Digital Media

Lecture 12: Additional Audio

Georgia Gwinnett CollegeSchool of Science and Technology

Dr. Jim Rowan

Audio & Illusions

Can you hear this? “mosquito ring tone” http://www.freemosquitoringtones.org/

hearing_test/ Audio illusion: “Creep”

http://www.youtube.com/watch?v=ugriWSmRxcM

The nature of sound

First, a video from ted.comhttp://www.wimp.com/howsound/

Other related video #1

How to use visualizations of human speech and music to explain computation:http://www.youtube.com/watch?

v=mGc6clf_Wt4&feature=bf_prev&list=PL278ECDA0705DAF3DS

Other related video #2

David Byrne on how the venue shapes the form of the music performed:http://www.ted.com/talks/lang/en/

david_byrne_how_architecture_helped_music_evolve.html

The nature of sound

Three types we will discuss– 1) Environmental sound(sounds found in the environment)

and there are two special classes of audio– 2) Music– 3) Speech

The nature of sound Environmental sounds

– Provides information about the surroundings that the human is currently in

Music and Speech– Functionally and uniquely different than other

sounds– Music

• Carries a cultural status• Can be represented by non-sound: MIDI• Can be represented by a musical score

– Speech• Linquistic content• Lends itself to special compression

And it’s complicated…

Converting energy to vibrations and back Transported through some medium

– Either air or some other compressible medium Consider speech

– Starts as an electrical signal (brain & nerves)– Ends as an electrical signal (brain & nerves)– But…

No… it’s REALLY complicated..

http://en.wikipedia.org/wiki/Ear– Starts as an electrical signal (brain & nerves) ==>– Muscle movement (vocal chords)

• Vibrates a column of air sending out a series of compression waves in the air

– Compression waves cause ear membrane to vibrate ==>

– Moves 3 tiny bones ==> – Causes waves in the liquid in the inner ear ==>– Bends tiny hair cells immersed in the liquid ==> – When bent they fire ==>– Sends electrical signals to the cerebral cortex– Processed by the temporal cortex

Audio Illusions

Audio creep… Play a 200 Hz pure tone

– Softly at first– Gradually increase the volume– Most listeners will report that the tone drops in

pitch as the volume increases Play a 2000 Hz pure tone

– Softly at first– Gradually increase the volume– Most listeners will report that the tone rises in

pitch as the volume increases

Why do you think…

You can’t tell where some sounds come from (like some alarms for instance)

You only need one sub woofer when you need at least two for everything else

You can’t tell where sound is coming from underwater

Two things running at the same speed make a “beating” sound

Why do you think… (cont)

With your eyes closed you can’t tell whether a sound is in front of you or behind you

You hear sound that isn’t there (tinnitis) Phantom sounds

– Heard… but not there Masking sounds

– Not simply drowning them out– Can mask a sound that occurs before the

masking sound actually starts

Why do you think… (cont)

You can hear your name in a noisy room– Cocktail party effect– http://en.wikipedia.org/wiki/

Cocktail_party_effect– Still very much a subject of research

Why? It’s complicated!

http://en.wikipedia.org/wiki/Psychoacoustics

Psychoacoustics– The study of human sound perception– The study of the psychological and

physiological affects of sound

Why?It’s complicated!

Sound is physical phenomenon that is interpreted through the human perceptual system– Wavelength affects stereo hearing

• The distance between your ears related to the wavelength

– Speed of sound affects stereo hearing• The faster the sound travels, the wider apart your

ears need to be– You can tell where a sound comes from if

• the wavelength is long enough and• the speed that sound travels is slow enough to allow

the waves arrive at your ears at different times

Processing Audio

Processing audio

How can we characterize sound?– Amplitude– Frequency– Time

Waveform displays– Summed amplitude of all frequencies & time– Amplitude & frequency components at one

point in time – Amplitude & frequency & time

Summed energy & time

Croak!

Play Croak!

The sonogram, a snapshot of frequency

Croak!

Play Croak!

Another way to show audio,frequency density across time

Slim Pickens from Dr. Strangelove

Croak!

Play Croak!

More examples…

Pure sine wave G, E, C

Bassoon playing the same notes

Summed energy & time

G

C

E

Sonogram

G

C

E

Frequency snapshot

Frequency over time

Digitized audio

As we have seen earlier this semester– Sample rate & quantization level – Reduction in sample rate is less noticeable

than reducing the quantization level Jitter is a problem

– Slight changes in timing causes problems 20k+ frequencies?

– Though they can’t be heard they manifest themselves as aliases when reconstructed

Audio Dithering is Weird… add noise…

get better sounding result?!?

Add random noise to the original signal

This noise causes rapid transitioning between the few quantized levels

Makes audio with few quantization levels seem more acceptable

Audio dithering

Audio processingterms to know

Clipping– …but you don’t know how high the amplitude will be

before the performance is recorded Noise gate

– has an amplitude threshold Notch filter

– remove 60 cycle hum Low pass filter High pass filter Time stretching (or shrinking… Limbaugh) Pitch alteration Envelope shaping (modifying attack)

Audio clipping

One thing about humans…

We can actively “filter out” what we don’t want to hear– remember the cocktail party effect?

Over time we don’t hear the pops and snaps of a vinyl record– Have you ever recorded something that you

thought would be good only to play it back and hear the air conditioner or traffic roaring in the background?

A piece of software can’t do this…– …not yet anyway!

Compressing sound: Voice

Remove silence– Similar to RLE

Non-linear quantization• “companding”

– Quiet sounds are represented in greater detail than loud ones

Compressing sound: Voice

Differential Pulse Code Modulation (DPCM)– Related to temporal (inter-frame) video compression

• It predicts what the next sample will be• It sends that difference rather than the absolute

value• Not as effective for sound as it is for images

Adaptive DCPM– Dynamically varies the sample step size

• Large differences were encoded using large steps• Small differences were encoded using small steps

Sound compressionthat is based on perception

The idea is to remove what doesn’t matter

Based on the psycho-acoustic model– Threshold of hearing

• Remove sounds too low to be heard

– High and low frequencies not as important (for voice)