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I am sharing my assumption  .Please Please correct and guide me If I am wrong.I cant I can't understand or thinking my assumption is wrong somewhere.

We can decompose voice signal or noise signal into noa number of sinusoidal signals of different frequencies according to fourierFourier transform from which we can extract our desired frequencies.

Lets say for one particular signal we required has following frequencies:

say letter AA of man xx has following frequencies when decomposed.  (Is it right I mean each alphabet or word we speak based on who speak etc has different frequencies mixed even though single letter right)

a=200+900+1200

noise=300+900+1200$$F_\mathbf{A,x}= \{200,900,1200\}\\ f_\text{noise}=\{300,900,1200\}$$

So to reduce noise though we cantcan't eliminate frequencies  (same frequencies almost so no bandpass etcc) we can reduce amplitude of noise compared to amplitude of desired signal such that it looks like noise is eliminated.

In above example a(desired signal) of 900hz900 Hz lets say has an amplitude of 2.5 amplitude and noise of 6 amplitude.So So how do we reduce this noise amplitude to eliminate noise from signal.?

I mean howHow does ita filter even distinguish this is noise amplitude and this is signal amplitude. and? And how does it decrease noise amplitude only  ? (I mean they are of same frequency so thinking though they are many algorithms like lms,weiner etccc how do they distinguish noise amplitude and reduce from signal amplitude of same frequency what other paramenter is is using to distinguish)

error(gives voice) == mixedsignal(voice+noise) - w(filter coefficent) * noisereference(close to noise lets say in this case noise reference is exactly equal to noise so as to completely eliminate noise ) mu=convergence factor w=w+2*muerrornoisereference


error(gives voice) ==  mixedsignal(voice+noise) - w(filter coefficent)   *     noisereference(close to noise lets say in this case noise reference is exactly equal to noise so as to completely eliminate noise )
mu=convergence factor
w=w+2*mu*error*noisereference

filter coefficent updates from feedback so as to make noiserefnoisereference exactly equal to noise in mixed signal.

From formula taking ww as 00 starting

If voice is more than noise we get high error = voice(high)+noise(low) --- w*refnoise(low for that partiucalr time instant)


error = voice(high)+noise(low) --- w*refnoise
(low for that partiucalr time instant)

so next instant(sample ) we get even more high error since w (filter coefficent increases) w=w+2*muerrornoise 


w=w+2*mu*error*noise

ww gets more high and more is subtracted from error to get desired signal.

I mean what is it doing really if voice is more it is subtracting high to remove noise.And And if voice is less we get less error so less filter coefficents then it is subtracting less next time instant to remove noise from voice.

I am sharing my assumption  .Please correct and guide me If I am wrong.I cant understand or thinking my assumption is wrong somewhere.

We can decompose voice signal or noise signal into no of sinusoidal signals of different frequencies according to fourier transform from which we can extract our desired frequencies.

Lets say for one particular signal we required has following frequencies

say letter A of man x has following frequencies when decomposed.(Is it right I mean each alphabet or word we speak based on who speak etc has different frequencies mixed even though single letter right)

a=200+900+1200

noise=300+900+1200

So to reduce noise though we cant eliminate frequencies(same frequencies almost so no bandpass etcc) we can reduce amplitude of noise compared to amplitude of desired signal such that it looks like noise is eliminated.

In above example a(desired signal) of 900hz lets say has 2.5 amplitude and noise of 6 amplitude.So how do we reduce this noise amplitude to eliminate noise from signal.

I mean how does it even distinguish this is noise amplitude and this is signal amplitude. and decrease noise amplitude only  (I mean they are of same frequency so thinking though they are many algorithms like lms,weiner etccc how do they distinguish noise amplitude and reduce from signal amplitude of same frequency what other paramenter is is using to distinguish)

error(gives voice) == mixedsignal(voice+noise) - w(filter coefficent) * noisereference(close to noise lets say in this case noise reference is exactly equal to noise so as to completely eliminate noise ) mu=convergence factor w=w+2*muerrornoisereference

filter coefficent updates from feedback so as to make noiseref exactly equal to noise in mixed signal.

From formula taking w as 0 starting

If voice is more than noise we get high error = voice(high)+noise(low) --- w*refnoise(low for that partiucalr time instant)

so next instant(sample ) we get even more high error since w (filter coefficent increases) w=w+2*muerrornoise

w gets more high and more is subtracted from error to get desired signal.

I mean what is it doing really if voice is more it is subtracting high to remove noise.And if voice is less we get less error so less filter coefficents then it is subtracting less next time instant to remove noise from voice.

I am sharing my assumption. Please correct and guide me If I am wrong. I can't understand or thinking my assumption is wrong somewhere.

We can decompose voice signal or noise signal into a number of sinusoidal signals of different frequencies according to Fourier transform from which we can extract our desired frequencies.

