I am working on a problem regarding noise removal from recorded signals using some Adaptive Filtering ( e.g Wiener Filter ).
It's a typical DSP problem in a following way.
Recording Time Signals
Windowing into Blocks
FFT Performing
Noise Removal Technique (Wiener )
IFFT
Results On WaterfallSpectrum
My Problem
Somehow, I need to make it Client/Server based application, or i want it to be developed as SOA based app. I researched over it , but I don't find any possible way or similar applications on audio processing or sound processing that based on SOA.
Can any body suggest the idea, how can I implement it? and to me it would be advatageous.
What modules do I need to put as client side and others on server side? ( e.g FFT and Noise Removal Techniques )
Edit:
Actually, I am having research to perform noise removal from audio or recorded signals.
For that, I have to develop not only straight forward solution, but to make the software component so much flexible and network oriented ( some how client/server oriented ) in a way some operations must be performed at one side and some on other side depending on it's nature ( e.g a lot of amount of data processing on server side ).
All I know How can I record signals from Microphone , and apply FFT on those signals and then finally to filter out noise from those frequencies. I have FFT based library ( KissFFT or AForge.NET ). So , I know what I am doing and what I have to do.
But I don't know how it can be done what I stated above? ( How can I manipulate client/server architecture based implementation of this problem which part on client side and which parts on server side)?
Regards
Usman
Related
I have built a loopstation in JSyn. It allows you to record and play back samples. By playing multiple samples you can layer up sounds (e.g. one percussion sample, one melody, etc)
JSyn allows me to connect each of the sample players directly to my lineout where it is mixed automatically. But now I would like to record the sound just as the user hears it to a .wav-file. But I am not sure what I should connect the input port of the recorder to.
What is the smartest way to connect the audio output of all samples to the WaveRecorder?
In other words: In the Programmer's Guide there is an example for this but I am not sure how I create the "finalMix" used there.
Rather than using multiple LineOuts, just use one LineOut.
You can mix all of your signals together using a chain of MultiplyAdd units.
http://www.softsynth.com/jsyn/docs/javadocs/com/jsyn/unitgen/MultiplyAdd.html
Or you can use a Mixer unit.
http://www.softsynth.com/jsyn/docs/javadocs/com/jsyn/unitgen/MixerStereoRamped.html
Then connect the mix to your WaveRecorder and to your single LineOut.
I am trying to get multiple time-frame data of different trading instrument ( _Symbol ) from MetaTrader4 Terminal to a node.
How can I do it?
Can we do it from the same EA inside a MetaTrader4 Terminal?
A.1: Yes, we can.
A.2: No, that initial idea is not a good one.
While the intention is clear, the idea to use a single EA to send live-data for multiple trading instruments is not working for the said interest well.
MQL4 code-execution environment has some fixed, hard-wired internal logic and due to these + plus due to the reality, how Capital Markets and Broker-type Market access mediators work, the solo-EA will never fit these requirements.
A simple call to
iOpen( aTradingInstrumentSymbolNAME, // iHigh, iLow, iClose, iVolume, iTime
aSelectedTimeFrameDefinedCODE,
aRelativeBarPTR
)
is by far not enough.
Professional solution will require a lot of care for a real-time handling capabilities, for unmasking the actual flow of mutually hiding events, for achieving minimalistic processing latencies, so a quite high engineering expertise will be needed.
Start with learning the basics about Scripts, benchmark all your critical code-sections with recording their actual durations in [us] and assure, your code will remain non-blocking under all circumstances. This will decide, whether more than one code-execution thread(s) will be necessary in prime-time / for peak-hour.
Having managed that, your way just started to lead in a direction towards your expected result.
Next one has to decide about a feasible inter-process / distributed-computing data-flow and signalling, needed for inter-platform integration.
Last, but not least, important point is the legal-side of such undertaking. It depends both on your local juri§$§$§diction and Broker's Terms & Conditions as no one would enjoy to celebrate a technically well mastered Project from inside of jail.
All that, quite an interesting Project.
iOpen(Symbol(),PERIOD_M1,1) - is the way to get data from M1 ( last bar ), if you need another timeframe - replace PERIOD_M1 with another ENUM_TIMEFRAMES. So what is the problem? Usually StackOverflow requires to see your MCVE-based example to help you.
I'm currently working with a system that makes scheduling decisions based on a series of requests and the state of the system.
I would like to take the stream of real inputs, mock out some of the components, and run simulations against the rest. The idea is to use it for planning with respect to system capacity (i.e. when to scale certain components), tracking down certain failure modes, and analyzing the effects of changes to the codebase (i.e. simulations with version A compared to simulations with version B).
