DavidsCD - Revision history

GabrielaV: /* CD Players Explained! */

2005-12-13T07:43:50Z

CD Players Explained!

← Older revision		Revision as of 00:43, 13 December 2005
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	In the frequency domain you can see that this relates to multipliing <math> \frac{1}{T} \sum_{n=-\infty}^\infty X(f - \frac{n}{T}) \cdot </math> by P(f) and results in a quite distored X(f). It has to many high frequency components and would require a really good brick wall filter to get rid of them. From there the signal is sent to a Low Pass Filter where the stair stepped shaped function is smoothed so that it sounds better when the signal is next sent to the speaker.		In the frequency domain you can see that this relates to multipliing <math> \frac{1}{T} \sum_{n=-\infty}^\infty X(f - \frac{n}{T}) \cdot </math> by P(f) and results in a quite distored X(f). It has to many high frequency components and would require a really good brick wall filter to get rid of them. From there the signal is sent to a Low Pass Filter where the stair stepped shaped function is smoothed so that it sounds better when the signal is next sent to the speaker.


	== Two Times Oversampling ==		== Two Times Oversampling ==

	Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math> if we wanted to predistort the signal as well. This makes it so that the resulting wave in the frequency domain more closely matches the original signal. So we get more data points doing it this way, we get to filter the signal however we want, plus this allows for the use of a cheaper low pass filter since the frequency spacing is now 2/T. We just have to be sure and meet the Nyquist criteria now and sample the DAC at T/2 or 88kHz.		Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math> if we wanted to predistort the signal as well. This makes it so that the resulting wave in the frequency domain more closely matches the original signal. So we get more data points doing it this way, we get to filter the signal however we want, plus this allows for the use of a cheaper low pass filter since the frequency spacing is now 2/T. We just have to be sure and meet the Nyquist criteria now and sample the DAC at T/2 or 88kHz.

Andeda at 10:53, 10 December 2004

2004-12-10T10:53:58Z

← Older revision		Revision as of 03:53, 10 December 2004
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	== CD Players Explained! ==		== CD Players Explained! ==

			[[Image:davemic.gif]]
	~~insert pic1~~

	As seen above storing voice samles on a cd only involves a couple of steps.		As seen above storing voice samles on a cd only involves a couple of steps.
	First the data must be passed through a low pass filter incase there are any unwanted high frequencies. In our case we would need a filter to pass anything under 22kHz. If we pass anything higher than this then there will be alaising. Next an analog to digital converter (ADC) samples the data at 44000kHz. It does this by basically picking the closest sampling value to the analog value. Next this data is stored on a CD.		First the data must be passed through a low pass filter incase there are any unwanted high frequencies. In our case we would need a filter to pass anything under 22kHz. If we pass anything higher than this then there will be alaising. Next an analog to digital converter (ADC) samples the data at 44000kHz. It does this by basically picking the closest sampling value to the analog value. Next this data is stored on a CD.

	~~insert pic2~~		[[Image:davespk.gif]]

	Data is taken from the CD player and is represented mathmatically as <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math>. When the data goes through the Digital to Analog Converter (DAC) it is convolved with p(t) to get <math> \tilde x (t) = \sum_{k= -\infty}^ \infty \ x(kT) p (t-KT) </math> as shown below.		Data is taken from the CD player and is represented mathmatically as <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math>. When the data goes through the Digital to Analog Converter (DAC) it is convolved with p(t) to get <math> \tilde x (t) = \sum_{k= -\infty}^ \infty \ x(kT) p (t-KT) </math> as shown below.

Andeda: /* Two Times Oversampling */

2004-12-10T10:41:11Z

Two Times Oversampling

← Older revision		Revision as of 03:41, 10 December 2004
Line 18:		Line 18:
	== Two Times Oversampling ==		== Two Times Oversampling ==

	Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math> if we wanted to predistort the signal as well. This makes it so that the resulting wave in the frequency domain more closely matches the original signal.		Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math> if we wanted to predistort the signal as well. This makes it so that the resulting wave in the frequency domain more closely matches the original signal. So we get more data points doing it this way, we get to filter the signal however we want, plus this allows for the use of a cheaper low pass filter since the frequency spacing is now 2/T. We just have to be sure and meet the Nyquist criteria now and sample the DAC at T/2 or 88kHz.

Andeda: /* CD Players Explained! */

2004-12-10T10:37:26Z

CD Players Explained!

← Older revision		Revision as of 03:37, 10 December 2004
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	= CD Players Explained! =		== CD Players Explained! ==

Andeda: /* Two Times Oversampling */

2004-12-10T10:37:13Z

Two Times Oversampling

← Older revision		Revision as of 03:37, 10 December 2004
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	= Two Times Oversampling =		== Two Times Oversampling ==

	Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math>.		Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math> if we wanted to predistort the signal as well. This makes it so that the resulting wave in the frequency domain more closely matches the original signal.

