Hi there, this is a shameless repost of a thread I made over on the DSP subreddit, however, I'm very interested to get some feedback from the folks of PF. Here is my question: -- Hi there, I recently graduated with an undergraduate degree in electrical engineering. During my schooling, I took various courses on linear systems, control theory, and a single course dedicated to digital signal processing. I very much enjoyed the course, however, I took this course my final semester of my undergrad and did not have the capability of take more classes. It had recently dawned on me that after the years of advancing my mathematics skills and learning about linear systems and controls, I've feel like I'm now at a point where I can truly appreciate the material. I'm at a point where I realize how little I know regarding the subject of DSP but have the capability to learn more on my own. As an example, I've recently taken up an interest in wavelet theory to analyze the time-frequency characteristics of signals. However, I'm bummed now that I'm out of school. Besides the obvious answer of grad school, I'm wishing there was a next step in guiding how I learn more about the subject. As of currently, my further pursuit of learning the material is just a mess of jumping from one subject to the next. My question: what's the best method for learning more about the subject of DSP from an intermediate to advanced level? It seems like the majority of the books I come across start at the basics of DSP and conclude at the discrete fourier transform. And when I do find a book on a more advanced topic, the entire book is dedicated to the subject. So what I'm looking for is some tips for further pursing the subject of DSP from a non-beginner perspective. I'm looking for book recommendations, compiled lists of topics that you consider 'advanced fundamentals'. If it's any help, my main area of interest is audio and sensor data (i.e. not image processing). Furthermore, I'm incredibly intrigued with the concept of time-frequency analysis hence my previous mentioning of wavelet transforms. The idea of signal decomposition, statistical analysis, adaptive filters, and signal coding are all very interesting also.