1853318
Description
Jaffe Synthesis Evaluation part II criteria 6 & 7
- how EFFICIENT is the ALGORITHM
- EXTREMELY IMP. - FOCUS OF MY DISSERTATION
- DETERMINES:
- 1. REALTIME
- NOS. VOICES AVAILABLE
ON GIVEN HARDWARE
- NOS. VOICES AVAILABLE
ON GIVEN HARDWARE
- 2. IN NON-REAL-TIME CONTEXTS -
- TIME OF WAITING FOR RESULTS OF COMPUTATIONS
- IN A FIXED AMOUNT OF TIME TO COMPLETE A PIECE AN EXTREMLY LONG
FEWER ITERATIONS == LESS-REFINED RESULTSTURN AROUND TIME =
- IN A FIXED AMOUNT OF TIME TO COMPLETE A PIECE AN EXTREMLY LONG
FEWER ITERATIONS == LESS-REFINED RESULTSTURN AROUND TIME =
- TIME OF WAITING FOR RESULTS OF COMPUTATIONS
- 1. REALTIME
- DETERMINES:
- QUOTE: "determining the
efficiency of an algorithm
is more complicated than
it might at first appear. It
is not merely a matter of
comparing processing
benchmarks. Numerous
aspects of a technique
and its implementation
come into play,"
- The aspects of the GS techniques and their
implementation that come into play can be divided
into 3 categories:- a memory, b processing requirements c control stream heaviness
- IaMEMORY REQUIREMENTS
- SINE E.G.Amem -
WAVETABLE SYNTH
- Mathews '69
- Mathews '69
- VARIABLE MEM REQ. Changes
with the parameter values
- Vm1 Karplus-Strong
(plucked str)
(waveguidebased
MODELLING):
MORE MEM LO
Pitch LESS MEM
HI Pitch
- Vm1 Karplus-Strong
(plucked str)
(waveguidebased
MODELLING):
MORE MEM LO
Pitch LESS MEM
HI Pitch
- SINE E.G.Amem -
WAVETABLE SYNTH
- Ib PROCESSING DETAILS
- SINE.G.Bproc -
ANALYTICAL
COMPUTATION
- Alt. techniques for sinewave generation
- EGB1proc
- marginally stable 2-POLE FILTERS
- marginally stable 2-POLE FILTERS
- EGB2proc
- EVAL. of COMPLEX PHASOR
- Gordon,
Smith
'85
- Gordon,
Smith
'85
- EVAL. of COMPLEX PHASOR
- EGB3proc
- WAVEGUIDES
- Smith,
Cook 92
- Smith,
Cook 92
- WAVEGUIDES
- EGB1proc
- Alt. techniques for sinewave generation
- II (b) PROCESSING POWER
- 1. quite
COMPLEX"
- 2. depends on the
PROCESSOR ARCHITECTURE
- poor
EXPENSIVE:
- EGIIB
- Finite Element
Modelling
(numerical
INTEGRATION of
DIFFERENCE
EQUATIONS that
Descrb. Masses &
Springs
- BUT wellsuited to: ARRAY -
PARALLEL PROCESSORS
- BUT wellsuited to: ARRAY -
PARALLEL PROCESSORS
- Finite Element
Modelling
(numerical
INTEGRATION of
DIFFERENCE
EQUATIONS that
Descrb. Masses &
Springs
- EGIIB
- good EFFICIENT
- 1. active code fits within
the CACHE of a RISC
CHIP RUNS FASTER
than an active code set
that OVERFLOWS THE
CACHE
- AFreed, Rodet
and Depalle 93
- AFreed, Rodet
and Depalle 93
- 2. With MINIMUM
CHANGES in PROGRAM
FLOW
- well
supported
by:
- a. DSP
processors
- & other b. HEAVILY
PIPELINED ARCHITECTURE
- a. DSP
processors
- well
supported
by:
- PURPOSE BUILT
HARDWARE
INCREASES
ENORMOULSY
THE EFFICIENCY
OF A
TECHNIQUE
- GOOD
- 1. active code fits within
the CACHE of a RISC
CHIP RUNS FASTER
than an active code set
that OVERFLOWS THE
CACHE
- B Some techniques
have a PROCESSING
REQUIRMENT that
CHANGES with the
PARAMETER VALUES
- Time domain
implementation of
CHANT
- more EXPENSIVE as FREQ RISES
- MORE PITCH PERIOD/SEC.