Andeda: /* Two Times Oversampling */

2004-12-10T10:32:59Z

Two Times Oversampling

← Older revision		Revision as of 03:32, 10 December 2004
Line 18:		Line 18:
	= Two Times Oversampling =		= Two Times Oversampling =

	Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with ~~h(t)~~ the desired impulse response. For two times oversampling ~~h(t)~~ would be by <math>h(lt/2) = frac{T}{2}\int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{j2pilt*f\2} \, df </math>		Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with the desired impulse response. For two times oversampling it would be convolved with <math> h(lt/2) = \frac{T}{2} \int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{(j 2 \pi l t f)/2} df </math>.

Andeda: /* Two Times Oversampling */

2004-12-10T10:24:44Z

Two Times Oversampling

← Older revision		Revision as of 03:24, 10 December 2004
Line 18:		Line 18:
	= Two Times Oversampling =		= Two Times Oversampling =

	Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with h(t) the desired impulse response. For two times oversampling it would be <math> h(lt/2) = \frac{T}{2} ~~/int~~ </math>		Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with h(t) the desired impulse response. For two times oversampling h(t) would be by <math>h(lt/2) = frac{T}{2}\int_{-\frac{-1}{T}}^{\frac{1}{T}} e^{j2pilt*f\2} \, df </math>

Andeda at 10:17, 10 December 2004

2004-12-10T10:17:44Z

← Older revision		Revision as of 03:17, 10 December 2004
Line 16:		Line 16:


	= Oversampling =		= Two Times Oversampling =

			Oversampling is the process of interpolating data so that it looks like we have more data than we really do. Two times oversampling is accomplished by adding a digital interpolation filter right before the DAC. Now <math> \sum_{k= -\infty}^ \infty \ x(kT) \delta (t-KT) </math> is convolved with h(t) the desired impulse response. For two times oversampling it would be <math> h(lt/2) = \frac{T}{2} /int </math>

Andeda at 10:04, 10 December 2004

2004-12-10T10:04:29Z

← Older revision		Revision as of 03:04, 10 December 2004
Line 1:		Line 1:
	CD Players Explained!		= CD Players Explained! =


	insert pic1		insert pic1
Line 14:		Line 15:
	In the frequency domain you can see that this relates to multipliing <math> \frac{1}{T} \sum_{n=-\infty}^\infty X(f - \frac{n}{T}) \cdot </math> by P(f) and results in a quite distored X(f). It has to many high frequency components and would require a really good brick wall filter to get rid of them. From there the signal is sent to a Low Pass Filter where the stair stepped shaped function is smoothed so that it sounds better when the signal is next sent to the speaker.		In the frequency domain you can see that this relates to multipliing <math> \frac{1}{T} \sum_{n=-\infty}^\infty X(f - \frac{n}{T}) \cdot </math> by P(f) and results in a quite distored X(f). It has to many high frequency components and would require a really good brick wall filter to get rid of them. From there the signal is sent to a Low Pass Filter where the stair stepped shaped function is smoothed so that it sounds better when the signal is next sent to the speaker.

	~~[[Image:barnsasample.jpg\|Sampling a signal]]~~
			= Oversampling =

Andeda at 09:45, 10 December 2004

2004-12-10T09:45:55Z

← Older revision		Revision as of 02:45, 10 December 2004
Line 12:		Line 12:
	[[Image:barnsaDA.jpg\|Digital to analog conversion]]		[[Image:barnsaDA.jpg\|Digital to analog conversion]]

	In the frequency domain you can see that this relates to multipliing <math> \frac{1}{T} \sum_{n=-\infty}^\infty X(f - \frac{n}{T}) \cdot </math> by P(f) and results in a quite distored X(f). From ~~thier~~ the signal is sent to a Low Pass Filter where the stair stepped shaped function is smoothed so that it sounds better when the signal is next sent to the speaker.		In the frequency domain you can see that this relates to multipliing <math> \frac{1}{T} \sum_{n=-\infty}^\infty X(f - \frac{n}{T}) \cdot </math> by P(f) and results in a quite distored X(f). It has to many high frequency components and would require a really good brick wall filter to get rid of them. From there the signal is sent to a Low Pass Filter where the stair stepped shaped function is smoothed so that it sounds better when the signal is next sent to the speaker.

	[[Image:barnsasample.jpg\|Sampling a signal]]		[[Image:barnsasample.jpg\|Sampling a signal]]