, Performance parameters I. INTRODUCTION Speech is the fundamental and common medium, hence important for us, to communicate. In general, there exists a need for voice based communications,human-machine/machine-machine interfaces, and automatic speech recognition systems to increase the reliability of these systems in noisy environments. In many cases, these systems work well in nearly noise-free conditions, but their performance deteriorates rapidly in noisy conditions. Therefore, improvement in existing pre-processing algorithms or introducing entire new class of algorithm for speech enhancement is always the objective of research community. In speech enhancement, the goal is to improve the quality of degraded speech. Speech enhancement algorithms are noise suppression techniques, using the knowledge from the field of hearing science, that mitigate the effect of the corrupting background noise, and hence improve the perceived speech quality and speech intelligibility. Enhancing of speech degraded by noise is used for many applications such as mobile phones, VoIP, teleconferencing systems, speech recognition, and hearing aids. The problem of improving performance of speech communication systems in noisy environments has been a challenging area for research for more than three decades now. Efforts to achieve higher quality and/or intelligibility of noisy speech may effectively end up improving performance of other speech applications such as speech coding/compression and speech recognition etc. given in [1][2][3][4][5]. Speech enhancement has three major goals: . To improve the quality and intelligibility of speech corrupted by background noise, reduce the perceptual fatigue. 2. To make speech coders robust when to input noise. 3. To make speech recognition systems more robust to noise. This project presents an overview of different speech enhancement methods and provides a review of some of the major aspects and approaches in this category. II. BAS IC BLOCK DIAGRAM The basic block diagram for speech enhancement is as shown below in Fig. 1. Fig. 1 Basic Block Diagram The noisy input signal is sent through the analysis window. Here, a few samples of the signal are selected at a time as the signal is continuous and big and cannot be processed in one go. Fast Fourier Transform is applied to convert the signal from time domain to frequency domain. The magnitude of noise and noisy speech are compared and noise is subtracted from the affected speech. The enhanced speech received is in frequency domain and hence requires to be converted back to frequency domain. This is done by taking Inverse Fourier Transform. Overlap and add method is applied to the recovered enhanced signal so as to compensate for the windowing method applied in the beginning. In our project, since the signal applied at input has few samples, windowing method is not implemented. III. DESCRIPTION A. SPECTRAL SUBTRACTION METHOD The Spectral Subtraction method is the most widely used due to the simplicity of implementation and also due to low computational load. As studied in [5] [6], Spectral subtraction is a method for restoration of the power spectrum or the magnitude spectrum of a signal observed in additive noise, through subtraction of an estimate of the average noise spectrum from the noisy signal spectrum. It reduces the effect of background noise based on the STSA estimation technique. The basic power spectral subtraction technique is popular due to its simple underlying concept and its effectiveness in enhancing speech degraded by additive noise. The basic principle of the spectral subtraction method is to subtract the magnitude spectrum of noise from that of the noisy Speech. The noise spectrum can be estimated, and updated, during the periods when the signal is absent or when only noise is present. The noise is assumed to be uncorrelated and additive to the Speech signal. An estimate of the noise signal is measured during silence or non-Speech activity in the signal. The phase of the noisy Speech is kept unchanged, since it is assumed that the phase distortion is not perceived by human ear. However the subtraction type algorithms have a serious drawback in that the enhanced Speech is accompanied by unpleasant musical noise artifact, which is characterized by tones with random frequencies. The simple subtraction processing comes with a price. The subtraction process needs to be done carefully to avoid any Speech distortion. If too much is subtracted, then some Speech information might be removed as well, while if too little is subtracted then much of the interfering noise remains. The block diagram given in [7] is as shown in Fig. 2. Fig. 2 SPECTRAL SUBTRACTION Noisy Speech is given as an input to this filter. Windowing is done in order to take fixed number samples of the signal which is continuous in nature. Inthis method only the magnitude is considered. The phase part is not taken into consideration as it increases the complexity and calculations. Fourier transform is applied to the signal in order to convert the signal from timedomain to frequency domain. This helps us to obtain magnitude and phase as separate values. The magnitude of estimated noise is subtracted from themagnitude of noisy signal and an enhanced Speech is obtained at the output of spectral modification block. Inverse Fourier transform of the enhanced speech is taken so as to obtain the signal in its time domain form. Phase ofsignal, in its original form, is added to the magnitude at this stage. Thus weobtain an enhanced version of the