Jaan Tollander de Balsch

Do the math.

Noise Filtering Using 1€ Filter

This article explores the 1€ Filter (1e filter, 1 euro filter, One Euro Filter), a simple, but powerful algorithm for filtering real-time noisy signals. The article focuses on the implementation of the algorithm in practice, and it covers the mathematical basis, a pseudocode implementation and simple pure Python implementation of the algorithm. In order to understand why and how the filter works, I recommend reading the original article (1).

The 1€ Filter

The 1€ Filter is a low pass filter for filtering noisy signals in real time. It’s also a simple filter with only two configurable parameters. The signal at time \(T_i\) is denoted as a value \(X_i\) and the filtered signal as value \(\hat{X}_i\). The filter is implemented using exponential smoothing \[ \hat{X}_1 = X_1 \\ \hat{X}_i = α X_i + (1-α) \hat{X}_{i-1}, i≥2 \tag{exponential-smoothing} \label{exponential-smoothing} \] where the smoothing factor \(α∈[0, 1]\), instead of being a constant, is adaptive, i.e. dynamically computed using information about the rate of change (speed) of the signal. This aims to balance the jitter versus lag trade-off since people are more sensitive to jitter at low speeds and more sensitive to lag at high speeds. The smoothing factor is defined as \[ α = \frac{1}{1 + \dfrac{τ}{T_e}}, \tag{smoothing-factor} \label{smoothing-factor} \] where \(T_e\) is the sampling period computed from the time difference between the samples \[ T_e=T_i-T_{i-1} \tag{sampling-period} \] and \(τ\) is time constant computed using the cutoff frequency \[ τ = \frac{1}{2πf_C}. \tag{time-constant} \]

The cutoff frequency \(f_C\) which is designed to increase linearly as the rate of change, aka speed, increases \[ f_C=f_{C_{min}} + β|\hat{\dot{X}}_i| \tag{cutoff-frequency} \] where \(f_{C_{min}}>0\) is the minimum cutoff frequency, \(β>0\) is the speed coefficient and \(\hat{\dot{X}}_i\) is the filtered rate of change. The rate of change \(\hat{X}_i\) is defined as the discrete derivative of the signal \[ \dot{X}_1 = 0 \\ \dot{X}_i = \frac{X_i-\hat{X}_{i-1}}{T_e}, i≥2, \tag{derivative} \] which is then filtered using \(\eqref{exponential-smoothing}\) with a constant cutoff frequency \(f_{C_d},\) by default \(f_{C_d}=1\).

The Algorithm

The One Euro Filter algorithm as pseudocode. The precise implementation of the One-Euro-filter will depend on the programming language and paradigm in question. This particular algorithm is written in a functional style.

\(\operatorname{Smoothing-Factor}(f_C, T_e)\)

  1. \(r=2π⋅f_c⋅T_e\)
  2. return \(\dfrac{r}{r+1}\)

\(\operatorname{Exponential-Smoothing}(α, X_i, \hat{X}_{i-1})\)

  1. return \(α X_i + (1-α) \hat{X}_{i-1}\)

\(\operatorname{One-Euro-Filter}(T_i,X_i,T_{i-1},\hat{X}_{i-1},\hat{\dot{X}}_{i-1},f_C,β,f_{C_d})\) for \(i≥2\)

  1. \(T_e=T_i-T_{i-1}\)
  2. \(α_d=\operatorname{Smoothing-Factor}(T_e, f_{C_d})\)
  3. \(\dot{X}_i = \dfrac{X_i-\hat{X}_{i-1}}{T_e}\)
  4. \(\hat{\dot{X}}_i=\operatorname{Exponential-Smoothing}(α_d, \dot{X}_i, \hat{\dot{X}}_{i-1})\)
  5. \(f_C=f_{C_{min}} + β|\hat{\dot{X}}_i|\)
  6. \(α=\operatorname{Smoothing-Factor}(f_C, T_e)\)
  7. \(\hat{X}_i=\operatorname{Exponential-Smoothing}(α, X_i, \hat{X}_{i-1})\)
  8. return \(T_i,\hat{X}_i,\hat{\dot{X}}_i\)

Tuning the Filter

There are two configurable parameters in the model, the minimum cutoff frequency \(f_{C_{min}}\) and the speed coefficient \(β\). Decreasing minimum cutoff frequency will decrease slow speed jitter. Increasing speed coefficient will decrease speed lag.

Python Implementation

The following object-oriented Python implementation is also available in OneEuroFilter GitHub repository. The object-oriented approach stores the previous values inside the object instead of giving them explicitly as a return value as functional implementation would. It should be relatively simple to implement this algorithm in other languages.

import math

def smoothing_factor(t_e, cutoff):
    r = 2 * math.pi * cutoff * t_e
    return r / (r + 1)

def exponential_smoothing(a, x, x_prev):
    return a * x + (1 - a) * x_prev

class OneEuroFilter:
    def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0,
        """Initialize the one euro filter."""
        # The parameters.
        self.min_cutoff = float(min_cutoff)
        self.beta = float(beta)
        self.d_cutoff = float(d_cutoff)
        # Previous values.
        self.x_prev = float(x0)
        self.dx_prev = float(dx0)
        self.t_prev = float(t0)

    def __call__(self, t, x):
        """Compute the filtered signal."""
        t_e = t - self.t_prev

        # The filtered derivative of the signal.
        a_d = smoothing_factor(t_e, self.d_cutoff)
        dx = (x - self.x_prev) / t_e
        dx_hat = exponential_smoothing(a_d, dx, self.dx_prev)

        # The filtered signal.
        cutoff = self.min_cutoff + self.beta * abs(dx_hat)
        a = smoothing_factor(t_e, cutoff)
        x_hat = exponential_smoothing(a, x, self.x_prev)

        # Memorize the previous values.
        self.x_prev = x_hat
        self.dx_prev = dx_hat
        self.t_prev = t

        return x_hat

An example of how to use the One Euro Filter for filtering a signal in real time.

# Let signal be a generator that yields a tuple (t, x)
# where t is time and x the values of the signal.
signal = ...

# Instantiate the OneEuroFilter with initial values and
# your value for parameters min_cutoff and beta.
t0, x0 = next(signal)
one_euro_filter = OneEuroFilter(

# Filter the signal in real time.
for (t, x) in signal:
    x_hat = one_euro_filter(t, x)


1. CASIEZ, Géry, ROUSSEL, Nicolas and VOGEL, Daniel. 1€ filter: A simple speed-based low-pass filter for noisy input in interactive systems. In : Proceedings of the sigchi conference on human factors in computing systems. ACM, 2012. p. 2527–2530.