diff --git a/src/scgenerator/math.py b/src/scgenerator/math.py
index ebba1c6..22132aa 100644
--- a/src/scgenerator/math.py
+++ b/src/scgenerator/math.py
@@ -662,3 +662,38 @@ def differentiate_arr(
             )
         )
     return result / h**diff_order
+
+
+def mean_angle(values: np.ndarray, axis: int = 0):
+    """
+    computes the mean angle of an array along a given axis
+
+    Parameters
+    ----------
+    spectra : np.ndarray
+        complex values from which to compute the mean phase
+    axis : int, optional
+        axis on which to take the mean, by default 0
+
+    Returns
+    ----------
+    np.ndarray
+        array of complex numbers of unit length representing the mean phase, decimated along the
+        specified axis
+
+    Example
+    ----------
+    >>> x = np.array([[1 + 1j, 0 + 2j, -3 - 1j],
+                      [1 + 0j, 2 + 3j, -3 + 1j]])
+    >>> mean_angle(x)
+    array([ 0.92387953+0.38268343j,  0.28978415+0.95709203j,  -1.        +0.j        ])
+
+    """
+    new_shape = values.shape[:axis] + values.shape[axis + 1 :]
+    total_phase = np.sum(
+        values / np.abs(values),
+        axis=axis,
+        where=values != 0,
+        out=np.zeros(new_shape, dtype="complex"),
+    )
+    return (total_phase) / np.abs(total_phase)
diff --git a/src/scgenerator/noise.py b/src/scgenerator/noise.py
index 1f71f65..253e26c 100644
--- a/src/scgenerator/noise.py
+++ b/src/scgenerator/noise.py
@@ -7,6 +7,8 @@ import numpy as np
 from scipy.integrate import cumulative_trapezoid
 from scipy.interpolate import interp1d
 
+from scgenerator import math
+
 
 def irfftfreq(freq: np.ndarray, retstep: bool = False):
     """
@@ -71,20 +73,38 @@ class NoiseMeasurement:
 
     @classmethod
     def from_time_series(
-        cls, time: np.ndarray, signal: np.ndarray, window: str | None = None
+        cls, time: np.ndarray, signal: np.ndarray, window: str = "Square", num_segments: int = 1
     ) -> NoiseMeasurement:
-        correction = 1
-        n = len(time)
-        if window is not None:
-            win_arr = cls._window_functions[window](n)
-            signal = signal * win_arr
-            correction = np.sum(win_arr**2) / n
+        """
+        compute a PSD from a time-series measurement.
+        Parameters
+        ----------
+        time : np.ndarray, shape (n,)
+            time axis, must be uniformly spaced
+        signal : np.ndarray, shape (n,)
+            signal to process. You may or may not remove the DC component, as this will only affect
+            the 0 frequency bin of the PSD
+        window : str | None, optional
+            window to use on the input data to avoid leakage. Possible values are
+            'Square' (default), 'Bartlett', 'Welch' and 'Hann'. You may register your own window
+            function by using `NoiseMeasurement.window_function` decorator, and use the name
+            you gave it as this argument.
+        num_segments : int, optional
+            number of segments to cut the signal in. This will trade lower frequency information for
+            better variance of the estimated PSD. The default 1 means no cutting.
+        """
+        signal_segments = segments(signal, num_segments)
+        n = signal_segments.shape[-1]
+        window_arr = cls._window_functions[window](n)
+        window_correction = np.sum(window_arr**2) / n
+        signal_segments = signal_segments * window_arr
 
-        freq = np.fft.rfftfreq(len(time), time[1] - time[0])
+        freq = np.fft.rfftfreq(n, time[1] - time[0])
         dt = time[1] - time[0]
-        psd = np.fft.rfft(signal) / np.sqrt(0.5 * n / dt)
+        psd = np.fft.rfft(signal_segments) / np.sqrt(0.5 * n / dt)
+        phase = math.mean_angle(psd)
         psd = psd.real**2 + psd.imag**2
-        return cls(freq, psd / correction, phase=np.angle(psd))
+        return cls(freq, psd.mean(axis=0) / window_correction, phase=phase)
 
     def __post_init__(self):
         df = np.diff(self.freq)
@@ -159,6 +179,11 @@ class NoiseMeasurement:
         return integrated_rin(self.freq, self.psd)
 
