Stanford has a well-regarded summer course at CCRMA that covers the
basics rigorously.
--rbt
On 07/20/2016 03:11 PM, Liam Sargent wrote:
Hello all,
Been subscribed to this list for a while and have found the
conversation fascinating. I recently graduated with a B.S. in Computer
Science
Ethan ---
If we use only a binary search in the discard step, isn't the amortized
complexity still O(N)? I suppose not... We'd be doing a log2(w) search
every sample in the worst case where a monotonic decrease occurs.
I'll have to look over the paper to get a better understanding, but would
Hi Liam,
I believe
https://ccrma.stanford.edu/academics/masters
is an excellent program. It's not an online program, but at least it
happens close to where you live.
At Kadenze.com you'll find online (related) courses.
Esteban
On 7/20/2016 3:11 PM, Liam Sargent wrote:
Hello all,
Been
You might take a look at www.ambiophonics.org which discusses a whole bunch of
DSP applications for both recording and reproduction. If you look at the NYU
thesis and the related AES paper you will see ideas for more MS thesis ideas.
One possibility is to codify Envelophonics and prove why it
Good idea and very clear summary.
One correction: in the discard step the paper prescribes a linear search
through the queue starting from the end, not a binary search. And the
modification I propose is to stick with this linear search for the first
log2(w) samples, then switch to a binary
Original Message
Subject: Re: [music-dsp] efficient running max algorithm
From: "Ethan Fenn"
Date: Wed, July 20, 2016 10:27 am
To: music-dsp@music.columbia.edu
Of course, for processing n samples, something that is O(n) is going to
eventually beat something that's O(n*log(w)), for big enough w.
FWIW if it's important to have O(log(w)) worst case per sample, I think you
can adapt the method of the paper to achieve this while keeping the O(1)
average.