linuxaudio.org

Hi Fons
On Tue, May 11, 2010 at 4:19 PM, Fons Adriaensen  wrote:

quoted text
> On Tue, May 11, 2010 at 03:36:08PM -0700, Mark Knecht wrote:

>

>> Actually though, as a test of an idea I had and just because I've got

>> the data here, I'm using stock market data, decomposing the data into

>> bands of energy if you will, and then summing the bands back together

>> to see how close I come to the original data. It works fairly well

>> most of the time, but stock data can have big directional moves at

>> times which appears as a big transient event. When those events

>> complete then the slower filter bands ring and cause large

>> displacements in the output.

>

> So you want a set of filters with the following properties:

>

> 1. each filter has a frequency response with a shape that allows

> an intuitive interpretation as a 'bandpass', i.e. a range of

> frequencies, and

>

Yes, a specific (small) range of frequencies and some ability to see

if the energy meets some minimum level of interest.
> 2. the sum of the filter outputs equals the original input.

quoted text
>

This is not really a requirement, but if they did then I'd have a more

visual demonstration that the process was working.
> This is a *hard* problem. Basically none of the classical filter

quoted text
> types have these properties. There are filter sets that can do

> this but they are quite esoteric.

>

> 'Ringing' is not really a problem here, each of the individual

> filters may ring, as long as this is cancelled by the others

> when you add the outputs.

Not sure I totally agree with this as it assumes that in the end I

listen to the reconstituted sum of all the filters. On the other hand,

if I listen to only a few then without the other 'negative'

contributions I end up hearing the ringing in the few I'm listening

to.
>

quoted text
> An FFT will provide a set of filters having property (2), but

> each filter has a sin(x)/x shape, which is not really a bandpass.

> You can 'improve' the shape by windowing, but that destroys (2).

> And anyway using an FFT like this is a 'block' operation - how

> a sample is treated depends on its position within the block.

> Windowing requires overlapping blocks, and they won't add up

> to the original input.

>

I think also that an FFT assumes some sort of longer-term steady state

repetitive nature which isn't in the stock data. I could take a

representative sample - the last 1000 bars, and then treat that as if

it was a repeating cycle and extract frequencies. I'm not sure what

that will do for me and the sample points are likely to introduce lots

of new artifacts. Still, worth thinking about.
>

quoted text
> Now if the purpose of the filtering is to extract 'features' of

> the data, you really don't need property (2). And given the

> nature of stock market data, I suspect that classic audio filters

> are not the right way to extract such features. I'd have a look

> at wavelet filters for example.

>

> Ciao,

>

Will do. Thanks.
Cheers,

Mark

_______________________________________________

Linux-audio-user mailing list

Linux-audio-user@lists.linuxaudio.org

http://lists.linuxaudio.org/listinfo/linux-audio-user