- MORE ADDONS and more TABLE
LOOKUPS PER OUTPUT SAMPLE
- MORE ADDONS and more TABLE
LOOKUPS PER OUTPUT SAMPLE
- MORE PITCH PERIOD/SEC.
- more EXPENSIVE as FREQ RISES
- Vm2 N harmonic partials in Trainlet
synthesis - brightest timbre (chroma) =
bandlimited pulse, softer timbre (low pass
filtered, ie higher partials attenuated
- with MORE MEM/MORE PROC
= NARROWER PEAK with 32
Harmonics than with 8 -
changes behaviour of the
technique
- with MORE MEM/MORE PROC
= NARROWER PEAK with 32
Harmonics than with 8 -
changes behaviour of the
technique
- Time domain
implementation of
CHANT
- Formant Wave
Function FOF
- synth. by
adding
overlapping
vocal tract
IResp.
- synth. by
adding
overlapping
vocal tract
IResp.
- 1. quite
COMPLEX"
- SINE.G.Bproc -
ANALYTICAL
COMPUTATION
- c CONTROL STREAM ATTRIBUTES
- density/heaviness of control stream
- Problem esp. for RT impl.
- may need 2 processors - then expensive
processing to get data from control
processor to the sound procesor
- even wth
just 1
processor -
is their
enough DISK
SPACE/BW
for the cntrl
stream data
ie is control
stream
density >
RAM
- may need 2 processors - then expensive
processing to get data from control
processor to the sound procesor
- Problem esp. for RT impl.
- Dispersion pattern:
- sys. FAIL wth sporadic,
CLUMPED, LARGE DENSE
BURSTS of parameter
update mess,
- e.g. ENTIRE AMP ENV chngs with
note data, at be of each note
- e.g. ENTIRE AMP ENV chngs with
note data, at be of each note
- more easily manageable:
REL STEADY STREAM OF
WELL-SPACED FAIRLY
DENSE CNTRL MESS.
- More EFF. feed ENV DATA BY BREAK
POINT THRU THE NOTE 1 POINT AT A TIME
- More EFF. feed ENV DATA BY BREAK
POINT THRU THE NOTE 1 POINT AT A TIME
- sys. FAIL wth sporadic,
CLUMPED, LARGE DENSE
BURSTS of parameter
update mess,
- density/heaviness of control stream
- TRADE OFF
- well known axiom
- A single period of a waveform
stored in memory
- EGA1mem
- use a non-interpolating
'drop-sample' oscillator
- stored in memory
in a HUGE TABLE
- Greater
Memory
less
processing
- use a non-interpolating
'drop-sample' oscillator
- EGA2mem
- stored in memory in
a SMALLER TABLE
- a more expensive OSC that
interpolates between
samples in the table
- Optimised
Mem - but
MEM < EG1
PROC> EG1
- stored in memory in
a SMALLER TABLE
- EGA3mem
- Precomputed
resultant. WAV:
ƒ WAVE * A ENV
- MAX
MEM
MIN RT
PROC
- MAX
MEM
MIN RT
PROC
- Precomputed
resultant. WAV:
ƒ WAVE * A ENV
- EGA1mem
- well known axiom
- IaMEMORY REQUIREMENTS
- The aspects of the GS techniques and their
implementation that come into play can be divided
into 3 categories:- a memory, b processing requirements c control stream heaviness
- EXTREMELY IMP. - FOCUS OF MY DISSERTATION
- how SPARSE /
DENSE is the
CONTROL STREAM
- what CLASS of SOUND can be represented
- what is SMALLEST
possibel LATENCY
- do ANALYSIS TOOLS exist
- 1 to 5 relate to parameter behaviour, physical, intuitive, wellbehaved, perceptible
changes, sound ID robust e.g. sounds good on more than 1 pitch/resonance of the instr.
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