IJIGSP Vol. 14, No. 3, 8 Jun. 2022
Cover page and Table of Contents: PDF (size: 1725KB)
Full Text (PDF, 1725KB), PP.11-22
Views: 0 Downloads: 0
Speech Enhancement, Normalized Hybrid Projection (NHP), Fast Euclidean Direction Search (FEDS), Fast Hybrid Euclidean Direction Search Algorithm (FHEDS), Signal to Noise Ratio (SNR).
Speech analysis is the modelling and estimating of the different speech characteristics that would provide the importance on each set of criteria established on the real time applications. One such analytic section in enhancement process on speeches would improve the need of speech enhancement. This paper compares the performance analysis of our proposed Fast Hybrid Euclidean Direction Search (FHEDS) algorithm with other adaptive algorithms such as NHP and FEDS algorithm. These algorithms have been tested for their adaptive noise cancellation of speech signal corrupted by different noises such as Babble, Factory, Destroy Engine, Car, Fire Engine and Train Noises. Ensuring the design criteria with current design limits of the database and its analysis have been encapsulated with each phase of design with Noise model, improving the better performance aspects. The relative factors for comparisons have been tabulated with each set of the noise and clear speech data with proposed filter operation. The proposed model effectively reduces the noise for achieving better speech enhancement. The proposed model achieves high Signal-to-Noise Ratio (SNR) when compared to traditional models.
Ch.D.Umasankar, M. Satya Sai Ram, "Speech Enhancement through Implementation of Adaptive Noise Canceller Using FHEDS Adaptive Algorithm", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.3, pp. 11-22, 2022. DOI: 10.5815/ijigsp.2022.03.02
[1]Lukas Pfeifenberger, Matthias Zöhrer, Franz Pernkopf, “Eigenvector-Based Speech Mask Estimation for Multi-Channel Speech Enhancement” IEEE/ACM Transactions on Audio, Speech, and Language Processing ( Volume: 27 , Issue: 12 , Dec. 2019 )Electronic ISSN: 2329-9304
[2]Femke B. Gelderblom, Tron V. Tronstad, Erlend Magnus Viggen, “Subjective Evaluation of a Noise-Reduced Training Target for Deep Neural Network-Based Speech Enhancement”, IEEE/ACM Transactions on Audio, Speech, and Language Processing ( Volume: 27 , Issue: 3 , March 2019 )Electronic ISSN: 2329-9304.
[3]Y. Laufer and S. Gannot, "A Bayesian hierarchical model for speech enhancement", Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 46-50, 2018.
[4]N. Dionelis and M. Brookes, "Phase-aware single-channel speech enhancement with modulation-domain Kalman filtering", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 26, no. 5, pp. 937-950, May 2018.
[5]S. Braun and E. A. P. Habets, "Linear prediction based online dereverberation and noise reduction using alternating Kalman filters", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 26, no. 6, pp. 1119-1129, Jun. 2018.
[6]S. Braun and E. A. P. Habets, "Linear prediction based online dereverberation and noise reduction using alternating Kalman filters", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 26, no. 6, pp. 1119-1129, Jun. 2018.
[7]Q. Wang, J. Du, L. Dai and C. Lee, "A multiobjective learning and ensembling approach to high-performance speech enhancement with compact neural network architectures", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 26, no. 7, pp. 1185-1197, Jul. 2018.
[8]Li Chai, Jun Du, Qing-Feng Liu, Chin-Hui Lee, “Using Generalized Gaussian Distributions to Improve Regression Error Modeling for Deep Learning-Based Speech Enhancement”,IEEE/ACM Transactions on Audio, Speech, and Language Processing (Volume: 27, Issue: 12, Dec. 2019), Electronic ISSN: 2329-9304.
[9]Sean U. N. Wood, Johannes K. W. Stahl, PejmanMowlaee, “Binaural Codebook-Based Speech Enhancement With Atomic Speech Presence Probability”,IEEE/ACM Transactions on Audio, Speech, and Language Processing ( Volume: 27 , Issue: 12 , Dec. 2019 ),Electronic ISSN: 2329-9304
[10]Y. Zhao, Z.-Q. Wang and D. Wang, "Two-stage deep learning for noisy-reverberant speech enhancement", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 27, no. 1, pp. 53-62, Jan. 2019.
[11]N. Dionelis and M. Brookes, "Phase-aware single-channel speech enhancement with modulation-domain Kalman filtering", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 26, no. 5, pp. 937-950, May 2018.
[12]Seyed Abolfazl Hosseini, Sayed Aref Hadei. “Fast Euclidean Direction Search Algorithm in adaptive noise cancellation and system identification.” International Journal of Innovative Computing, Information and Control Volume 9, Number 1, January 2013:191-206. ISSN 1349-4198
[13]C. S. J. Doire et al., "Single-channel online enhancement of speech corrupted by reverberation and noise", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 25, no. 3, pp. 572-587, Mar. 2017.
[14]B.M. Mahmmod, A.R. Ramli, S.H. Abdulhussian et al., "Low-distortion MMSE speech enhancement estimator based on laplacian prior", IEEE Access, vol. 5, pp. 9866-9881, 2017.
[15]Q. Zhang, M. Wang and L. Zhang, "A robust speech enhancement method based on microphone array", IEEE 17th Int. Conf. on Communication Technology (ICCT), pp. 1673-1678, 2017.
[16] Kumar, K. & Venkata Subbaiah, Potluri. (2016). “A Survey on Speech Enhancement Methodologies”, International Journal of Intelligent Systems and Applications, 8.37-45.
[17] Kunche, Prajna & Rao, Gottapu & Reddy, K. & Maheswari, R. (2014). “A New Dual Channel Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO).”, International Journal of Intelligent Systems and Applications, 6. 1-10.
[18] Wu, Chaogang & Li, Bo & Zheng, Jin. (2011). “A Speech Enhancement Method Based on Kalman Filtering”, International Journal of Wireless and Microwave Technologies, 1.55-61.