How to do research and enjoy the same as a PhD student

From my experience in the past 5.5 years and thinking deeply where I was most productive and enjoyed my research, I am now confident enough on how to do research. Being mostly independent as a research student, we tend to procrastinate a lot without any direction. We do get lot of ideas, which gets lost or diluted without any direction and motivation to work.

I have put the following guidelines which will help us get novel ideas and enjoy the process of research along with the practicality of publishing papers. Please see https://www.youtube.com/watch?v=g3dkRsTqdDA on how to write a great research paper.

1. We get many ideas for a problem. So, initially document the same in the form of a research paper.
2.  Put down the motivation, assumptions and the expected outcome roughly.
3.  Don't worry if the idea is small or we are not sure it will work as simple ideas have outperformed complicated approaches in many fields
4. There are many methods and approaches to solve a problem which many researchers around the world used and applied
5. We should survey and learn the various methods and approaches others have used, and try to see a connection and get ideas for our own research
6. We should think out of the box and devise our own approach instead of trying to improve already existing methods 
7. We should master and learn as many topics/ subjects as possible and are comfortable with and have abroad overview of other topics
8. We should use methods, concepts in which we are strong and have a good understanding, as we will get great ideas if we have a good understanding and think openly.
9. We must be aware and accept our weakness and shortcomings in some topics and have a broad overview of those topics instead of getting demotivated 
10. Attend talks out of our field of research as we get hints and ideas in our work from other areas/ topics (I get a small idea/ hint in a talk and then I try to link/ fit it to my research problem there itself)
11. Be focused in what approach you want to use for your research problem, and don't deviate just because someone forces or some other approach/ topic is hot. 
12. Only if the upcoming areas and topics really interests you, we can see the feasibility and applicability to our problem
13. Maintain a research diary where we put down all the ideas, approaches, positive/ negative results we have got along with the date/ time, so as to see the timely flow of our thoughts/ observations
14. Work consistently and vary the schedule/ place of work to avoid monotonicity, and do take a break if you want to
15. Credit courses which are directly related to our research, and audit or be TA for at least one course every semester
16. Maintain a rough schedule and do take internship opportunities if feasible
17. Maintain a research website/ blog to convey our thoughts and ideas/ observations
18. Getting a new idea and even small results are the moments of joy during research
19. We can enjoy the research life by following above steps
20. Have a nice and healthy research life !

Random thoughts towards completion of my PhD thesis

So, now having finished 5.5 years of my PhD studentship, I am trying to consolidate all the ideas and work into a logical and presentable form for journal papers and thesis. I think any PhD thesis should tend to be a like a subspace, which itself is a vector space (closed under addition and scalar multiplication). It means that every element of the thesis and its combinations should be a part of the thesis.

Updated<08-01-16> So, the course registration for this semester is over and I had a sudden thought whether I should credit any new courses, initiated by the by a new upcoming rule in IISc, that a direct PhD student can get a M.Tech (Research) + PhD degree with a requirement of 21 credits as coursework. Even though I finished the credit requirement with 26 credits, it has been a long duration of 4.5 years since I credited a course. I thought it will be very good and exciting to credit courses. We get the feel of giving exams and assignments/ projects without the pressure of CGPA/ RTP as the  courses credited now will be counted as non-RTP additional courses. I think a research student should credit or be a TA for at least one course every semester so as to keep in touch with basics and learn new subjects helpful for our research. We may think that we can learn on our own through books/ video lectures but crediting and doing the course has a different higher level of commitment and understanding. So, even though I am in the sixth year, I am crediting two courses now.

Quality time for Research ?...?...

Its not a course work, an exam, a project or a contest. Its very different, research is like none of these activities. Research requires a self time, which I am now struggling to find. Distractions, mails, internet, talks and noises during the normal hours 10-5 p.m. In the 5th year of PhD, and need to schedule a quality time. Mornings and nights are only option. Nights are very lethargic and tiring mind for research, needs a fresh mind for research, mornings seem to be the only time. But, early morning is the only possible time. So, around 4 hours of quality time in the mornings is the best option, no disturbance in the dept or room. Maybe 6:30 am -7: 30 am, and then 8:30 am-11:30 a.m after breakfast, or 8-12 am . This time should be devoid of checking even dept. e-mail. The best part is the quality research for the PhD thesis is over early morning and have the rest of the day for other lab, dept. meetings talks, and extra-curricular activities. And night 8:30- 10:30 p.m. is also a optimum time after early dinner. 

Dull and boring part of Research..

As everyone thinks research is very interesting, innovative and tough, it is sad that sometimes we tend to do something which is already done with slight changes, complexify it and publish and get publications. And as we say it, we tend to do it ourselves. Like taking an already attacked application, already used methods do some permutation of different methods and think we are doing great research. May it will be much better if we try to think afresh about a new, unattacked  application and devise a simple method to solve it, even if it is very simple. Given that we do lot of literature survey, we get many new ideas to use them, but we should not get carried away by what others have already done. We may get inspired from others work, but for our own research we must have our own different, novel ideas. As it is always not possible that our new ideas work, we tend to do this dull and boring part of research. Hope we will get over it and make it more interesting.

Effect of high frequencies in audio signals and intricacies in audio research

A nice article describes why high sampling rates are not required for audio. The audio signal is the most intriguing of all signals. It is quasi-periodic but non-stationary over long  periods above 20 ms.

I have been evaluating instants of maximum excitations or epochs and I was using 3 Core i5 systems simultaneously as I had to the run the same code using different parameters to get the best performance and accuracy. I have re-refined my output recursively so as to get the best accuracy and identification rates.

On the random way to research

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: