The science and engineers guide to digital signal processing

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The scientist and engineers guide to digital signal processing

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Limited time offer. Just the first chapter on sampling made so many things click for me I already had a background in sound synthesis with synthesizers, bpt no theory , and it made it all seem so beautiful I still think signal processing is one of the most beautiful fields of math, probably because of how the Fourier Transform "happens" to be almost identical to its inverse and this lets you do so much with such a small and simple set of tools.

The book was clearly written by a good engineer, as it is full of wisdom, tricks and intuition of the kind you only learn on the field. It has good exercises and very simple code for everything. The book takes the discrete-only approach - it doesn't even once show an integral, only sums on arrays of floating point numbers. This is a very good approach that I'm not aware of other texts taking.

I studied most of it while living with a friend who applied and taught DSP on his day job. Every evening we would sit in front of a whiteboard and compare my newly earned knowledge on a tool of discrete signals processing with the continuous version that he used and taught. Invariably, we ended with the conclusion that "my" version was much simpler and more intuitive while preserving all the needed power in practice.

So I never even tried to grok the continuous Fourier Transform, and yet my intuition of linear systems has served me very well ever since.

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Same here, and I did a lot of work in signal processing from designing digital synthesizers to field and post sound engineering. Probably my single favorite textbook, unusually well written. ChuckMcM on Dec 24, Lyons' book is more thorough and complete, each chapter builds on the foundation of the prior ones. He aimed for a hands-on approach, not theoretical. It is very suitable for someone who isn't intimidated by math of which there is ample , but would prefer to see examples of it worked out especially visually.

His list of tips and tricks at the end come in handy for specific applications some of which I didn't know I need until I later came across them. Smith's book, on the other hand, is cursory and a bit more brief. It might leave you feeling that there is more depth there which you do not understand. Perhaps I feel this way because I read it online whereas I read Lyons' as a physical copy. Smith treats similar subjects but with less detail and fewer of the expert topics. On the other hand, it is a faster read and free. Both are good for beginners who want to understand DSP better! Lyons' book is liked by many people I work with who are learning DSP on the job.

The current version has pages of tricks, 51 sections; additionally it covers quadrature signals and Hilbert transforms. But, it is not free. AndrewUnmuted on Dec 24, AudioKit is too limiting for those with actual DSP knowledge. I've seen JUCE being recommended for developing cross-platform audio applications.

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ZenoArrow on Dec 24, Looks promising, shame it's only available for Apple devices. Not at all; it only covers a subset of AudioKit's features, such as low-latency access to audio and MIDI, global timekeeping and routing between clients. But its API is sufficiently terse to get started with smaller clients, and you can chain these pieces together with existing applications.

This is a fantastic book. As a Software Engineer with little background in DSP, this book has been invaluable in tackling DSP-related projects that have come up at my current job. Really pragmatic approach to the subject matter, which I've found can easily be lost in other texts. What projects? Primarily those involving frequency analysis and smoothing via bandpass filters of time domain audio signals. Is this even still relevant though?

I had a DSP textbook in college back in the s. Digital Signal Processing is a fundamental field in electronics and always will be as long as we have any form of electronic communication or signals. Most modern active electronics parts even many power converters! Technically, your CPU is just a power hungy multipurpose DSP but we don't refer to them as such to maintain precision in the jargon. Proper DSP chips are highly specialized processors just like GPUs except they contain lots of silicon for low latency and low power math. Your wifi or cellular radios, for example, are highly specialized DSP chips that only deal with a few protocols.

Hmm not sure what you mean by relevant here. I'll take a crack at my interpretation: Is DSP still relevant - undoubtedly yes. Is the book still relevant? Because it focuses on the underlying theory and concepts and not so much on language specific implementations, I'd say yes to that as well. Not sure whether you're asking whether DSP is still relevant I think it is or a textbook published in the 80s would be relevant. I imagine such a book might talk a lot about the hardware and software available at the time.

Hell machine learning is largely a signals problem.

The scientist and engineers guide to digital signal processing pdf download

This is one of the best technical books I've ever read. It is extremely to read. It focus on building intuition, and then introduces the mathematics that formally describes what you've just learned. Maybe it's just my learning style, but I love this book. I first read this book online over 10 years ago and it has helped me tremendously in understanding the time domain, frequency domain, and Fourier transforms.

I can't recommend this free book enough!

I do as well. Particularly that it approaches the topic from an applications standpoint, rather than starting by deriving the mathematics and treating applications as an afterthought. Wavelet theory was an active area of research in mathematics when this book was first published, but I'm unable to find any mention of the use of wavelets in the book's table of contents. Aren't wavelets analogous to the fourier transform.

Wouldn't that mean it would be a fundamental aspect of DSP these days? True, the Fourier transform can be viewed as a specialization of wavelet transforms, so in that sense wavelets are fundamental. Like many mathematical subjects the specialization is treated before some historical generalization in educational contexts if the generalization is ever discussed, as it doesn't seem to be here.