noisy Speech signal at the output end. B. WEINER FILTER Speech processing has been a growing and dynamic field for more than two decades and there is every indication that this growth will continue and even accelerate. A useful approach to filter optimization problem is to minimize the mean squared value of the error signal that is defined as the differencebetween some desired response and the actual filter output. There are workslikes [13][14], which describes Weiner filters as class of optimum linear filterswhich involve linear estimation of a desired signal sequence from anotherrelated sequence. This technique is widely used in the field of signal processing. Weiner filter is a common and adaptive filter technique and is the solution for stationary input signals. The filter has its origin in Kalmans document (1960) where it is describedas a recursive solution for the linear filtering problem for discrete data. Theresearch was in a wide context of state-space models, where the point is the estimation through the recursive least squares. The goal of the Wiener filter is to filter out noise that has corrupted a signal. It is based on a statisticalapproach. Typical filters are designed for a desired frequency response. However, the design of wiener filter takes a different approach. One is assumedto have knowledge of the spectral properties of the original signal and thenoise, nd one seeks the linear time invariant filter whose output would comeas close to the original signal as possible. Fig. 3 WEINER FILTER Shown above in Fig. 3, is the block diagram of Weiner filter. In this process, mean of all the samples is calculated. Deviation of each sample from the mean is found and the summation is represented as Pd(w). Mean of noise power is represent ed as Py(w). Py(w) is subtracted from Pd(w) and the transfer function is calculated as shown. Thus we get enhanced speech signal at the output of the filter. C. MINIMUM MEAN SQUARE ERROR Given that some a priori knowledge of the radar SNR is available, a minimum mean-squared error estimator can be implemented. This estimator is the discrete implementation of a Wiener filter and minimizes the estimate error due to both noise and clutter. In other words if the matched filter maximizes signal to noise, and the ML estimator maximizes signal to clutter, the MMSE estimator can be said to maximize signal to interference, where interference is defined as the summation of both clutter and noise energy. Accordingly, this estimator provides SAR images superior to both correlation and ML processing for all SNR. As presented in [9], the STSA estimation problem formulated here is that of estimating the modulus of each complex Fourier expansion coefficient of the Speech signal in a given analysis frame from the noisy Speech in that frame. This formulation is motivated by the fact that the Fourier expansion coefficients of a given signal segment are samples of its Fourier transform, and by the close relation between the Fourier series expansion and the discrete Fourier transform is given in [10][11]. The latter relation enables an efficient implementation of the resulting algorithm by utilizing the FFT algorithm. The basic formula for power spectral density of MMSE filter as given in [8] is: E|Xk|2Y? k=? k1+? k? dk+(? 1+? kYk)2 Where,? k is the a priori SNR. The basic block diagram of MMSE filter is shown in Fig. 4 Fig. 4 MMSE Filter To derive the MMSE STSA estimator, the a priori probability distribution of the Speech and noise Fourier expansion coefficients should be known. Since in practice they are unknown, one can think of measuring each probability distribution or, alternatively, assume a reasonable statistical model. In the discussed problem, the Speech and possibly also the noise are neither stationary nor Ergodic processes. This fact excludes the convenient possibility of obtaining the above probability distributions by examining the long-time behavior of each process. Hence, the only way which can be used is to examine independent sample functions belonging to the ensemble of each process, e. g. , for the Speech process these sample functions can be obtained from different speakers. However, since the probability distributions we are dealing with are time-varying (due to non-stationary nature of processes), their measurement and characterization by the above way is complicated, and the entire procedure seems to be impracticable. The only disadvantage of the MMSE processor as explained in [12] is the huge additional complexity in determining the linear estimator. Additionally, for large problems, the matrix inverse operation required to implement the MMSE estimator is very problematic. Especially in the field of radar signal processing computing the inverse of the large matrices can really slow down the processing speed. An iterative implementation of the MMSE algorithm can be developed where the data vector is split into smaller segments to reduce processing time. D. KALMAN FILTER Described in works of[16][17], Kalman filter (KF) algorithm, an iterative implementation of the MMSE estimator is proposed, developed, analysed andoptimized. It has been shown that the processing speed can be decreased, bybreaking the data vector into an optimal number of segments. Kalman filtering is known as an effective Speech Enhancement technique, in which Speechsignal is usually modelled as autoregressive (AR) process and represented inthe state-space domain. In the above context, all the Kalman filter-basedapproaches proposed in the past operate in two