diff --git a/docs/pyttadoc/functions.rst b/docs/pyttadoc/functions.rst
index add57c0..eb5d384 100644
--- a/docs/pyttadoc/functions.rst
+++ b/docs/pyttadoc/functions.rst
@@ -26,4 +26,7 @@ Functions
 .. autofunction:: pytta.plot_bars
 .. autofunction:: pytta.plot_spectrogram
 .. autofunction:: pytta.plot_waterfall
-
+.. autofunction:: pytta.dither
+.. autofunction:: pytta.impulsive_noise
+.. autofunction:: pytta.invert_spectrum
+.. autofunction:: pytta.merge_near_far
diff --git a/pytta/__init__.py b/pytta/__init__.py
index d8740aa..47286da 100644
--- a/pytta/__init__.py
+++ b/pytta/__init__.py
@@ -113,7 +113,8 @@
     list_devices, print_devices, get_device_from_user,\
     fft_convolve, find_delay, corr_coef, resample,\
     peak_time, save, load, plot_time, plot_time_dB, plot_freq,\
-    plot_bars, plot_spectrogram, plot_waterfall, SPL
+    plot_bars, plot_spectrogram, plot_waterfall, SPL,\
+    dither, impulsive_noise, invert_spectrum, merge_near_far
 
 # Must go on v0.1.0 (DEPRECATED)
 from .functions import fft_degree
@@ -146,6 +147,10 @@
            'resample',
            'corr_coef',
            'peak_time',
+           'dither',
+           'impulsive_noise',
+           'invert_spectrum',
+           'merge_near_far',
            'save',
            'load',
            'weighting',
diff --git a/pytta/functions.py b/pytta/functions.py
index 9b39239..6cc00b4 100644
--- a/pytta/functions.py
+++ b/pytta/functions.py
@@ -274,6 +274,121 @@ def peak_time(signal):
     else:
         return peaks_time[0]
 
+def dither(signal, bits=24, kind='tri', quiet=False, seed=None):
+    """
+    signal (full scale 1) with dither noise added before it is quantized to
+    bits bits, so that the quantization error becomes noise independent of
+    the signal, as ita_dither (Lipshitz, Wannamaker and Vanderkooy,
+    Quantization and dither: a theoretical survey, J. Audio Eng. Soc.
+    40(5), 1992). kind 'rect' is uniform over one step, 'tri' (triangular,
+    two steps wide) makes the mean and the power of the error independent
+    of the signal, and 'gauss' has a standard deviation of half a step. With
+    quiet, samples equal to zero get no dither.
+    """
+    rng = np.random.default_rng(seed)
+    x = np.asarray(signal.timeSignal, dtype=float)
+    step = 2.0 ** (1 - bits)
+    if kind == 'rect':
+        d = rng.uniform(-step / 2, step / 2, x.shape)
+    elif kind == 'tri':
+        d = rng.uniform(-step / 2, step / 2, x.shape) + rng.uniform(-step / 2, step / 2, x.shape)
+    elif kind == 'gauss':
+        d = rng.normal(0, step / 2, x.shape)
+    else:
+        raise ValueError("kind must be 'rect', 'tri' or 'gauss'.")
+    if quiet:
+        d[x == 0] = 0
+    return SignalObj(x + d, 'time', signal.samplingRate)
+
+
+def impulsive_noise(ir, excitation, noise_start, threshold=20):
+    """
+    Detection of an impulsive event during a sweep measurement, as
+    ita_impulsiveNoiseDetection: the part of the impulse response ir after
+    noise_start seconds, where it holds only noise, is convolved again with
+    the excitation (circularly, at the length of ir) to rebuild the
+    background noise of the recording, and L_max_rms = 20 lg(max |n| /
+    rms(n)) of that noise is compared with threshold (dB). Stationary noise
+    gives 12 to 14 dB.
+
+    Returns (detected, L_max_rms, background noise SignalObj).
+    """
+    h = np.asarray(ir.timeSignal[:, 0], dtype=float).copy()
+    h[:int(round(noise_start * ir.samplingRate))] = 0
+    x = np.asarray(excitation.timeSignal[:, 0], dtype=float)
+    n = np.fft.irfft(np.fft.rfft(h) * np.fft.rfft(x, h.size), h.size)
+    level = 20 * np.log10(np.abs(n).max() / np.sqrt(np.mean(n ** 2)))
+    return level > threshold, level, SignalObj(n, 'time', ir.samplingRate)
+
+
+def _log_crossfade(f, f0, f1):
+    # 0 below f0, 1 above f1, raised cosine on a logarithmic axis between.
