This document discusses digital audio representation and summarizes key concepts from the lecture, including:
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- The Nyquist theorem states the minimum required sampling frequency is twice the highest frequency contained in the audio signal to avoid aliasing.
- Quantization assigns discrete levels to the sampled audio amplitudes. More levels through more bits provides higher quality but larger file sizes.
- Techniques like PCM (linear quantization), u-Law (logarithmic quantization), DPCM (differential coding of changes), and ADPCM (adaptive quantization) tradeoff quality and file size.
These slides cover the fundamentals of data communication & networking. It covers Channel Capacity It is useful for engineering students & also for the candidates who want to master data communication & computer networking.
These slides cover the fundamentals of data communication & networking. It covers Channel Capacity It is useful for engineering students & also for the candidates who want to master data communication & computer networking.
The application wavelet transform algorithm in testing adc effective number o...ijcsit
In evaluating Analog to Digital Convertors, many parameters are checked for performance and error rate.
One of these parameters is the device Effective Number of Bits. In classical testing of Effective Number of
Bits, testing is based on signal to noise components ratio (SNR), whose coefficients are driven via
frequency domain (Fourier Transform) of ADC’s output signal. Such a technique is extremely sensitive to
noise and require large number of data samples. That is, longer and more complex testing process as the
device under test increases in resolutions. Meanwhile, a new time – frequency domain approach (known as
Wavelet transform) is proposed to measure and analyze Analog-to-Digital Converters parameter of
Effective Number of Bits with less complexity and fewer data samples.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
The application wavelet transform algorithm in testing adc effective number o...ijcsit
In evaluating Analog to Digital Convertors, many parameters are checked for performance and error rate.
One of these parameters is the device Effective Number of Bits. In classical testing of Effective Number of
Bits, testing is based on signal to noise components ratio (SNR), whose coefficients are driven via
frequency domain (Fourier Transform) of ADC’s output signal. Such a technique is extremely sensitive to
noise and require large number of data samples. That is, longer and more complex testing process as the
device under test increases in resolutions. Meanwhile, a new time – frequency domain approach (known as
Wavelet transform) is proposed to measure and analyze Analog-to-Digital Converters parameter of
Effective Number of Bits with less complexity and fewer data samples.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
lect03-audio-representation.ppt
1. CS 414 - Spring 2009
CS 414 – Multimedia Systems Design
Lecture 3 – Digital Audio
Representation
Klara Nahrstedt
Spring 2009
2. CS 414 - Spring 2009
Administrative
Form Groups for MPs
Deadline January 26 to email TA
3. Key Questions
How can a continuous wave form be
converted into discrete samples?
How can discrete samples be converted
back into a continuous form?
CS 414 - Spring 2009
6. Sampling (in time)
Measure amplitude at regular intervals
How many times should we sample?
CS 414 - Spring 2009
7. Example
Suppose we have a sound wave with a
frequency of 1 cycle per second
-1.5
-1
-0.5
0
0.5
1
1.5
CS 414 - Spring 2009
8. Example
If we sample at one cycle per second,
where would the sample points fall?
-1.5
-1
-0.5
0
0.5
1
1.5
CS 414 - Spring 2009
9. Example
If we sample at 1.5 cycles per second,
where will the sample points fall?
-1.5
-1
-0.5
0
0.5
1
1.5
CS 414 - Spring 2009
10. Sampling - Example
If we sample at two cycles per second,
where do the sample points fall?
-1.5
-1
-0.5
0
0.5
1
1.5
CS 414 - Spring 2009
11. Nyquist Theorem
For lossless digitization, the sampling rate
should be at least twice the maximum
frequency response.
In mathematical terms:
fs > 2*fm
where fs is sampling frequency and fm is
the maximum frequency in the signal
CS 414 - Spring 2009
12. Nyquist Limit
max data rate = 2 H log2V bits/second, where
H = bandwidth (in Hz)
V = discrete levels (bits per signal change)
Shows the maximum number of bits that can be sent
per second on a noiseless channel with a bandwidth
of H, if V bits are sent per signal
Example: what is the maximum data rate for a 3kHz
channel that transmits data using 2 levels (binary) ?
