• Source: Dattorro industry scheme
  • The Dattorro industry scheme is a digital system used to implement a wide range of delay-based audio effects for digital signals. It was proposed by Jon Dattorro. The common nature of these effects allows to produce an output signal as the linear combination of (dynamically modulated) delayed replicas of the input signal. The proposed scheme allows to implement such effects in a compact form, only using a set of three parameters to control the type of effect.
    The Dattorro industry scheme is based on digital delay lines and to ensure a proper resolution in the time domain, it leverages fractional delay lines, thus avoiding discontinuities.
    The effects that this scheme is able to produce are: echo, chorus, vibrato, flanger, doubling, white chorus. These effects are characterized by a nominal delay, a modulating function for the delay and the depth of modulation.


    Delay line interpolation


    Consider continuous time signal



    y
    (
    t
    )


    {\displaystyle y(t)}

    . The



    τ


    {\displaystyle \tau }

    -delayed version of such signal is



    y
    (
    t
    )
    =
    x
    (
    t

    τ
    )


    {\displaystyle y(t)=x(t-\tau )}

    . Considering the signal in the discrete time domain (i.e., sampling it with sampling frequency




    F

    s




    {\displaystyle F_{s}}

    at



    n
    =
    t

    F

    s




    {\displaystyle n=tF_{s}}

    ), we obtain



    y
    [
    n
    ]
    =
    x
    [
    n

    M
    ]


    {\displaystyle y[n]=x[n-M]}

    . The delay line can then be described in terms of the Z-transform of the discrete time signal as




    Y
    (
    z
    )
    =


    Z


    {
    y
    }
    (
    n
    )
    =

    z


    M


    X
    (
    z
    )
    =

    H

    M


    (
    z
    )
    X
    (
    z
    )


    {\displaystyle Y(z)={\mathcal {Z}}\{y\}(n)=z^{-M}X(z)=H_{M}(z)X(z)}

    .
    When going back in the time domain exploiting the inverse Z-transform, this corresponds to




    h

    m


    [
    n
    ]
    =
    δ
    [
    n

    M
    ]


    {\displaystyle h_{m}[n]=\delta [n-M]}

    , where



    δ
    [

    ]


    {\displaystyle \delta [\cdot ]}

    is the Dirac delta. The former equation holds for



    D


    N



    {\displaystyle D\in \mathbb {N} }

    but for the implementation we need a fractional delay, meaning



    D
    =

    D

    +
    (
    D


    D

    )
    =
    M
    +
    d
    ,

    d


    R

    ,

    0
    <
    d
    <
    1


    {\displaystyle D=\lfloor D\rfloor +(D-\lfloor D\rfloor )=M+d,\quad d\in \mathbb {R} ,\ 0
    . In this case, interpolation is required to reconstruct the value of the signal that lies between two samples. One can resort to the preferred interpolation technique, e.g., Lagrange interpolation, or all-pass interpolation.


    Block diagram


    The output of the delay block




    H

    M


    (
    z
    )
    =

    z


    M




    {\displaystyle H_{M}(z)=z^{-M}}

    is noted above and below as rarely it is taken from the last sample of the tap but instead it changes dynamically. Considering the end to end structure, we can write the filter as:

    Setting the coefficients according to the desired effect results in a change of the filter topology. For example, setting the feedback to 0 results in a FIR filter, whereas, if the coefficients are set to be equal, we approximate an all-pass filter.


    Effects


    All the effects can be obtained by changing the parameters of the Dattorro system and by setting the delay ranges according to the following table. Delay values are expressed in milliseconds.


    = Vibrato

    =
    Vibrato is a small quasi-periodic change in pitch of a tone. It's more of a technique than an effect per se but can be added to any audio signal. The delay is modulated with a low frequency sinusoidal function and no mix of the direct path of the signal is considered.


    = Chorus

    =
    The chorus is an effect which tries to emulate multiple independent voices playing in unison. This effect is made as a linear combination of the input signal (dry signal) and a dynamically delayed version of the input (wet signal).


    = Flanging

    =
    The flanging effect originated with tape machines. This effect was created by mixing two tape machines set to play the same track but one of them is slowed down. This produces a lowering in pitch and a delay of the slow track. The process is then repeated with the other track reabsorbing the accumulated delay.
    This effect is very similar to chorus and the main difference is due to the delay range. Chorus usually has longer delay, larger depth and lower modulating frequency.


    = White chorus

    =
    White chorus is a modification to the standard chorus effect aimed at reducing the aberrations introduced by the forward path. The change consists in adding a negative feedback path with a different and fixed tap point in order to obtain an approximation of an all-pass configuration.


    = Doubling

    =
    When the system is used without feedback we achieve doubling. This effect is analogous to that of the Leslie speaker, a particular kind of speaker consisting of a rotating chamber in front of the bass loudspeaker and rotating cones above the treble loudspeakers
    .


    Knob settings




    Pseudocode


    The filter can be implemented in software or hardware. In the following is the pseudocode for a software Dattorro system.

    function dattorro is
    input: x, # input signal
    Fs, # sampling frequency
    depth, # modulation depth
    freq, # LFO frequency
    b, # blend knob
    ff, # feedforward knob
    fb, # feedback knob
    mod # modulation type
    output: y # signal with FX applied

    depth_samples = depth·Fs # compute delay in samples
    if mod is sinusoid # compute delay sequence
    delay_seq = depth_samples*(1 + sin(2·



    π


    {\displaystyle \pi }

    ·freq*t))
    else mod is noise
    lowpass_noise = noise_gen() # generate white noise and low-pass filter it
    delay_seq = depth_samples + depth_samples·lowpass_noise

    for each n of the N samples

    d_int = floor(delay_seq(n)) # integer delay
    d_frac = delay_seq(n) - d_int # fractional delay
    h0 = (d_frac - 1)·(d_frac - 2)/2 # first FIR filter coefficient
    h1 = d_frac·(2 - d_frac) # second FIR filter coefficient
    h2 = d_frac/2·(d_frac - 1) # third FIR filter coefficient

    x_d = delay_line(d_int + 1)·h0 + delay_line(d_int + 2)·h1 + delay_line(d_int + 3)·h2 # delay input

    f_b = fb·x_d # delayed input feedback
    delay_line(2:L) = delay_line(1:L-1) # delay line update
    delay_line(1) = x(i) - fb_comp

    f_f = ff·x_d # feedforward component
    blend_comp = b·delay_line(1) # blend component

    y(n) = ff_comp + blend_comp # compute output sample

    return y


    See also


    Digital delay line
    Effects unit
    Filter design


    References




    Further reading







    H






    {\displaystyle H^{\infty }}

    -optimal delay filters by Masaaki Nagahara and Yutaka Yamamoto
    Digital Audio Signal Processing by Udo Zolzer
    Dattorro, Jon, ed. 1988; The Implementation of Recursive Digital Filters for High-Fidelity Audio; (JAES Volume 36 Issue 11),


    External links


    Introduction to Digital Filters by Julius Smith
    Discrete-Time Modeling of Acoustic Tubes Using Fractional Delay Filters by Valimaki Vesa
    Time varying delay effects by Julius Smith

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