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Published byAron McBride Modified over 8 years ago
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Systems (filters)
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Non-periodic signal has continuous spectrum Sampling in one domain implies periodicity in another domain time frequency Periodic sampled signal has always discrete and periodic spectrum
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PROCESSING One way of “signal processing”
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Linear system k*input system k*output Frequency response input system output frequency response = output/input
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deciBel [dB] Log-log frequency response
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Output at time t depends only on the input at time t Memoryless system (amplifier) Frequency response of the system Magnitude (dB) 3 0 phase frequency 1101001000 1101001000 2x
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System with a memory (differentiator) Frequency response of the differentiator (high-pass filter) time 0t0t0 in 0t0t0 time out 1 sample delay -
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System with a memory (integrator) Frequency response of the integrator (low-pass filter) time 0t0t0 in 0t0t0 time out 1 sample delay +
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delay T D - TDTD e.t.c T D =T 1 T D =3T 2 T D =5T 3 Comb filter 1/T D 3/T D 5/T D 1 Frequency response of the system magnitude 0 e.t.c. frequency const
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linear system output input nonlinear system output input
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noise noisy system
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10 ms2 ms Pulse train Its magnitude spectrum
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10 ms 2 ms 20 ms
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T For a single pulse, the period becomes infinite the sum in Fourier series becomes integral THE LINE SPECTRUM BECOMES CONTINUOUS
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time tt 0 frequency t system Dirac impulseImpulse response time Fourier transform frequency Frequency response Dirac impulse contains all frequencies Fourier transform of the impulse response of a system is its frequency response!
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Sinusoidal signal (pure tone) T time [s] frequency [Hz] 1/T Its spectrum ? Truncated sinusoidal signal Its spectrum
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time [s] Truncated signal Infinite signal multiplied by square window Multiplication in one (time) domain is convolution in the dual (frequency) domain
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10 ms2 ms Pulse train Its magnitude spectrum f = 1/2 10 3 =500 Hz line spectrum with |sinc| envelope 1/t p 2/t p 3/t p frequency 0 continuous |sinc| function tptp ∞ ∞ -
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Convolution of the impulse with any function yields this function frequency [Hz] 1000 Spectrum of an infinite 1 kHz sinusoidal signal Truncated
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t = ∞ t = 100 ms t = 13 ms 0 850 Hz
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Narrow-band (high frequency resolution) system Wide-band (low frequency resolution) system frequency time
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Narrow-band (high frequency resolution) Broad-band (low frequency resolution) Long impulse response (low temporal resolution) Short impulse response (high temporal resolution)
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Time-Frequency Compromise Fine resolution in one domain ( f-> 0 or t->0) requires infinite observation interval and therefore pure resolution in the dual domain ( -> or F-> ) – You cannot simultaneously know the exact frequency and the exact temporal locality of the event – infinitely sharp (ideal) filter would require infinitely long delay before it delivers the output
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signal is typically changing in time (non-stationary) time short-term analysis: consider only a short segment of the signal at any given time TT to analysis the signal appear to be periods with the period T TT
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Non-stationary turns into periodic
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Discrete Fourier Transform Discrete and periodic in both domains (time and frequency)
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Short-term Discrete Fourier Transform
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Signal multiplied by the window Spectrum of the signal convolves with the spectrum of the window
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time frequency time
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frequency
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Analysis window 50 ms time [s]01.2 Analysis window 5 ms time [s]01.2 frequency [kHz] 5 0 frequency log amplitude frequency
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frequency [Hz] time [s] frequency log amplitude
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/a;/ / :/ /i:/ /o:/ /u:/ 4 frequency [kHz] 0 time [s]0 6
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Speech production
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/j/ /u/ /a r / /j/ /o/ /j/ /o/
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time [s] 0 1.2 frequency [kHz] 5 0 Fourier transform of the signal s(m) multiplied by the window w(n-m) Spectrum is the line spectrum of the signal convolved with the spectrum of the window Spectral resolution of the short-term Fourier analysis is the same at all frequencies.
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time TT t0t0 fourier transform s(f,t 0 ) spectrum of the short segment time frequency
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Short-term discrete Fourier transform W(m)
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