(2x3,000xln2=6,000bits/second)
CS 414 - Spring 2009
14. Limited Sampling
Reduce signal frequency to half of
maximum sampling frequency
low-pass filter removes higher-frequencies
e.g., if max sampling frequency is 22kHz,
must low-pass filter a signal down to 11kHz
CS 414 - Spring 2009
15. Sampling Ranges
Auditory range 20Hz to 22.05 kHz
must sample up to to 44.1kHz
common examples are 8.000 kHz, 11.025
kHz, 16.000 kHz, 22.05 kHz, and 44.1 KHz
Speech frequency [200 Hz, 8 kHz]
sample up to 16 kHz
but typically 4 kHz to 11 kHz is used
CS 414 - Spring 2009
18. Quantization
Typically use
8 bits = 256 levels
16 bits = 65,536 levels
How should the levels be distributed?
Linearly? (PCM)
Perceptually? (u-Law)
Differential? (DPCM)
Adaptively? (ADPCM)
CS 414 - Spring 2009
19. Pulse Code Modulation
Pulse modulation
Use discrete time samples of analog signals
Transmission is composed of analog
information sent at different times
Variation of pulse amplitude or pulse timing
allowed to vary continuously over all values
PCM
Analog signal is quantized into a number of
discrete levels
CS 414 - Spring 2009
20. PCM Quantization and Digitization
CS 414 - Spring 2009
Digitization
Quantization
21. Linear Quantization (PCM)
Divide amplitude spectrum into N units (for
log2N bit quantization)
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Sound Intensity
Quantization
Index
CS 414 - Spring 2009
23. Perceptual Quantization (u-Law)
Want intensity values logarithmically
mapped over N quantization units
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Sound Intensity
Quantization
Index
CS 414 - Spring 2009
24. Differential Pulse Code
Modulation (DPCM)
What if we look at sample differences, not
the samples themselves?
dt = xt-xt-1
Differences tend to be smaller
Use 4 bits instead of 12, maybe?
CS 414 - Spring 2009
25. Differential Quantization
(DPCM)
Changes between adjacent samples small
Send value, then relative changes
value uses full bits, changes use fewer bits
E.g., 220, 218, 221, 219, 220, 221, 222, 218,.. (all
values between 218 and 222)
Difference sequence sent: 220, +2, -3, 2, -1, -1, 3, ..
Result: originally for encoding sequence 0..255
numbers need 8 bits;
Difference coding: need only 3 bits
CS 414 - Spring 2009
26. Adaptive Differential Pulse Code
Modulation (ADPCM)
Adaptive similar to DPCM, but adjusts the width of the
quantization steps
Encode difference in 4 bits, but vary the mapping of bits
to difference dynamically
If rapid change, use large differences
If slow change, use small differences
CS 414 - Spring 2009
28. Signal To Noise Ratio
Measures strength of signal to noise
SNR (in DB)=
Given sound form with amplitude in [-A, A]
Signal energy =
)
(
log
10 10
energy
Noise
energy
Signal
-1
-0.75
-0.5
-0.25
0
0.25
0.5
0.75
1
A
0
-A
2
2
A
CS 414 - Spring 2009
29. Quantization Error
Difference between actual and sampled value
amplitude between [-A, A]
quantization levels = N
e.g., if A = 1,
N = 8, = 1/4
N
A
2
-0.5
-0.25
0
0.25
0.5
0.75
1
CS 414 - Spring 2009
30. Compute Signal to Noise Ratio
Signal energy = ; Noise energy = ;
Noise energy =
Signal to noise =
Every bit increases SNR by ~ 6 decibels
12
2
N
A
2
2
2
3 N
A
2
3
log
10
2
N
2
2
A
CS 414 - Spring 2009
31. Example
Consider a full load sinusoidal modulating signal of
amplitude A, which utilizes all the representation levels
provided
The average signal power is P= A2/2
The total range of quantizer is 2A because modulating
signal swings between –A and A. Therefore, if it is N=16
(4-bit quantizer), Δ = 2A/24 = A/8
The quantization noise is Δ2/12 = A2/768
The S/N ratio is (A2/2)/(A2/768) = 384; SNR (in dB) 25.8
dB
CS 414 - Spring 2009
32. Data Rates
Data rate = sample rate * quantization * channel
Compare rates for CD vs. mono audio
8000 samples/second * 8 bits/sample * 1 channel
= 8 kBytes / second
44,100 samples/second * 16 bits/sample *
2 channel = 176 kBytes / second ~= 10MB / minute
CS 414 - Spring 2009