steps. They first estimatethe noise and the driving variances and parameters of the signal model, thenestimate the Speech signal. It uses a systems dynamic model (i. e. , physical laws of motion), knowncontrol inputs to that system, and measurements (such as from sensors) toform an estimate of the systems varying quantities that is better than theestimate obtained by using any one measurement alone. As such, it is acommon sensor fusion algorithm. The Kalman filter averages a predictionof a systems state with a new measurement using a weighted average. Thepurpose of the weights is that values with better estimated uncertainty are trusted more. The weights are calculated from the covariance, a measureof the estimated uncertainty of the prediction of the systems state. The result of the weighted average is a new state estimate that lies in between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated every time step, with new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only thelast best guess and not the entire history of a systems state to calculate a newstate. When performing actual calculations for the filter, the state estimateand covariance are coded into matrices to handle the multiple dimensions involved in a single set of calculations. This allows for representation of linearrelationship between different state variables such as position, velocity, andacceleration in any of the transition models or covariance. The use of Kalmanfilter for Speech Enhancement was first introduced by Paliwal (1987). Thismethod however is best suitable for reduction of white noise to comply withKalman assumption. In deriving Kalman equations it is normally assumed that the process noiseis uncorrelated and has a normal distribution. This assumption leads towhiteness character of this noise. It is assumed that Speech signal is stationary during each frame that is the AR model of Speech remains the sameacross the segment. Kalman filter is an adaptive least square error filterthat provides an efficient computational recursive solution for estimating asignal in presence of Gaussian noises. Kalman filter theory is based on astate-space approach in which a state equation odels the dynamics of thesignal generation process and an observation equation models the noisy anddistorted observation signal. The advantages of Kalman Filtering Technique [18] are: It avoids the influence of possible structural changes on the result. The recursive estimationstarts from an initial sample and updates the estimations by adding a newobservation until the end of the data. This implies that the most recent coefficients estimation is affected by the distant history; in presence of structuralchanges the data series can be cut. This cut can be corrected through thesequential estimations but with the biggest standard error. Like this, theKalman filter, like other recursive methods, uses all the series history butwith one advantage: It tries to estimate a stochastic path of the coefficientsinstead of a deterministic one. In this way it solves the possible estimationcut when structural changes happen. This filter is in equal terms with Gauss-Markov theorem and this gives to Kalman filter its enormous power to solvea wide range of problems on statistic inference. The filter is distinguishedby its skill to predict the state of a model in the past, present and future,although the exact nature of the modelled system is unknown. The dynamicmodelling of a system is one of the key features which distinguish the Kalmanmethod. The disadvantages of Kalman Filtering Technique are: That it is necessary toknow the initial conditions of the mean and variance state vector to start therecursive algorithm. There is no general consent over the way of determiningthe initial conditions. Fig. 5 As shown in Fig. 5, the input Speech signal is taken and distortion of noise isin the signal is found. The current output is based on the past output andcurrent input which is solved using Yules equation. All the parameters are represented in the form of state space matrix because it makes calculationseasier. Next filter gain is calculated and noise is then removed from the noisySpeech input to get enhanced Speech signal. IV. MEASURES OF PERFORMANCE PARAMETERS A. SIGNAL-TO-NOISE RATIO (SNR) Signal-to-noise ratio (often abbreviated as SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level ofbackground noise. Signal-to-noise ratio is sometimes used informally to refer to the ratio of usefulinformation to false or irrelevant data in a conversation or exchange. Signal-to-noise ratio is defined as the power ratio between a signal (meaningful information)and the background noise (unwanted signal). It is measured in dBs SNR=10? log10mean(Input2)mean(Input2-Enhanced2) B. MEAN SQUARE ERROR (MSE) In statistics, the Mean Squared Error (MSE) of an estimator is one of many waysto quantify the difference between values implied by an estimator and the truevalues of the quantity being estimated. MSE is a risk function, corresponding tothe expected value of the squared error loss or quadratic loss. MSE measures theaverage of the squares of the errors. The error is the amount by which the valueimplied by the estimator differs from the quantity to be estimated. The differenceoccurs because of randomness or because the estimator doesnt account for information that could produce a more accurate estimate. MSE=1length(Input)? (Enhanced-Input)2 C. NORMALIZED ROOT MEAN SQUARE ERROR (NRMSE) The Root Mean Square Error (RMSE) also known as Root-Mean-Square Deviati on(RMSD) is