My another blog describes my early stages of my research. I am well into my 3rd year of my PhD, having redefined, refined, re-refined my problem statement. Having gone through a lot of research papers, journals related to my problem, some very much theoretical, application oriented and some seemly useless papers.
The problem regarding my problem statement is the solution is a random entity. The problem is seemingly NP-hard. The real problem is it is difficult and challenging. The problem statement is: Given a single channel audio signal containing mixture of only two sources: 1) speech, 2) non-speech, the output should be two separated channels one containing only speech and other non-speech signal. Now, its a supervised learning where dictionaries for both speech and non-speech need to be learnt before testing on a mixed audio.
Now, I have seen lot of good papers on sparse dictionary learning, having finally formulated the problem which is quite difficult and has lot of applications if I solve or solve to some extent. Most of the past dictionary learning has been used for object tracking in videos, image classification, few for speech recognition and denoising but very few for source separation.
Having done the literature survey, where most of the good papers are published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Transactions on Signal Processing, International Conference on Machine Learning, IEEE Workshop on Machine Learning for Signal Processing and Journal of Machine Learning Research.
I see source separation of guitar music and other sources as one aspect of my problem. A recording of a guitar tune played by me:
The problem regarding my problem statement is the solution is a random entity. The problem is seemingly NP-hard. The real problem is it is difficult and challenging. The problem statement is: Given a single channel audio signal containing mixture of only two sources: 1) speech, 2) non-speech, the output should be two separated channels one containing only speech and other non-speech signal. Now, its a supervised learning where dictionaries for both speech and non-speech need to be learnt before testing on a mixed audio.
Now, I have seen lot of good papers on sparse dictionary learning, having finally formulated the problem which is quite difficult and has lot of applications if I solve or solve to some extent. Most of the past dictionary learning has been used for object tracking in videos, image classification, few for speech recognition and denoising but very few for source separation.
Having done the literature survey, where most of the good papers are published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Transactions on Signal Processing, International Conference on Machine Learning, IEEE Workshop on Machine Learning for Signal Processing and Journal of Machine Learning Research.
