This is an excellent course for those who have knowledge of Digital Signal Processing and have interest in Music.

If you haven't had exposure to Digital Signal Processing, then it would be a very good idea to get introduced to it before taking this course. On the other hand, for Music, you don't need formal education -- This is my case, good knowledge on DSP and interest in Music (but not a musician).

You need to know how to program. I think knowing Python is a plus, but in my case, I had never had exposure to Python and yet I had (almost) no problem, since I had good experience with other languages such as C and Matlab.

Here are some recommendations: [1]: If you have some trouble in the first few programming assignments, go to the forums, they will help you solve some stupid issues (probably strictly related to Python, not DSP) [2]: Take a look at the question quizzes before watching the videos. That will help you know beforehand what you need to look for. This way, you'll likely get most of 100's. [3] Enjoy what you are learning. Try to implement things you want, not only what you are requested to do. This way, you will arrive to the final project with good ideas on what to do. [4]: Although the first 5 or 6 assignments have no due date limit, the last few do have strict due dates. Don't get too confident on this, since you will find yourself having trouble finding reviewers (last few projects are graded by other students). This happened to me...

I learned a lot during this course. It took quite a lot of time and energy to complete it, but I'm glad I did. It is now much easier to follow the text of Richard Lyons' book. Highly recommended.

Great course! As the course page mentions, this is an intermediate level course. You will need some basics in digital signal processing, python programming (or a general familiarity with computer programming), and some basic mathematics. If you aren't familiar with these, you will have a chance to brush up on these concepts through lecture videos and some Googling and reading (be prepared to spend some time on that). I did not have much background in any of these, yet I managed to finish the course with some effort. If you face technical difficulties with Ubuntu, Python, and programming assignments, discussion forums will help you. It is totally worth the effort. Knowledge of music is not required at all, but it helps if you have some interest in (and perhaps some functional understanding of) music, because the applications are all music-based. This course is more about spectral analysis of (musical) sounds, sound transformation, and sound description. It doesn't cover filter design and other signal processing topics in detail. For me, it was a starting point towards computational musicology. The course not only gives you the usual signal processing knowledge, it also introduces you to FreeSound.org, CompMusic, Computer Technology Group, Essentia, Dunya, and AcousticBrainz, and gives you an overview of current research in computational musicology. I completed this course almost two years ago and am revisiting it now for a refresher.

A very challenging course, involving mathematics, scientific python programming and music, focussing on the Discrete Fourier Tansform and Spectral Analysis of Audio Signals. The course material is written by two of the leaders in the world of Audio Signal Processing. Don't underestimate how challenging this course will be, you'll need at least mathematics at university entrance level, and current experience of python (spend some time getting to know the numpy ndarray class and scipy.signal packages before the course starts, if you can). If you really want to get the most out of this course, expect to spend another 8 hours or so every week reading Julius Smith's books (available free online). I'd hoped to do a bit more learning on filter design in this course, and hadn't understood the fact that this course focusses on Spectral Analysis, but it has vastly increased my understanding of signal processing, nevertheless!

An excellent course, particularily when enhanced by reading the four book series by Juli us O. Smith III.

Allowing one to continue into the next semester while keeping credit for work done is a major

plus point for the course. It actually took me about a year to complete and the alloted ten weeks would be an extremely demanding schedule.

For a free course this was amazing. It was technically very challenging in places but was massively informative and completing it has felt like a excellent accomplishment. My thanks fro all of the hard work that clearly went into constructing the lectures and material.

Good lectures with a focus on practical applications. Good introduction to how signal processing can be used for musical analysis, and more specifically how to use the Essentia library

Very well explained and organized course material. The classes are also very detailed and special emphasis is put on illustrating every concept with example plots.

The course is very well explained, the assignment are really interesting and the topic is very interesting as well.

If you never worked with coding, the beginning may be a bit tricky, but everything is well explained. Take your time to view the videos, they always help if you have some problems.

Exceptional course! For anyone looking to dive deeply into audio signal processing, it's essential to thoroughly study the provided material. The only drawback is the lack of a certificate, which is unfortunate given the course's depth and quality.

Very good course. You learn basics of Audio digital Processing while you learn how to use software that is open source. I really enjoy programing and learning Python and more because it was totally applied.

A real gem of a course in Coursera. It covers all the aspects of the topics discussed on Audio Signal Processing ranging from In-depth Theory to excellent examples & demonstration to programming aspects.

Brilliant course! Good balance between theory and implementations with Python. Also, very good additional materials - video lectures from Julius Smith, if you want to go even deeper into math theory.

Top class! Very well explained, good examples, excellent learning material, practical exercises, and lots and lots of room for further personal study! Well done guys, and especially Xavier! Cheers!

An absolutely awesome introduction to Audio Signal Processing. The additive introduction of new concepts is capable of teaching any beginner this topic which ordinarily is difficult to understand.

Lots of learning in 10 weeks. Teacher is good takes time and explain things well. Lectures include theory, demonstration and programming which helped me learn the basics really well. Thank you !

Excellent course, for me this was pitched at just the right level to be interesting and also challenging. Everything was well explained and well structured.

good course . If there will any Speech recognition course it will useful > Hope Coursera will start speech recognition course in future .

Very well presented and thorough investigation into spectrogram, STFT and reconstruction techniques. Enjoying thanks.

Great course ! The topics covered are all very interesting and the theory lectures provide a good understanding of them. It makes Python programming approachable (basic previous knowledge is needed) with hands-on exercises. With the extensive packages provided it is possible to dive into digital signal processing and to understand much of what is applied during the course. I really liked the encouragement for new application and for different possible researches within the subjects covered.