i-manager Publications

i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 11 Issue 2

Most Cited

Volume 5 Issue 4 October - December 2017

Research Paper

Image Segmentation using Fuzzy C means Clustering with Mahalanobis Distance Norm

Jincy V. Raj * , S. Jini Mol , Jisha G. Das*, R.S. Sajitha ****

*-**** B.E Scholars, Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, India.

Raj, J, V., Mol, J, S., Das, J, G., and Sajitha, R, S. (2017). Image Segmentation Using Fuzzy C means Clustering With Mahalanobis Distance Norm. i-manager's Journal on Digital Signal Processing, 5(4), 1-9. https://doi.org/10.26634/jdp.5.4.14560

Abstract

In order to map the image, color intensity of the image, or for detecting the object image segmentation is used. It is one of the important procedures used by many of the algorithms. Fuzzy C Means algorithm is one of the effective and powerful image segmentation algorithms compared to all other segments. To describe or explain the dissimilarity in-between Clustered Prototype and the data acquired, FCM uses Euclidean distance to resolve (Zhao et al., 2015). Since the mean information of the cluster is only characterized by the Euclidean distance, both the cluster divergence and noise is made sensitive. Mahalanobis distance is more accurate than the Euclidean distance as a dissimilarity measure when they are used for image segmentation, and they also used to define the mean and covariance of a cluster. The final experimental results show that the Mahalanobis distance is more accurate than the Euclidean distance.

References

[1]. Cherkaoui, A., & Barrah, H. (2015, December). Fast robust fuzzy clustering algorithm for grayscale image segmentation. In Xème Conférence Internationale: Conception et Production Intégrées.

[2]. Dungarwal, P. R., & Patil, D. D. (2017). An Ameliorate Technique for brain lumps detection using Fuzzy C-Means Clustering. International Research Journal of Engineering and Technology (IRJET), 4(1), 335-338.

[3]. Gayathri, R., & Kuppusamy, K. (2015). Medical Image Segmentation based on Improved Fuzzy Clustering algorithm with Bias Field Estimation. International Journal of Computer Science and Mobile Computing, 4(7), 480-488.

[4]. Ghogge, R. (2014). Brain Tumour Detection using KMeans and Fuzzy C Means Clustering Algorithm. International Journal of Science, Engineering and Technology Research (IJSETR), 3(7), 2081-2085.

[5]. Guo, Y., Liu, K., Wu, Q., Hong, Q., & Zhang, H. (2015). A new spatial fuzzy c-means for spatial clustering. WSEAS Transactions on Computer, 14, 369-381.

[6]. Jui, S. L., Lin, C., Guan, H., Abraham, A., Hassanien, A. E., & Xiao, K. (2014, July). Fuzzy c-means with wavelet filtration for MR image segmentation. In Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on (pp. 12-16). IEEE.

[7]. Liu, H. C., Yih, J. M., & Liu, S. W. (2007). Fuzzy C-mean algorithm based on Mahalanobis distances and better initial values. In Information Sciences 2007 (pp. 1398- 1404).

[8]. McLachlan, G, J. (1999). Mahalanobis Distance. Resonance, 20-26.

[9]. Rezaee, M. R., Lelieveldt, B. P., & Reiber, J. H. (1998). A new cluster validity index for the Fuzzy C-Mean. Pattern Recognition Letters, 19(3-4), 237-246.

[10]. Saravanan, P., & Sailakshmi, P. (2015). Missing Value Imputation using Fuzzy Possibilistic C Means Optimized with Support Vector Regression and Genetic Algorithm. Journal

of Theoretical & Applied Information Technology, 72(1), 34-39.

[11]. Silviu, B. (n.d.). Fuzzy Clustering. Babes-Bolyai University. Retrieved from https://pdfs.semantic scholar.org/13e4/ 3b4710620fb3e6e303d612f541 e8e74777d6.pdf?_ga= 2.175216610.113536729. 1526375308-520127494. 1525682930

[12]. Suganya, R., & Shanthi, R. (2012). Fuzzy C- Means Algorithm- A Review. International Journal of Scientific and Research Publications, 2(11), 1-3.

[13]. Tamilselvan, K. S., Murugesan, G., & Kandasamy, K. (2015). A novel image segmentation algorithm for clinical CT images using Wavelet Transform, Curvelet Transform and Multiple Kernel FCM. Applied Mathematical Sciences, 9(48), 2351-2362.

[14]. Tembhekar, P. A., Thakare, M. N., & Dhande, S. A. (2015). Spatial Fuzzy Clustering With Level Set Method For MRI Image. International Journal of Advanced Research in Computer and Communication Engineering, 4(7), 250- 252.

[15]. Ye, A. X., & Jin, Y. X. (2016). A Fuzzy C-Means clustering algorithm based on improved quantum genetic algorithm. International Journal of Database Theory and Application, 9(1), 227-236

[16]. Zhao, X., Li, Y., & Zhao, Q. (2015). Mahalanobis distance based on fuzzy clustering algorithm for image segmentation. Digital Signal Processing, 43, 8-16.

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Research Paper

Frequency Based Filtering for Voice Activity Detection

V.Adlin Vini*

Post Graduate, Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Vini, A, V. (2017). Frequency Based Filtering for Voice Activity Detection. i-manager's Journal on Digital Signal Processing, 5(4), 10-19. https://doi.org/10.26634/jdp.5.4.14561

Abstract

Signal Processing is used to bring out the speech in a degraded signal. Amplitude of the signal is obtained by using the SFF (Single Frequency Filtering). Spectral and Temporal resolutions are compared by using three different methods, which are discussed in this paper. Voice Activity Detection is the process in which any noise or disturbance that are made to the speech signal is detected. In this paper, the author has proposed Voice Activity Detection system with the help of Frequency based filtering method. The experimental results show that it gives better results compared to the existing systems.

References

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[11]. Ong, W. Q., Tan, A. W. C., Vengadasalam, V. V., Tan, C. H., & Ooi, T. H. (2017). Real-time Robust Voice Activity Detection using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter. Entropy, 19(11), 487.

[12]. Pham, T. V., Stark, M., & Rank, E. (2010, October). Performance analysis of wavelet subband based voice activity detection in cocktail party environment. In Advanced Technologies for Communications (ATC), 2010

International Conference on (pp. 85-88). IEEE.

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[14]. Sehgal, A., & Kehtarnavaz, N. (2018). A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection. IEEE Access, 6, 9017-9026.

[15]. Sharmida, A. H., & Athira, A. P. (2016). A robust observation model for automatic speech recognition with Adaptive Thresholding. International Research Journal of Engineering and Technology (IRJET), 3(8), 644-649.

[16]. Sohn, J., & Sung, W. (1998, May). A Voice Activity Detector employing soft decision based noise spectrum adaptation. In Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on (Vol. 1, pp. 365-368). IEEE.

[17]. Zhang, X. L., & Wang, D. (2016). Boosting contextual information for deep neural network based Voice Activity Detection. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(2), 252-264.

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Review Paper

Removal of Noise in Speech Signal – A Review

H. Hensiba*

Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Hensiba, H. (2017). Removal of Noise in Speech Signal – A Review. i-manager's Journal on Digital Signal Processing, 5(4), 20-26. https://doi.org/10.26634/jdp.5.4.14562

Abstract

In almost all the acoustic environments, noise is always considered as a ubiquitous one. The quality of the signal gets degraded and also contaminated because of the infection, which was caused by various sources when one speaks through the microphone. Here there is a possibility, where there may be a harm caused when human to machine communication happens. The digital filtering problem is considered in this paper, which is the estimation of the clean speech from Noise detection as well as Noise reduction. The estimation is done through linear filtering of noise in the speech signal. In this paper, the author has reviewed different and various speech signal processing techniques, where the noise gets affected and also how the noise gets removed.

References

[1]. AL-Allaf, O. N. (2015). Removing Noise from Speech Signals using different approaches of Artificial Neural Networks. I. J. Information Technology and Computer Science, 07, 8-18.

[2]. Alvarez, E., Mendez, R., & Langwagen, G. (2004, October). Detection of clicks using sinusoidal modeling for the confirmation of the clicks. In Proceedings of the 7 International Conference on Digital Audio Effects (DAFx04) (Vol. 8).

[3]. Anzalone, M. C., Calandruccio, L., Doherty, K. A., & Carney, L. H. (2006). Determination of the potential benefit of time-frequency gain manipulation. Ear and Hearing, 27(5), 480-492.

[4]. Arbogast, T. L., Mason, C. R., & Kidd Jr, G. (2002). The effect of spatial separation on informational and energetic masking of speech. The Journal of the Acoustical Society of America, 112(5), 2086-2098.

[5]. Brons, I., Houben, R., & Dreschler, W. A. (2012). Perceptual effects of noise reduction by time-frequency masking of noisy speech. The Journal of the Acoustical Society of America, 132(4), 2690-2699.

[6]. Brungart, D. S., Chang, P. S., Simpson, B. D., & Wang, D. (2006). Isolating the energetic component of speech-onspeech masking with ideal time-frequency segregation.

The Journal of the Acoustical Society of America, 120(6), 4007-4018.

[7]. Canazza, S., De Poli, G., & Mian, G. A. (2010). Restoration of audio documents by means of extended Kalman filter. IEEE Transactions on Audio, Speech, and Language Processing, 18(6), 1107-1115.

[8]. Cao, S., Li, L., & Wu, X. (2011). Improvement of intelligibility of ideal binary-masked noisy speech by adding background noise. The Journal of the Acoustical Society of America, 129(4), 2227-2236.

[9]. Cooke, M., Green, P., Josifovski, L., & Vizinho, A., (2001). Robust automatic speech recognition with missing and unreliable acoustic data. Speech Comm., 34, 267-285.

[10]. Garg, K., & Jain, G. (2016, September). A comparative study of noise reduction techniques for automatic speech recognition systems. In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on (pp. 2098-2103). IEEE.

