>It's essentially computing a frequency median,
>rather than a frequency mean as is the case
>with the derivative-power technique described
> in my original approach.

So I'm wondering, is there any consensus on what is the best measure of
central tendency for a music signal spectrum? There's the median vs the
mean (vs trimmed means, mode, etc). But what is the right domain in the
first place: magnitude spectrum, power spectrum, log power spectrum or ???

E

On Wed, Feb 17, 2016 at 2:40 PM, Evan Balster <e...@imitone.com> wrote:

> Dario's adaptive approach is interesting.  It's essentially computing a
> frequency median, rather than a frequency mean as is the case with the
> derivative-power technique described in my original approach.
>
> Dario, I would suggest experimenting with zero-phase FIR filters if you're
> doing offline music analysis.  This would allow you to iteratively refine
> your median "in-place" for different points in time.
>
> – Evan Balster
> creator of imitone <http://imitone.com>
>
> On Wed, Feb 17, 2016 at 7:52 AM, STEFFAN DIEDRICHSEN <sdiedrich...@me.com>
> wrote:
>
>> This reminds me a bit of the voiced / unvoiced detection for vocoders or
>> level independent de-essers. It works quite well.
>>
>>
>> Steffan
>>
>>
>>
>> On 17.02.2016|KW7, at 13:08, Diemo Schwarz <diemo.schw...@ircam.fr>
>> wrote:
>>
>>    1. Apply a first-difference filter to input signal A, yielding signal
>> B.
>>     2. Square signal A, yielding signal AA; square signal B, yielding
>> signal BB.
>>     3. Apply a low-pass filter of your choice to AA, yielding PA, and BB,
>>        yielding PB.
>>     4. Divide PB by PA, then multiply the result by the input signal's
>> sampling
>>        rate divided by pi.
>>
>>
>>
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>
>
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