Lets say for one particular signal we required has following frequencies:

say letter A of man x has following frequencies when decomposed.  (Is it right I mean each alphabet or word we speak based on who speak etc has different frequencies mixed even though single letter right)

$$F_\mathbf{A,x}= \{200,900,1200\}\\ f_\text{noise}=\{300,900,1200\}$$

So to reduce noise though we can't eliminate frequencies  (same frequencies almost so no bandpass etcc) we can reduce amplitude of noise compared to amplitude of desired signal such that it looks like noise is eliminated.

In above example a(desired signal) of 900 Hz lets say has an amplitude of 2.5 and noise of 6. So how do we reduce this noise amplitude to eliminate noise from signal?

How does a filter even distinguish this is noise amplitude and this is signal amplitude? And how does it decrease noise amplitude only? (I mean they are of same frequency so thinking though they are many algorithms like lms,weiner etccc how do they distinguish noise amplitude and reduce from signal amplitude of same frequency what other paramenter is is using to distinguish)


error(gives voice) ==  mixedsignal(voice+noise) - w(filter coefficent)   *     noisereference(close to noise lets say in this case noise reference is exactly equal to noise so as to completely eliminate noise )
mu=convergence factor
w=w+2*mu*error*noisereference

filter coefficent updates from feedback so as to make noisereference exactly equal to noise in mixed signal.

From formula taking w as 0 starting

If voice is more than noise we get high


error = voice(high)+noise(low) --- w*refnoise
(low for that partiucalr time instant)

so next instant(sample ) we get even more high error since w (filter coefficent increases)  


w=w+2*mu*error*noise

w gets more high and more is subtracted from error to get desired signal.

I mean what is it doing really if voice is more it is subtracting high to remove noise. And if voice is less we get less error so less filter coefficents then it is subtracting less next time instant to remove noise from voice.

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how filters noise of same frequencies(lms)

I am sharing my assumption .Please correct and guide me If I am wrong.I cant understand or thinking my assumption is wrong somewhere.

We can decompose voice signal or noise signal into no of sinusoidal signals of different frequencies according to fourier transform from which we can extract our desired frequencies.

So how do we filter noise from voice of same frequency.

Lets say for one particular signal we required has following frequencies

say letter A of man x has following frequencies when decomposed.(Is it right I mean each alphabet or word we speak based on who speak etc has different frequencies mixed even though single letter right)

a=200+900+1200

noise=300+900+1200

Though it has different frequencies amplitude may differ.

So to reduce noise though we cant eliminate frequencies(same frequencies almost so no bandpass etcc) we can reduce amplitude of noise compared to amplitude of desired signal such that it looks like noise is eliminated.

So how do we reduce noise amplitude of particular frequency component.

In above example a(desired signal) of 900hz lets say has 2.5 amplitude and noise of 6 amplitude.So how do we reduce this noise amplitude to eliminate noise from signal.

I mean how does it even distinguish this is noise amplitude and this is signal amplitude. and decrease noise amplitude only (I mean they are of same frequency so thinking though they are many algorithms like lms,weiner etccc how do they distinguish noise amplitude and reduce from signal amplitude of same frequency what other paramenter is is using to distinguish)

When I saw adaptive lms

it says

error(gives voice) == mixedsignal(voice+noise) - w(filter coefficent) * noisereference(close to noise lets say in this case noise reference is exactly equal to noise so as to completely eliminate noise ) mu=convergence factor w=w+2*muerrornoisereference

From above formula noise is removed from mixed signal and we get voice signal.

filter coefficent updates from feedback so as to make noiseref exactly equal to noise in mixed signal.

From formula taking w as 0 starting

If voice is more than noise we get high error = voice(high)+noise(low) --- w*refnoise(low for that partiucalr time instant)

so next instant(sample ) we get even more high error since w (filter coefficent increases) w=w+2*muerrornoise

w gets more high and more is subtracted from error to get desired signal.

I mean what is it doing really if voice is more it is subtracting high to remove noise.And if voice is less we get less error so less filter coefficents then it is subtracting less next time instant to remove noise from voice.

Is it subtracting high assuming next time instant(sample) consists of more signal information since present instant(sample) consists of signal amplitude more ?

eventhough present instant or sample is of same frequency how is it even sepereating amplitude of that particualr frequency and why is subtracting more amplitude if voice is more in that particualr sample

If it is subtracting amplitude how is it diffenretiating of same frequency.If voice is more why is it even subtracting high and what exactly it is subtracting.

I also came across another variable called phase .i can understand amplitude and frequency based on voice signal or what we speak.But I cant understand how phase is related to voice what exactly is it doing.

In above example we take non stationary(signal varying with time) and repeating loops by taking no of samples.Our aim is to serperate voice from noise(background of same frequencies).I want to know how this formual works.