02 bdg Audiocodierung Psychoacoustics WS2013-14€¦ · istics of human ear. ... Fig: Zwicker,...

Prof. Dr.-Ing. K. Brandenburg, [email protected] Prof. Dr.-Ing. G. Schuller, [email protected]

Psychoacoustics

lecturer:[email protected]


Block Diagram of a Perceptual Audio Encoder

Source: Brandenburg, “Vorlesung: Dig. Audiosignalverarbeitung”

• loudness

• critical bands

• masking:

• frequency domain

• time domain

• binaural cues (overview)


Structure of the Human Ear


Structure of the Human Ear

Quelle: Ars Auditus; http://www.dasp.uni-wuppertal.de/index.php?id=57, 2010

outer ear middleear

inner ear

ear canal

pinn

acochlea with

organ of Corti

archwaysossicles

eustachische tubeear drum


Structure of the Human Ear - Cochlea

Quelle: Ars Auditus; http://www.dasp.uni-wuppertal.de/index.php?id=57, 2010

left picture :- cochlea of a 5 month old fetus,- 2 ½ coils, 35 mm long,- dimensions are relative constant,

Quelle: Cochlee, http://www.cochlee.org, 2010

- blue arrow oval window- yellow arrow round window

0,5 mm


Structure of the Human Ear – Organ of Corti- organ of corti of a guinea pig - white bar = 20 µm

outer hair cells (OHC)

inner hair cells (IHC)

Quelle: Cochlee, http://www.cochlee.org, 2010

Quelle: David C. Mountain, Boston University, 146th ASA Meeting

- ~ 3500 IHC and ~12000 OHC at humans

pumping OHC


Preprocessing of Sound in the Peripheral System

Source: Zwicker & Fastl “Psychoacoustics Facts and Models”

- frequency selectivity of the basilar membrane




- frequency selectivity of the basilar membrane (simulation)

21 kHzfrequency channels (Bark scale)

basi

lar m

embr

ane

ampl

itude

14 Hz




- frequency selectivity of the basilar membrane (simulation)

frequency channels (Bark scale)

ampl

itude

in m


Information Processing in the Auditory System


- basilar membrane as a filter bank


Information Processing in the Auditory System

amplitude-time representation

basilar membrane oscillation

neurotransmitter concentration


Sound Perception


Frequency and Level Range of Human Hearing



Threshold in Quiet or the Absolute Threshold



Loudness

• Loudness Level:

• Loudness N: psychological concept to describe the magnitude of an auditory sensation, the loudness of a sound (measured in ‘sone’)

• loudness level L of a sound is measured in ‘phon’ • L of a sound is the sound pressure of a 1 kHz tone which is as loud as

the sound

Fig: Fletcher, “Speech and Hearing in Communication”, 1953.


Loudness

• Equal-Loudness Level Contours:

Links to measure the sensitivity on different frequencies: http://www.phys.unsw.edu.au/jw/hearing.htmlhttp://www.phys.unsw.edu.au/music/dB/loudness.html, 2010

Fig: Suzuki et al., “Precise and Full-range Determination of Two-dimensional Equal Loudness Contours”, 2003.

0.15

1

4

16

N=64 sone

Equal loudness contours of pure tones in a free sound field.

The parameter is expressed in loudness level, LN, and loudness, N.


Loudness

• Loudness Scale:

• aim: double the number of units on this scale means magnitude of sensation is doubled

• relation G(L) between loudness level L and the loudness N on the new scale

• one potential experiment:

• listen to sound with L1 and than adjust same sound until L2=2xL1


Loudness

• Loudness Scale:

• Example 1:• L=40 phonN=1 sone• 2 x loudness: N=2 sone

L=40+10 = 50 phon

• Example 2:• L=40 phonN=1 sone• ½ of loudness: N=0.5

L=40-7.8 = 32.2 phon



Loudness

• Loudness Scale:

• Example 2:• L = 40 phonN=1 sone• ½ of loudness: N=0.5

L=40-7.8=32.2 phon

• for values of L above 40 phon:N=2^((L-40)/10)



Loudness

Loudness contours for loudspeaker and binaural headphone presentation.

headphone loudspeakerFig: Master Thesis, F. Jürgens, TU Ilmenau, 2012.


Loudness

• Loudness Scale:

Fig: en.wikipedia.org/wiki/Sone, 2010.


Sound Example

• Example 1: Equal amplitude

– tones of frequencies 40Hz, 100Hz, 4000Hz, and 12000Hz

• Example 2: Equal amplitude

– Sweep from 0-16000 Hz


Critical Bands


Frequency Grouping in Human Hearing

Source: Brandenburg, “Vorlesung: Dig. Audiosignalverarbeitung

• Different interpretations that produce the same segmentation– Constant distance in the Cochlea– By using tones under the threshold in quiet,

their intensity add up in a critical band and are now audible

– Tones in a critical band above the threshold in quiet: their energy adds up

• Formula for the width of the critical bands– for frequencies < 500 Hz: Constant 100Hz

width– for frequencies > 500 Hz: 0.2*frequency


Frequency Grouping Bandwidth The Critical Bands


Critical bandwidth as a function of frequency.

