treffpunkt matlab 2017 ita-toolbox · treffpunkt matlab 2017 ita – institute of technical...

Treffpunkt MATLAB 2017

TREFFPUNKT MATLAB 2017ITA-TOOLBOX

Johannes Klein, Marco Berzborn,Ramona Bomhardt, Jan-Gerrit RichterInstitut für Technische AkustikRWTH Aachen


ITA - INSTITUTE OF TECHNICAL ACOUSTICS


ITA – INSTITUTE OF TECHNICAL ACOUSTICS

Faculty of Electrical Engineering and Communication Technology Two departments: Technical Acoustics (Prof. Michael Vorländer) Medical Acoustics (Prof. Janina Fels)

18 Researchers and 1 senior engineer

Architectural acoustics & virtual reality Electro acoustics & measurement Binaural & spatial audio


ARCHITECTURAL ACOUSTICS & VIRTUAL REALITY

Room and building acoustics Numerical acoustics Fast simulation methods Auralization


BINAURAL HEARING & SPATIAL AUDIO

Binaural hearing Array technologies and directivity Medical acoustics and audiology Psychoacoustics


ELECTRO ACOUSTICS & MEASUREMENT

Pro-audio and loudspeakers development and engineering Vibro-acoustics and TPA Condition monitoring www.ita-toolbox.org


ita-toolbox.org || git.rwth-aachen.de

DOWNLOAD & GO…


BACK TO THE TOOLBOX!


DIGITAL SIGNAL PROCESSING



Quantization: evenly spaced integer values for previouslycontinuous amplitude

Common number of quantization steps:216 ≡± 32767

Quantization errors mostly result in white noise



Sampling: evenly spaced discretization of previously time-continuous signal

Nyquist theorem: SignalSampling ff 2



Signal Processing Basics Discrete Fourier Transform (DFT)

Sampling causes mirrored ‘alias’ spectra

discrete representation in frequency domaincauses ‘alias’ impulses in time domain



Signal Processing Basics Influence of the phase


LINEAR TIME-INVARIANT SYSTEMS (LTI)



Signal processing basics LTI system



Signal processing basics Impulse response

Do we have to measure with an impulse?

Pro: directly obtain impulse response Con: not feasible



Signal processing basics Impulse response

Do we have to measure with an impulse? No!



Signal processing basics Advantages of extended measurement signals

Distribute more energy over time

Better Signal-to-Noise Ratio (SNR)

Adapt signals to measurement hardware Equalize equipment Optimal dynamic range

More information about the system properties



Signal processing basics Signal properties

Real-valued time signals – symmetric spectra Periodic time signals – discrete spectra

Frequency resolution depends on measurement time

Averaging to improve SNR

av dB 10lg N

3dB 6dB 9dB 12dB 15dB 18dB2 4 8 16 32 64


Measurement signals How long to measure?

Tmeas > TReverb

Bin distance

analogous to„sampling“

Measurement period

f


Tmeas > 1/∆f



Signal processing basics How does deconvolution work?

treffpunkt matlab 2017 ita-toolbox · treffpunkt matlab 2017 ita – institute of technical...

Documents