" PARALLEL DIGITAL SIGNAL PROCESSING FOR EFFICIENT PIANO SYNTHESIS " While computational acoustics techniques for musical instruments emulation reached a remarkable maturity due to continuous development in the last three decades, implementation into embedded digital instruments lags behind, with only a few notable commercial products to solely employ physics based algorithms for acoustic instruments tone synthesis. In this paper a parallel DSP architecture for the efficient implementation of the acoustic piano on embedded processors is reported. The resulting model is able to provide faithful reproduction of the acoustic piano physical behaviour and can also be used as an engine for novel instruments that need to provide advanced multimodal output (haptics, spatial audio) with a low-cost embedded platform. We can use technology behind a physics based piano synthesis engine that has been used in digital pianos and has partially been ported to a portable device that enables piano playing without any keyboard on generic surface.

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A seminar on
PARALLEL DIGITAL SIGNAL PROCESSING FOR
EFFICIENT PIANO SYNTHESIS
Presented by
Mahesh Pawar
(MIS-121697010)

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INTRODUCTION
1. Digital Piano
2. Computational techniques used in piano
3. Physics based algorithm
4. Parallel signal processing
5. Virtual reality
6. To drive musical instruments
7. Haptics and spatial audio

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BACKGROUND
1.Traditional and Physics based synthesis
2.Realistic performance can be observed
3.Sympathetic Resonance
4.Vibrotactile feedback in virtual pianos
Vibrotactile Feedback

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NECESSITY OF PARALLEL DSP
1.Large computational requirement
2.Higher Throughput
3.Important for keyboard which are small
like portable devices

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ARCHITECTURE OF PIANO SYNTHESIS
1.Signal Processing blocks corresponding to mech. elements
- Hammer is for exciter
- String is for resonator
- Instrument body is for resonator
2. String partial differential equation
3.Discretization technique used in Numerical solution
4.Previous Methods used for Solution of PDE of string
- Finite difference
- Digital waveguide
5.New method : Variation of modal Synthesis

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(Continued……….)
6.Base of Algorithm: Decomposition of the string displacement
Where , y(t) = amplitude of the partial
7. This results in ordinary 2nd
order DE for each modes
∑=
=
N
n
n
L
xn
tytxy
1
)sin()(),(
π

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NUMERICAL COMPUTATION OF STRING BLOCKS
2
,2
1
,1
1
,1
,mod
1
,,mod,
1
)()(
)()()(
−−
−
=
++
=
=
=
∑
zaza
zb
H
WzHWzH
zFzHzF
kk
k
ke
N
k
koutkekinstring
hstringstring
Where,
Fstring (z) : is the transversal force at the bridge
Fh (z) is the force coming from the hammer
Hmode,k(z) are the transfer functions of the normal modes

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BLOCKS OF ACOUSTIC PIANO MODEL
•Secondary Resonators
- Needed for simulating the complex beating envelopes
found in piano partials
- They are employed for the simulation of the sympathetic
resonance effect
•duplex resonators model
- A portion of the strings above their speaking length
- Synchronization Mechanism is required
Synthesis Architecture of the acoustic piano model

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SOUNDBOARD FILTERING
-Soundboard Radiation Module
-First Version of Piano : Block based convolution
-Algorithm capable of computing 4 responses at the same
time
-It is based on parallel second-order filter approximation
-Buffer size is kept low (64 samples)
-50x faster compared to FFT based convolution

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PARALLEL IMPLEMENTATION
- Maximum number of resonators :
1. C674x : 5200
2. Cortex – A7 : 3920
3. x86-i5 : 17300
-Modelling of soundboard would require nearly 3 GFLOPS
-Parallel computation of N oscillators is obtained by resorting
to explicit SIMD instruction, SSE2 or NEON for x86 and
ARM processors.

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C67x basics
1.Can implement complex linear or nonlinear algorithms.
3. Can modify easily by changing software.
4. Reduced parts count makes fabrication easier
5. High reliability
6. Features :
- Operating at 225 MHz
- AIC23 stereo codec
- 16 MB RAM, 512 MB non-volatile memory
- Software board configurations
- Configurable boot options
- Expansion Connections
- JTAG emulation

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BLOCK DIAGRAM OF PARALLEL COMPUTING SYSTEM

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CHALLENGES
1.Parallel solutions are harder to implement
2.suffers from communication and coordination overhead
3.Upper bound on Speed up
4.Complexity
5.Portability
6.Resource requirement
7.Scalability
8.Parallel slowdown

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CONCLUSION
1. Parallel DSP can reduce the power consumption of a system
by reducing the supply voltage.
2. This Model presents technical aspects of modern
computational acoustics.
3. Useful for both digital piano and virtual musical instrument.
4. This model allows for large computational savings.
5. Resonators emulate both Sympathetic resonance and
envelope effects.

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REFERENCES
[1] Federico Fontana, Hanna J¨arvel¨ainen, Stefano Papetti, Federico Avanzini, Francesco Zanini, and Valerio Zanini,
“Perception of interactive vibrotactile cues on the acoustic grand and upright piano,” in Proc.Of Joint SMC and ICMC 2014
Conference. National and Kapdistrian University of Athens, Greece, 2014.
[2] Yuri De Pra, Fausto Spoto, Federico Fontana, and Linmi Tao, “Infrared vs. ultrasonic finger detection on a virtual piano
keyboard,”in Proc.of Joint SMC and ICMC 2014 Conference.National and Kapodistrian University of Athens,Greece, 2014
[3] Stefano Zambon, Leonardo Gabrielli, and Balazs Bank, “Expressive physical modeling of keyboard instruments: From
theory to implementation,” in Audio Engineering Society Convention 134. Audio Engineering Society, 2013.
[4] S. Zambon, E. Giordani, B. Bank, and F. Fontana, “A system to reproduce the sound of a stringed instrument,” Deposited
PCT international patent, March 2013.

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Thank You

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Fig. Acoustic Piano Model<<

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Fig. : Architecture of
c6713

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Fig. : Block diagram of c6713 kit

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