A Fast Adaptive Speech Extraction Method using Blind Source Separation for Audio Signal Processing
Mandli Rami Reddy1, M L Ravi Chandra2, Alam Siva sankar3
1Mandli Rami Reddy*, ECE Department, Srinivasa Ramanujan Institute of Technology, Ananthapuramu, Andhra Pradesh, India.
2Dr M L Ravi Chandra, ECE Department, Srinivasa Ramanujan Institute of Technology, Ananthapuramu, Andhra Pradesh, India.
3Dr Alam Siva sankar, ECE Department, Srinivasa Ramanujan Institute of Technology, Ananthapuramu, Andhra Pradesh, India.
Manuscript received on January 18, 2020. | Revised Manuscript received on January 24, 2020. | Manuscript published on February 10, 2020. | PP: 727-733 | Volume-9 Issue-4, February 2020. | Retrieval Number: B7253129219/2020©BEIESP | DOI: 10.35940/ijitee.D1318.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The adaptive signal processing methods are used in several applications like channel estimation, Noise removal and extraction of signals also. The methods vary on time, frequency and statistical approach. In this paper, the source speech signals are separated using different methods like Fast ICA,PCA and kICA. Comparison of original signal and estimated signals are evaluated for different methods. The implementation was done in MATLAB. The spectrogram, Negentropy and Kurtosis waveforms are plotted for different methods.
Keywords: BSS, ICA, Noise, Speech, Spectrogram, Negentropy, Kurtosis, Statistical.
Scope of the Article: Digital signal processing theory