This IEEE paper discusses an interesting application called Blind Audio Source Separation. This is used to recover the source signals from the mixture of signals. In case of Blind audio separation,the characteristics of the filter are not known. The source signals can be considered as a mixture of information and interference. These signals may be coming from different sources. Considering this mixture as a linear combination of these signals, the authors have developed a method to isolate 2-3 signals using the Weiner filtering approach and Short-time Fourier Transform(STFT). The output is a complex signal,this approach removes the complexity of the signal. The areas of application are Speech Processing and Telecommunication.
IEEE Paper Link: http://ieeexplore.ieee.org/document/7359371/
IEEE Paper Link: http://ieeexplore.ieee.org/document/7359371/
It would be interesting to implement.
ReplyDeleteIt can be implemented on MATLAB
Deletenice concept
ReplyDeleteIt uses concepts of neural networks.
DeleteGood application of Fourier Transform
ReplyDeleteShort time Fourier Transform is used.
DeleteNice application
ReplyDeleteCheck more IEEE papers for a better understanding.
Deletegood content
ReplyDeleteRefer to IEEE explore for a better review.
DeleteCan be used for decryption
ReplyDeleteSimilar to frequency hopping and pseudo random noise generators.
DeleteWiener is considered to be the most effective method for filtering till date
ReplyDeleteWeiner filtering is fast. Other algorithms can also be be used.
DeleteCheck more IEEE papers.
ReplyDeleteNice application.
ReplyDeleteYou can refer to IEEE Xplore for more.
DeleteWiener filtering is one of the most advanced Techniques of filtering
ReplyDeleteIt is faster compared to other conventional methods.
Delete