[11]. Ma, N., Bouchard, M., & Goubran, R. A. (2006). Speech enhancement using a masking threshold constrained Kalman filter and its heuristic implementations. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 19-32.

[12]. Madhu, N., Spriet, A., Jansen, S., Koning, R., & Wouters, J. (2013). The potential for speech intelligibility improvement using the ideal binary mask and the ideal wiener filter in single channel noise reduction systems: Application to auditory prostheses. IEEE Transactions on Audio, Speech, and Language Processing, 21(1), 63-72.

[13]. Oudre, L. (2015). Automatic detection and removal of impulsive noise in audio signals. Image Processing on Line, 5, 267-281.

[14]. Rouat, J. (2008). Computational auditory scene analysis: Principles, algorithms, and applications (Wang, D. & Brown, G. J., Eds.; 2006) [Book Review]. IEEE Transactions on Neural Networks, 19(1), 199-199.

[15]. Saha, G., Chakroborty, S., & Senapati, S. (2005, January). A new silence removal and endpoint detection algorithm for speech and speaker recognition applications. In Proceedings of the 11 National Conference on Communications (NCC) (pp. 291-295).

[16]. Shirzadi, S., Kia, M, S, S., & Hashemi, S, T. (2016). Noise Removing of Audio Speech Signals by Means of Kalman Filter. International Journal of Advanced Biotechnology and Research (IJBR), 7, 98-103.

[17]. Wang, D. (2008). Time-frequency masking for speech separation and its potential for hearing aid design. Trends in Amplification, 12(4), 332-353.

[18]. Wang, D., Kjems, U., Pedersen, M. S., Boldt, J. B., & Lunner, T. (2009). Speech intelligibility in background noise with ideal binary time-frequency masking. The Journal of the Acoustical Society of America, 125(4), 2336-2347.

[19]. Wieland, B., Urban, K., & Funken, S. (2009). Speech Signal Noise Reduction with Wavelets (Doctoral Dissertation, Verlag nicht ermittelbar).

[20]. Zhang, X., & Xiong, Y. (2009). Impulse noise removal using directional difference based noise detector and Adaptive Weighted Mean Filter. IEEE Signal Processing Letters, 16(4), 295-298.

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Survey Paper

A Survey on Different Noise Removal Techniques in Images

S. Eljin*

Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, India.

Eljin, S. (2017). A Survey on Different Noise Removal Techniques in Images. i-manager's Journal on Digital Signal Processing, 5(4), 27-33. https://doi.org/10.26634/jdp.5.4.14563

Abstract

The process of removing noise from the signal is known as Noise Reduction. Both the digital and analog recordable devices can be affected by noise. Noise can be of two variations, it can be either a coherent noise which could be introduced by the algorithm or they can be of non-coherent with white or random noise. Since the structure of the medium is a grained one, noise is introduced in both the photographic and magnetic taped scenarios accordingly. Noise can be reduced by different techniques with a corresponding algorithm or methodology, whereas in this paper, the author comprises the survey of different noise removal techniques from different authors’ point of view.

References

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[6]. Chou, H. H., Hsu, L. Y., & Hu, H. T. (2013). Turbulent-PSObased fuzzy image filter with no-reference measures for high-density impulse noise. IEEE Transactions on Cybernetics, 43(1), 296-307.

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i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 5 Issue 4 October - December 2017

Image Segmentation using Fuzzy C means Clustering with Mahalanobis Distance Norm

Jincy V. Raj * , S. Jini Mol **, Jisha G. Das***, R.S. Sajitha ****

*-**** B.E Scholars, Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, India.

Raj, J, V., Mol, J, S., Das, J, G., and Sajitha, R, S. (2017). Image Segmentation Using Fuzzy C means Clustering With Mahalanobis Distance Norm. i-manager's Journal on Digital Signal Processing, 5(4), 1-9. https://doi.org/10.26634/jdp.5.4.14560

Abstract

References

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Frequency Based Filtering for Voice Activity Detection

V.Adlin Vini*

Post Graduate, Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Vini, A, V. (2017). Frequency Based Filtering for Voice Activity Detection. i-manager's Journal on Digital Signal Processing, 5(4), 10-19. https://doi.org/10.26634/jdp.5.4.14561

Abstract

References

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Removal of Noise in Speech Signal – A Review

H. Hensiba*

Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Hensiba, H. (2017). Removal of Noise in Speech Signal – A Review. i-manager's Journal on Digital Signal Processing, 5(4), 20-26. https://doi.org/10.26634/jdp.5.4.14562

Abstract

References

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A Survey on Different Noise Removal Techniques in Images

S. Eljin*

Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, India.

Eljin, S. (2017). A Survey on Different Noise Removal Techniques in Images. i-manager's Journal on Digital Signal Processing, 5(4), 27-33. https://doi.org/10.26634/jdp.5.4.14563

Abstract

References

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Jincy V. Raj * , S. Jini Mol , Jisha G. Das*, R.S. Sajitha ****