a frequently used measure of the differences between values predictedby a model or an estimator and the values actually observed. These individualdifferences are called residuals when the calculations are performed over the datasample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent. NRMSE=mean[(Input-Enhanced)2]mean{[Input-mean(Input)]2} D. PEAK SIGNAL-TO-NOISE RATIO(PSNR) Peak Signal-to-Noise Ratio, often abbreviated PSNR, is an engineering term forthe ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signalshave a very wide dynamic range, PSNR is usually expressed in terms of the logarithmic decibel scale. PSNR is most commonly used to measure the quality of reconstruction of lossy compression codecs (e. g. , for image compression). The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codecs, PSNR is an approximation to human perception of reconstruction quality. Although a higher PSNR generally indicates that the reconstruction is of higher quality, in some cases the reverse may be true. PSNR=10? log10length? max? [Input2]Input2-Enhanced2 E. DISTORTION(AAD) Distortion (or warping) is the alteration of the original shape (or other characteristic) of something, such as an object, image, sound or waveform. Distortion isusually unwanted, and often efforts are to lessen it. The addition of noise or otheroutside signals (hum, interference) is not deemed distortion, though the effects ofquantization distortion are sometimes deemed noise. In this project we use theparameter AAD to measure the distortion in the given Speech signal. AAD=1lengthInput? (Enhanced-Input) V. APPLICATIONS AND FUTURE SCOPE * Cell phone speech enhancement * Pay phones in a noisy environment * Air-ground communication systems * Teleconferencing systems * Hearing aids VI. EXPERIMENTAL RESULTS The above mentioned techniques of speech enhancement were applied to the noisy speech input and the performance parameters were evaluated as below. Fig. 6 Clean Speech Fig. 7 Noisy Speech Fig. 8 Spectral Subtraction Output Fig. 9Weiner Filter Output Fig. 10MMSE Filter Output Fig. 11 Kalman Filter Output VII. CONCLUSION The technique most suitable for speech enhancement is the one which provides robustness to environmental factors, robustness to acoustical inputs. Table 1. Parameters In this project, we havereviewed the methodologies and principles of various techniques and presented the analysis in GUI MATLAB Based on theperformance parameters the following points have been concluded: (a) Wiener Filter follows statistical approach and could be tuned to provideoptimal performance b) Kalman has the ability to estimate accurately by using autoregressive(AR)modeling and is suitable for real-time applications. (c) Spectral Subtraction is a real time filter which is relatively easy toimplement for stationary noise. (d) MMSE provides best values for the most parameters under given conditionsand hence is most suitable technique for spee ch enhancement Agraphical representation for comparison of the above mentioned techniques is as below: A. SNR Fig 12. SNR Comparison The above graph provides a comparison between input SNR for each technique and their respective output SNR. The signal to noise ratio for MMSE is more than all filters for any value of input SNR whereas that of Spectral subtraction is the least for all inputs SNR. B. PSNR Fig 13. PSNR Comparison The graph given shows the value of peak Signal-to-noise ratio for all speech enhancement techniques. The value of PSNR is greatest for Wiener filter for all input SNR. C. MSE Fig 13. MSE Comaprison The graph gives the comparison of all four speech enhancement methods for mean square error. At input SNR=2DB,error reduced is the most in MMSE. Kalman filter works most efficiently at SNR input=5DB. Noise suppression is least in wiener filter for the given conditions. VIII. REFERENCES [1] Speech Signal Processing by School of Electronic Information,Wuhan University. [2] Recent Advancements in Speech Enhancement by Yariv Ephraimand Israel Cohen, March 9, 2004. [3] Speech Enhancement using Adaptive Filters by T. Lalith Kumarand Soudara Rajan. [4] http://en. wikipedia. org/wiki [5] Overview of Speech Enhancement Techniques for Automatic Speaker recognition by Javier Ortega-Garca and Joaqun Gonzlez-Rodrguez [6] Advanced Digital Signal Processing and Noise Reduction, SecondEdition by Saeed V. Vaseghi. 7] Transform Based Speech Enhancement Techniques by Soon IngYann. [8] Speech Enhancement using a Laplacian based MMSE estimator of the magnitude spectrum byDr. Bin Chen. [9] Linear Prediction Algorithms by Mohit Garg, IIT-B. [10] Speech Enhancement Using a Minimum Mean Square Error Short Time Spectral Amplitude Estimator by Yaric Ebrahim. [11] A Laplacian based MMSE estimator for Speech Enhancement by Bin Chen, Philipos C. Loizou. [12] Minimum Mean Square Error Filtering:Autocorrelation/Covariance, General Delays and Multirate Systems by Peter Kabal. [13] Improve Speech Enhancement using Weiner Filtering by S. China, Venkateswarlu, Dr. K. Satya Prasad, Dr. A. SubbaRami Reddy. [14] Performance Analysis of Multichannel Wiener amp; Filter-BasedNoise Reduction in Hearing Aids under Second Order Statistics Estimation Errors by Bram Cornelis, Marc Moonen and JanWouters. [15] A Wiener Filtering Ian V. Oppenheim and George C. Verghese,2010. [16] Dual channel Speech Enhancement using Hadamard LMS algorithm with DCT preprocessing technique by D. Deepa. [17] An improved SNR estimator for Speech Enhancement by Yao Ren and Michael T. Johnson. [18] http://www. mathworks. com/support