 
+@NoiseMeasurement.window_function("Square")
+def square_window(n: int):
+    return np.ones(n)
+
+
 @NoiseMeasurement.window_function("Bartlett")
 def bartlett_window(n: int):
     hn = 0.5 * n
@@ -174,3 +199,18 @@ def welch_window(n: int) -> np.ndarray:
 @NoiseMeasurement.window_function("Hann")
 def hann_window(n: int) -> np.ndarray:
     return 0.5 * (1 - np.cos(2 * np.pi * np.arange(n) / n))
+
+
+def segments(signal: np.ndarray, num_segments: int) -> np.ndarray:
+    """
+    cut a signal into segments
+    """
+    if num_segments < 1 or not isinstance(num_segments, (int, np.integer)):
+        raise ValueError(f"{num_segments = } but must be an integer and at least 1")
+    if num_segments == 1:
+        return signal[None]
+    n_init = len(signal)
+    seg_size = 1 << int(np.log2(n_init / (num_segments + 1))) + 1
+    seg = np.arange(seg_size)
+    off = int(n_init / (num_segments + 1))
+    return np.array([signal[seg + i * off] for i in range(num_segments)])
diff --git a/src/scgenerator/physics/pulse.py b/src/scgenerator/physics/pulse.py
index db716da..7ca6b51 100644
--- a/src/scgenerator/physics/pulse.py
+++ b/src/scgenerator/physics/pulse.py
@@ -587,39 +587,6 @@ def C_to_A(C: np.ndarray, effective_area_arr: np.ndarray) -> np.ndarray:
     return (effective_area_arr / effective_area_arr[0]) ** (1 / 4) * C
 
 
-def mean_phase(spectra: np.ndarray, axis: int = 0):
-    """
-    computes the mean phase of spectra
-
-    Parameters
-    ----------
-    spectra : np.ndarray, shape (..., n,..., nt)
-        complex spectra from which to compute the mean phase
-    axis : int, optional
-        axis on which to take the mean, by default 0
-    Returns
-    ----------
-    mean_phase : 1D array of shape (len(spectra[0]))
-        array of complex numbers of unit length representing the mean phase
-
-    Example
-    ----------
-    >>> x = np.array([[1 + 1j, 0 + 2j, -3 - 1j],
-                      [1 + 0j, 2 + 3j, -3 + 1j]])
-    >>> mean_phase(x)
-    array([ 0.92387953+0.38268343j,  0.28978415+0.95709203j,  -1.        +0.j        ])
-
-    """
-
-    total_phase = np.sum(
-        spectra / np.abs(spectra),
-        axis=axis,
-        where=spectra != 0,
-        out=np.zeros(len(spectra[0]), dtype="complex"),
-    )
-    return (total_phase) / np.abs(total_phase)
-
-
 def flatten_phase(spectra: np.ndarray):
     """
     takes the mean phase out of an array of complex numbers
@@ -634,7 +601,7 @@ def flatten_phase(spectra: np.ndarray):
     np.ndarray, shape (..., nt)
         array of same dimensions and amplitude, but with a flattened phase
     """
-    mean_theta = mean_phase(np.atleast_2d(spectra), axis=0)
+    mean_theta = math.mean_angle(np.atleast_2d(spectra), axis=0)
     tiled = np.tile(mean_theta, (len(spectra), 1))
     output = spectra * np.conj(tiled)
     return output
diff --git a/tests/test_noise.py b/tests/test_noise.py
new file mode 100644
index 0000000..ece6b8a
--- /dev/null
+++ b/tests/test_noise.py
@@ -0,0 +1,19 @@
+import numpy as np
+
+import scgenerator as sc
+
+
+def test_segmentation():
+    t = np.arange(32)
+
+    r = np.arange(16)
+    assert np.all(sc.noise.segments(t, 3) == np.vstack([r, r + 8, r + 16]))
+
+    r = np.arange(8)
+
+    assert np.all(sc.noise.segments(t, 4) == np.vstack([r, r + 6, r + 12, r + 18]))
+    assert np.all(sc.noise.segments(t, 5) == np.vstack([r, r + 5, r + 10, r + 15, r + 20]))
+    assert np.all(sc.noise.segments(t, 6) == np.vstack([r, r + 4, r + 8, r + 12, r + 16, r + 20]))
+    assert np.all(
+        sc.noise.segments(t, 7) == np.vstack([r, r + 4, r + 8, r + 12, r + 16, r + 20, r + 24])
+    )