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EE513 EE513

EE513 - PowerPoint Presentation

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EE513 - PPT Presentation

Audio Signals and Systems Noise Kevin D Donohue Electrical and Computer Engineering University of Kentucky Quantization Noise Signal amplitudes take on a continuum of values A discrete signal must be digitized mapped to a finite set of values to be stored and processed on a computerDSP ID: 390152

room signal noise sound signal room sound noise quantization distortion time absorption power class efficiency compute frequency field range

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Slide1

EE513Audio Signals and Systems

Noise

Kevin D. Donohue

Electrical and Computer Engineering

University of KentuckySlide2

Quantization Noise

Signal amplitudes take on a continuum of values. A discrete signal must be digitized (mapped to a finite set of values) to be stored and processed on a computer/DSP

Digital Signal

Discrete-time Signal

Quantizer

Analog Signal

Coder

11 10 01 00Slide3

Quantization Error and Noise

Quantization has the same effects as adding noise to the signal as long as the rounding error is small compare to the original signal amplitude:

11 10 01 00

Analog

Discrete

Digital

Intervals between quantization levels are proportional to the resulting quantization noise since they limit the maximum rounding or truncation error.

For uniform quantization, the quantization level interval is the maximum signal range divided by the number of quantization intervals.Slide4

Quantization Noise

Original CD clip quantized at 16 bits (blue)

Quantized at 6 bits (red)

Quantized at 3 bits (black)Slide5

Quantization Noise Analysis

Assume is a uniformly distributed (amplitude), white, stationary process that is uncorrelated with the signal.

Show that the signal to quantization noise ratio (SNR

q

) for a full scale range (FSR) sinusoid, quantized with

B bit words is approximately:Note this is the SNR for a signal amplitude at FSR, signals with smaller amplitudes. What would be the formula for a sinusoid with an X% FSR? Slide6

Homework 4.1

Derive a formula for SNRq similar to the one on last slide (in dB) for a sinusoid that is X% of the FSR in amplitude.Slide7

Noise generated from a source inside a room will undergo frequency dependent propagation, absorption and refection before reaching the sink. Thus, the room effectively filters the sound.

Sound impinging on surfaces in the room will be absorbed, reflected, or diffused.

Room Noise

Heat

Direct

Sound

Absorption

Direct

Sound

Reflection

Specular

Reflected

Sound

Direct

Sound

Diffusion

Diffuse

Scattered

Sound

TransmissionSlide8

Reflected and reverberant sounds become particularly bad distractions because they are highly correlated with the original sound source. The use of absorbers and diffusers on reflective surfaces can cut down the reverberation effects in rooms.

The model for a signal received at a point in space from many reflections is given as:

where

n

(t) denotes the attenuation of each reflected signal due to propagation through the air and absorption at each reflected interface and n

is the time delay associated with the travel path from the source to the receiver. The signal in the frequency domain is given by: Reflection Absorption EffectsSlide9

Reverberant Sound Travel

The near or direct field (D)

The free or early field (EF1 and EF2)

The reverberant or diffuse field (RF1 to RF3)Slide10

Decay of Reverberant Sound Field

The time it takes for the reverberant sound field to decay

by 60dB has become a standard way to characterize reverberation in room acoustics.Slide11

For a space with many randomly distributed reflectors (typically large rooms) reverberation time (RT

60

) is defined as the amount of time for the sound pressure in a room to decrease by 60 dB from its maximum. The time is statistically predicted from the room features with the Sabine equation:

whereV is the volume of the room in cubic metersS

i is the surface area of the ith

surface in room (in square meters)ai is the absorption coefficient of ith surfacem is the absorption coefficient of air. Discuss: The relationship between absorption, volume, and RT.

Room Reverberation TimeSlide12

Room Response to White Noise Input

Data collected and spectrogram computed by H.L. Fournier

Note frequency dependence on of decay time.Slide13

Example

Given the simulated reverb signal compute the RT60. Find the autocorrelation function and try to estimate the delays associated with the major scatterers.

% Create reverb signal

[y,fs] = wavread('clap.wav'); % Read in Clap sound

% Apply simulated reverb signal

yout1 = mrevera(y,fs,[30 44 121]*1e-3,[.6 .8 .6]);

taxis = [0:length(yout1)-1]/fs;% Compute envelope of signalenv = abs(hilbert(yout1));figure(1)plot(taxis,20*log10(env+eps)) % Plot Power over timehold on

% Create Line at 60 dB below max point and look for intersection point

mp = max(20*log10(env+eps));mp = mp(1);dt = mp-60;plot(taxis,dt*ones(size(taxis)),'r'); hold off; xlabel('Seconds')ylabel('dB'); title('Envelope of Room Impulse Response')% Compute autocorrelation function of envelop and look for peaks % to indicate delay of major echoes

maxlag = fix(fs*.5);[ac, lags] = xcorr(env-mean(env), maxlag);figure(2)plot(lags/fs,ac)xlabel('seconds')

ylabel('AC coefficient')% Compute autocorrelation function of raw and look for peaks to% indicate delay of major echoes[ac, lags] = xcorr(yout1, maxlag);

figure(3)plot(lags/fs,ac)xlabel('seconds')ylabel('AC coefficient')Slide14

Room Modes

The air in a (small) rectangular room has natural modes of vibration given by:

where

c is the speed of sound in the room p, h, and

r are integers 0,1,2, …., and L, W, and H are the length, width, and height of the room.Slide15

Efficiency

– Output power over Input power (including that of the power supply).

Distortion – Total harmonic distortion (THD). For a sinusoidal signal input, THD is the ratio of power at all harmonic frequencies P

i (excluding the fundamental P

1) to the power at the fundamental frequency. where P

T is total signal power Fidelity – Flatness of frequency response characterized by frequency range and transfer function variation in that range.

Amplifiers and DistortionSlide16

Given the transfer characteristic for a class B amplifier below, compute the THD for a 3 volt input sinusoid.

Example

V

in

V

out

0.6v

-0.6v

7v

3v

-7v

-3vSlide17

Class A

- Low distortion, bad efficiency. Output stage with single transistor requires DC biased output (10-20% efficiency).

Class B - Crossover distortion, good efficiency. Output stage has 2 transistors so bias current is zero (~80% efficient).

Class AB – Reduced crossover distortion, good efficiency. Output stage has 2 transistors with biasing to push signal out of crossover distortion range.

Class D – Moderate distortion, high efficiency, operates in switch mode. Good for battery driven applications.

Amplifier ClassesSlide18

Center Clip Distortion

f

o

= 200 Hz

THD = 4.13%

Harmonic Peak Heights

= [-8, -23, -29, -37, -47, -55, -47, -46, -49, -57];Slide19

Given the transfer characteristic for a class AB amplifier below, compute the THD for a 3 volt input sinusoid.

Example

V

in

V

out

7v

3v

-7v

-3v

1.75v

-1.75vSlide20

Clip/Overload Distortion

f

o

= 200 Hz

THD = 4.14%

Harmonic Peak Heights =

[-7, -21, -46, -37, -44, -49, -45, -72, -49, -55];

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