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Digital Media

Digital Media

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Digital Media

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  1. Digital Media Lecture 12: Additional Audio Georgia Gwinnett College School of Science and Technology Dr. Jim Rowan

  2. Refer to Supplemental text:

  3. Audio & Illusions • Can you hear this? • “mosquito ring tone” • • Audio illusion: “Creep” •

  4. The nature of sound First, a video from

  5. Other related video #1 How to use visualizations of human speech and music to explain computation:

  6. Other related video #2 David Byrne on how the venue shapes the form of the music performed:

  7. The nature of sound • Three classes of audio that we will discuss • 1) Environmental sound (sounds found in the environment) • 2) Music • 3) Speech

  8. 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

  9. 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…

  10. No… it’s REALLY complicated.. • 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

  11. 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

  12. 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

  13. 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

  14. Why do you think… (cont) • You can hear your name in a noisy room • Cocktail party effect • • Still very much a subject of research

  15. Why? It’s complicated! • • Psychoacoustics • The study of human sound perception • The study of the psychological and physiological affects of sound

  16. 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

  17. Processing Audio

  18. 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

  19. Summed energy & time

  20. Croak! Play Croak!

  21. The sonogram, a snapshot of frequency Croak! Play Croak!

  22. Another way to show audio,frequency density across time Slim Pickens from Dr. Strangelove

  23. Croak! Play Croak!

  24. More examples… Pure sine wave G, E, C Bassoon playing the same notes

  25. Waveform & time G C E

  26. Sonogram G C E

  27. Frequency snapshot

  28. Frequency over time

  29. 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

  30. 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

  31. Audio dithering

  32. 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)

  33. What these filters look like: High pass filter

  34. What these filters look like: Low pass filter

  35. What these filters look like: Notch filter

  36. Audio clipping

  37. 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!

  38. Compressing sound: Voice • Remove silence • Similar to RLE • Non-linear quantization • “companding” • Quiet sounds are represented in greater detail than loud ones

  39. 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

  40. 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)