Denosing speech signan using fast independent component

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1. DENOISING SPEECH SIGNAL USING FAST INDEPENDENT COMPONENT ANALYSIS METHOD(FAST ICA) College Name: College Address: Department of Electronics and Communication Engineering

2. CONTENTS: Introduction Aim of the Project Software Requirements Block Diagram Flowchart Working Independent Component Analysis Implementation and Results Applications Advantages Conclusion and Future Scope References

3. INTRODUCTION Independent component analysis (ICA) is a novel statistical technique in signal processing and machine learning that aims at finding linear projections of the data that maximize their mutual independence. Its main applications are blind source separation (BSS) and feature extraction. In recent years, ICA has been attracted a lot of attention in speech processing application such as multiple channels speech blind separation. When applied to speech frames, ICA provides a linear representation that maximizes the statistical independence of its coefficients, and therefore finds the directions with respect to which the coefficients are as sparsely distributes as possible

4. AIM OF THE PROJECT The main aim of the project is to remove the noise from the speech signals so that useful information can be extracted. Speech enhancement algorithm aims to improve the quality of speech for various different applications. With the development of communication systems, there is a strong need to develop speech enhancement algorithms. A speech enhancement system helps in increasing the quality of noisy speech. General speech enhancement system approaches are divided into two main categories: multi-channel and single channel methods. Some multi-channel methods are blind source separation(ICA), beamforming algorithms and generalized side lobe cancellation algorithms. Some well-known single channel methods are spectral subtraction, wiener filter and minimum mean-square error estimator

5. SOFTWARE: MATLAB R2021A SOFTWARE REQUIREMENTS

6. MATLAB R2021A MATLAB (an abbreviation of "matrix laboratory") is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran.

7. BLOCK DIAGRAM

8. FLOWCHART

9. WORKING The Three different noise free sources as input are taken and are mixed with each other to generate three noise sources in such a way that, in each noise signal a particular message signal dominates in terms of pitch power and amplitude The three noise speech signals are given to Fast ICA algorithm which does Independent component analysis and separates the noise from signal and gives us the filtered noise free signal

10. INDEPENDENT COMPONENT ANALYSIS Independent Component Analysis (ICA) is a machine learning technique to separate independent sources from a mixed signal. Unlike principal component analysis which focuses on maximizing the variance of the data points, the independent component analysis focuses on independence, i.e. independent components. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation. Independent component analysis attempts to decompose a multivariate signal into independent non-Gaussian signals. As an example, sound is usually a signal that is composed of the numerical addition, at each time t, of signals from several sources. 

11. FAST ICA ALGORITHM Algorithm -   Fast ICA Input:   Number of desired components Input:   Pre-whitened matrix, where each column represents an N-dimensional sample, where  C<=N        Output:   Un-mixing matrix where each column projects X onto independent component . Output:   Independent components matrix, with M columns representing a sample with C dimensions.

12. IMPLEMENTATION AND RESULT In this project, the noise signal is filtered and desired message signal is obtained. The proposed and implemented system uses Fast Independent Component analysis algorithm to remove noise from the source signal The Fast Independent Component analysis algorithm is implemented using MATLABR2021A Software The obtained signal is represented in the Kalman spectrograph.

13. IMPLEMENTATION AND RESULTS Noisy signal Noise free signal

14. APPLICATIONS Blind source separation Image denoising Medical signal processing- fMRI, ECG, EEG Modelling of the hippocampus and visual cortex Compression, redundancy reduction Watermarking Clustering Time series analysis(Stock market, microarray data)

15. ADVANTAGES AND DIS-ADVANTAGES ADVANTAGES Fast ICA is parallel and distributed Computationally efficient and requires less memory Independent components can be estimated one by one which again decreases the computational load LIMITATIONS The sources must be statistically independent. The sources must have non Gaussian distributions. However, ICA can still estimate the sources with small degree of non-gaussianity. The number of available mixtures N must be at least the same as the number of the independent components M. The mixtures must be (can be assumed as) linear combination of the independent sources.

16. CONCLUSION AND FUTURE SCOPE Speech enhancement has substantial interest in the utilization of speaker identification, video-conference, speech transmission through communication channels, speech-based biometric system, mobile phones, hearing aids, microphones, voice conversion etc. A substantial number of methods from traditional techniques and machine learning can be utilized to process and remove the additive noise from a speech signal. With the advancement of machine learning and deep learning, classification of speech has become more significant. Methods of speech enhancement consist of different stages, such as feature extraction of the input speech signal, feature selection, followed by classification. Deep learning techniques are also an emerging field in the classification domain, so signal denoising can be done using deep learning algorithms which provides more accuracy and possibly with less errors. The widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems overcomes present challenges

17. REFERENCES: [1] Comon, Pierre (1994): "Independent Component Analysis: a new concept?", Signal Processing, 36(3):287–314 (The original paper describing the concept of ICA) [2] S. Makeig, A.J. Bell, T.-P. Jung, and T.-J. Sejnowski. Independent component analysis of electro-encephalographic data. In Advances in Neural Information Processing Systems 8, pp. 145-151. MIT Press, 1996. [3] S. Li and T.J. Sejnowski, Adaptive separation of mixed broadband sound sources with delays by a beam-forming Herault-Jutten network, IEEE Journal of Oceanic Engineering Vol.20,No. 1, pp.73-79,1994 [4] L. Bohy, M. Neve, D. Samyde, and J. jacques Quisquater. Principal and independent component analysis for crypto-systems with hardware unmasked units. In proceedings of e-Smart 2003, 2003 [5] Draper B., Baek K., Bartlett M., Beveridge J. Recognizing faces with PCA and ICA Comput. Vis. Image Underst., 91 (1–2) (2003), pp. 115-137 [6] S. Amari, A. Cichocki, and H. Yang. A new learning algorithm for blind signal separation. In Advances in neural information processing systems, pages 757–763, 1996. [7] He, F. He, and T. Zhu. Large-scale super-Gaussian sources separation using fast-ICA with rational nonlinearities. International Journal of Adaptive Control and Signal Processing, 31(3):379–397, 2017

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