I'm starting a new series today on Acoustics to Deep Learning. Rather than simply discuss tools (Deep Learning, SVM...), I've decided to present a bunch of interesting techniques in the context of audio and sound.
While we’re covering a lot of ground here, in some cases in substantial depth, this series is not meant to be exhaustive or even thorough. I’m going to talk about what I think is interesting and compelling with as much depth and clarity I can squeeze into a YouTube video. This is mostly because I believe that with the number of resources available, being thorough is a waste of our time. Finally, we will cover some serious computational techniques here, but only in the context of examples. I think it’s important to remember that as cool as tools and techniques are, they are only, at most, a means of accomplishing something. The techniques shown here were not developed in a vacuum, but in the rich and complex world of application, and I believe that presenting tools and techniques in the absence of the appropriate context does you a disservice, and can even hinder learning – the why is just as important as the how.