I can do everything related to this, except generate a suitable input stream. Replaying the exact input from production hasn't been very helpful because it's hard to get a long enough data stream to tease out some of the behavior that I'm trying to find. In other words, if production falls over at 300 days of input, I don't have enough data to find out until after it fell over. Repeating the same input set has been considered; but after a few initial tries, the developers all agree that the simulation seems to "need more random".
About this particular system:
The input is a series of irregularly spaced events (i.e. a stochastic process with discrete time and continuous state space).
Properties are not independent of each other.
Even the more independent of the properties are composites of other properties that will always be, by nature, invisible to me (leading to a multi-modal distribution).
Request interval is not independent of other properties (i.e. lots of requests for small amounts of resources come through in a batch, large requests don't).
There are feedback loops in it.
It's provably chaotic.
So:
Given a stream of input events with a certain distribution of various properties (including interval), how do I generate an infinite stream of events with the same distribution across a number of non-independent properties?
Having looked around, I think I need to do a Markov-Chain Monte-Carlo Simulation. My problem is figuring out how to build the Markov-Chain from the existing input data.
Maybe it is possible to model the input with a Copula. There are tools that help you doing so, e.g. see this paper. Apart from this, I would suggest to move the question to http://stats.stackexchange.com, as this is a statistical problem and will likely draw more attention over there.
What are the main differencees between a Complex Event Processing engine and an Expert System engine?
When should I use one or another?
A CEP engine will help if time has a strong influence in your decision model. For example : deciding to fire event X, if event Y and Z happen in the same given time window. Another example is the capability to aggregate events over time windows.
Another influence of time is latency constraint : ie. you want to process event in a short timescale (eg. 100ms or much less). CEP fits very well in that situation.
But both technologies are more or less complementary. In particular, some CEP tools have inference rules. Or you can have a first processing stage with a CEP engine (filtering, aggregation, composition of events), and then you query an expert system to decide what to do with your aggregated, complex event.
Here is an article about some use-cases of CEP that may help : Complex Event Processing
They are different.
#MDE wrote about SEP, but expert system gives decisions. It do not process events, its task to give answer based on (X, Y, Z, ....) parameters. If you want to trigger events then it is CEP, but if you need analyzes of them, so it's expert system. As #Bob wrote, thay can be used together:
CEP to capture event
Expert System to "think of it"
And then, SEP to fire it back, for example.
I'm new to audio filters so please excuse me if i'm saying something wrong.
I like to write a code which can split up audio stored in PCM samples into two or three frequency bands and do some manipulation (like modifying their audio levels) or analysis on them then reconstruct audio samples from the output.
As far as i read on the internet for this task i could use FFT-IFFT and do manipulation on the complex form or use a time domain based filterbank which for example is used by the MP2 audio encoding format. Maybe a filter-bank is a better choice, at least i read somewhere it can be more CPU usage friendly in real time streaming environments. However i'm having hard times understanding the mathematical stuff behind a filterbank. I'm trying to find some source code (preferably in Java or C/C++) about this topic, so far with no success.
Can somebody provide me tips or links which can get me closer to an example filter bank?
Using FFT to split an Audio signal into few bands is overkill.
What you need is one or two Linkwitz-Riley filters. These filters split a signal into a high and low frequency part.
A nice property of this filter is, that if you add the low and high frequency parts you get almost the original signal back. There will be a little bit of phase-shift but the ear will not be able to hear this.
If you need more than two bands you can chain the filters. For example if you want to separate the signal at 100 and 2000Hz it would in pseudo-code somewhat like this:
low = linkwitz-riley-low (100, input-samples)
temp = linkwitz-riley-high (100, input-samples)
mids = linkwitz-riley-low (2000, temp)
highs = linkwitz-riley-high (2000, temp);
and so on..
After splitting the signal you can for example amplifiy the three output bands: low, mids and highs and later add them together to get your processed signal.
The filter sections itself can be implemented using IIR filters. A google search for "Linkwitz-Riley digital IIR" should give lots of good hits.
http://en.wikipedia.org/wiki/Linkwitz-Riley_filter
You should look up wavelets, especially Daubechies wavelets. They will let you do the trick, they're FIR filters and they're really short.
Update
Downvoting with no explanation isn't cool. Additionally, I'm right. Wavelets are filter banks and their job is to do precisely what is described in the question. IMHO, that is. I've done it many times myself.
There's a lot of filter source code to be found here