Approximations for low and high frequency ranges are indicated by broken lines.


Excursus - Critical Bands and Loudness

• Spectral effects - influence of frequency separation:• measure the loudness level (or level of the equally loud 1 kHz tone) of 2 tones by

varying the frequency separation

Fig: Zwicker, Fastl “Psychoacoustics - Facts and Models”, 2nd Edition, 1999.


Excursus - Critical Bands and Loudness

• Spectral effects - influence of bandwidth:• bandwidth of the signals plays an important role• sound level also influence loudness level• total sound intensity (SPL) have to be constant to measure loudness

as function of bandwidth

• critical bandwidth



Critical Bands: Bark Scale

• Critical-band concept used in many models and hypothesis

unit was defined leading to so-called critical-band rate scale

• scale ranging from 0 –24, unit “Bark”

• relation between z and fis important for under-standing many character-istics of human ear


Critical Band Rate and Threshold in Quiet

Source: U. Zölzer, “Digitale Audiosignalverarbeitung”


Signal Power in Critical Bands



Masking


Masking

• data compression

– exploitation of perception in critical bands with

reference to the threshold in quiet is not

enough

• Basis for further compression are masking effects as

described by Zwicker, Fletcher, Fastl, Feldtkeller and others.


Masking of Pure Tones by Noise - Broad-Band Noise


•broad-band noise:•white noise from 20 Hz -20 kHz

•figure:•masking threshold for pure tones masked by broad band noise of different levels

•uniform masking noise (UMN) by equalization of the 10 dB per decade slope


Masking of Pure Tones by Noise - Narrow-Band Noise


•narrow-band noise:•noise with a bandwidth equal or smaller than critical bandwidth

•figure: •threshold of pure tones masked by narrow-band noise for different centre frequencies

•difference between maximum of masked threshold and test tone level


Masking of Pure Tones by Noise - Narrow-Band Noise


•narrow-band noise:•noise with a bandwidth equal or smaller than critical bandwidth

•figure: •dependence of masked threshold on level of narrow-band noise

•dips at higher levels nonlinear effects (difference noise caused by interactions between test tone and noise)


Test: Narrow Band Noise Masking Tone• Example 3:

Narrow Band Noise at 1000 Hz, width 160 Hz;Sine tones at 600, 800, 1000, 1200, 1400, 1600 Hz at varying levels (-80 to -20 dB)


Sound Examples: Masking with White Noise• Example 4: Masking with white noise

– 500 Hz sinusoid tone at varying amplitude ALONE– Level: -40,-35,-30,-25,-20,-15,-10 dB

• Example 5: Masking with white noise– 500 Hz tone at varying amplitude with White

Noise– Level: -40,-35,-30,-25,-20,-15,-10 dB– Noise Level: -50 db

• Example 6: Masking with white noise– 5000 Hz tone at varying amplitude with White

Noise– Levels: same as Example 5


Masking of Pure Tones by Low-Pass or High-Pass Noise



Masking of Pure Tones by Pure Tone


•pure tone:•single frequency

•figure: •1 kHz masking tone with level of 80 dB

•threshold for ‘detection of anything’

difficulties:•beats (hatching)

•masker and difference tone (stippling)


Masking of Pure Tone by Complex Tones


•complex tone:•fundamental tone with its harmonics

•figure: •threshold of pure tones masked by a complex tone with 200 Hz fundamental frequency and nine harmonics


Tonality (1)

• Tonality index :

– noisy signal: = 0

– tonal signal: = 1

• System theory

– Sharp spectral lines = Signal is periodic = Signal is predictable

– Approximation: If the signal is predictable then it should be periodic

– Therefore we can use prediction to approximate if a signal is tonal (by periodicity)


Tonality (2)

Source: Brandenburg, “Vorlesung: Dig. Audiosignalverarbeitung


Tone Masking



Calculating the Masking Threshold

• Different Masking with different maskers:

– Tone masking: (14.5 + i) dB, where i is the frequency in bark

– Noise as a masker: 5.5 dB


Calculating the Masking Threshold

SFM = 0 dB = 0SFM = -60 dB = 1


In-Band Masking



Masking Neighboring Bands



• Example 7: Dynamic range

– Bach organ music with 16 bits per sample





Sound Examples


Temporal Masking Effects (1)



Temporal Masking Effects (2)

• Post-Masking: corresponds to decay in the effect of

the masker expected• Pre-Masking: appears during time before masker is

switched on

– Quick build-up time for loud maskers

– Slower build-up time for faint test sounds

• Frequency resolution Blurring in time

• Frequency resolution in the ear Masking in time

• Because of in-ear fast processing between quiet to

loud signals, we get Pre-Echoes

– Pre-Masking: 1-5 ms

– Post-Masking: ~100ms


Pre-Echo: Example without Pre-Echo


Pre-Echo: Example


Sound Examples

• Example 10:

- Castanets original

• Example 11:

- Castanets coded with a block size of

2048 samples


next lecture:

Basics of Multirate Signal Processing

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02 bdg Audiocodierung Psychoacoustics WS2013-14€¦ · istics of human ear. ... Fig: Zwicker,...

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