Sigma approximation

In mathematics, σ-approximation adjusts a Fourier summation to greatly reduce the Gibbs phenomenon, which would otherwise occur at discontinuities.

Animation of the additive synthesis of a square wave with an increasing number of harmonics by way of the σ-approximation with p=1

In mathematics, σ-approximation adjusts a Fourier summation to greatly reduce the Gibbs phenomenon, which would otherwise occur at discontinuities.

An m-1-term, σ-approximated summation for a series of period T can be written as follows:

    s
    (
    θ
    )
    =
    
      
        1
        2
      
    
    
      a
      
        0
      
    
    +
    
      ∑
      
        k
        =
        1
      
      
        m
        −
        1
      
    
    
      
        (
        
          sinc
          ⁡
          
            
              k
              m
            
          
        
        )
      
      
        p
      
    
    ⋅
    
      [
      
        
          a
          
            k
          
        
        cos
        ⁡
        
          (
          
            
              
                
                  2
                  π
                  k
                
                T
              
            
            θ
          
          )
        
        +
        
          b
          
            k
          
        
        sin
        ⁡
        
          (
          
            
              
                
                  2
                  π
                  k
                
                T
              
            
            θ
          
          )
        
      
      ]
    
    ,
  

{\displaystyle s(\theta )={\frac {1}{2}}a_{0}+\sum _{k=1}^{m-1}\left(\operatorname {sinc} {\frac {k}{m}}\right)^{p}\cdot \left[a_{k}\cos \left({\frac {2\pi k}{T}}\theta \right)+b_{k}\sin \left({\frac {2\pi k}{T}}\theta \right)\right],}

in terms of the normalized sinc function:

    sinc
    ⁡
    x
    =
    
      
        
          sin
          ⁡
          π
          x
        
        
          π
          x
        
      
    
    .
  

{\displaystyle \operatorname {sinc} x={\frac {\sin \pi x}{\pi x}}.}





  
    
      a
      
        k
      
    
  

{\displaystyle a_{k}}

and

      b
      
        k
      
    
  

{\displaystyle b_{k}}

are the typical Fourier Series coefficients, and p, a non negative parameter, determines the amount of smoothening applied, where higher values of p further reduce the Gibbs phenomenon but can overly smoothen the representation of the function.

The term

        (
        
          sinc
          ⁡
          
            
              k
              m
            
          
        
        )
      
      
        p
      
    
  

{\displaystyle \left(\operatorname {sinc} {\frac {k}{m}}\right)^{p}}

is the Lanczos σ factor, which is responsible for eliminating most of the Gibbs phenomenon. This is sampling the right side of the main lobe of the

    sinc
  

{\displaystyle \operatorname {sinc} }

function to rolloff the higher frequency Fourier Series coefficients.

As is known by the uncertainty principle, having a sharp cutoff in the frequency domain (cutting off the Fourier series abruptly without adjusting coefficients) causes a wide spread of information in the time domain (equivalent to large amounts of ringing).

This can also be understood as applying a window function to the Fourier series coefficients to balance maintaining a fast rise time (analogous to a narrow transition band) and small amounts of ringing (analogous to stopband attenuation).

  • Lanczos resampling

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