+    with np.errstate(divide='ignore'):
+        u = np.clip(np.log(f / f0) / np.log(f1 / f0), 0, 1)
+    return 0.5 - 0.5 * np.cos(np.pi * np.nan_to_num(u))
+
+
+def invert_spectrum(signal, freqRange, minimum_phase=False):
+    """
+    Regularized inverse of the spectrum of signal, C = conj(H) / (|H|^2 +
+    eps) (Kirkeby, Rubak, Nelson and Farina, Design of cross-talk
+    cancellation networks by using fast deconvolution, 1999), with eps
+    negligible inside freqRange = [fmin, fmax] and half the largest |H|^2
+    outside it, crossfaded over half an octave, the rule of the
+    regularization of ImpulsiveResponse. With minimum_phase, the inverse
+    gets the minimum phase of its magnitude, as
+    ita_invert_spk_minphase_regularization.
+    """
+    x = np.asarray(signal.timeSignal, dtype=float)
+    n = x.shape[0]
+    H = np.fft.rfft(x, axis=0)
+    f = np.fft.rfftfreq(n, 1 / signal.samplingRate)
+    fmin, fmax = freqRange
+    outside = 1 - _log_crossfade(f, fmin / np.sqrt(2), fmin) \
+        + _log_crossfade(f, fmax, fmax * np.sqrt(2))
+    eps = (1e-10 + outside[:, None]) * (np.abs(H) ** 2).max(axis=0) / 2
+    C = np.conj(H) / (np.abs(H) ** 2 + eps)
+    if minimum_phase:
+        # Homomorphic: fold the real cepstrum of log |C| onto positive times.
+        c = np.fft.irfft(np.log(np.abs(C)), n, axis=0)
+        fold = np.zeros(n)
+        fold[0] = 1
+        fold[1:(n + 1) // 2] = 2
+        if n % 2 == 0:
+            fold[n // 2] = 1
+        C = np.exp(np.fft.rfft(c * fold[:, None], axis=0))
+    return SignalObj(np.fft.irfft(C, n, axis=0), 'time', signal.samplingRate)
+
+
+def merge_near_far(near, far, crossover, order=4, port=None, port_ratio=1.):
+    """
+    Loudspeaker response from a near-field measurement below crossover (Hz)
+    and a far-field one above it, as
+    ita_add_nearfield_farfield_measurements (Keele, Low-frequency
+    loudspeaker assessment by nearfield sound-pressure measurement, J.
+    Audio Eng. Soc. 22(3), 1974). The near-field response, with the port
+    one times port_ratio (port radius over diaphragm radius) added for a
+    vented box, is delayed to the far-field one (find_delay) and scaled to
+    its level over the third octave around the crossover, and the two are
+    crossfaded with the power-complementary weights 1 / (1 + (f/fc)^(2
+    order)) and its complement, which add up to 1.
+    """
+    fs = far.samplingRate
+    m = np.asarray(near.timeSignal[:, 0], dtype=float)
+    if port is not None:
+        m = m + port_ratio * np.asarray(port.timeSignal[:, 0], dtype=float)
+    x = np.asarray(far.timeSignal[:, 0], dtype=float)
+    n = x.size
+    f = np.fft.rfftfreq(n, 1 / fs)
+    lag = np.argmax(ss.correlate(x, m, mode='full', method='fft')) - (m.size - 1)
+    M = np.fft.rfft(m, n) * np.exp(-2j * np.pi * f * lag / fs)
+    X = np.fft.rfft(x)
+    band = (f >= crossover * 2 ** (-1 / 6)) & (f <= crossover * 2 ** (1 / 6))
+    M *= np.sqrt(np.sum(np.abs(X[band]) ** 2) / np.sum(np.abs(M[band]) ** 2))
+    low = 1 / (1 + (f / crossover) ** (2 * order))
+    return SignalObj(np.fft.irfft(low * M + (1 - low) * X, n), 'time', fs)
+
+
 def fft_degree(*args,**kwargs):
     """
     DEPRECATED
diff --git a/tests/test_measurement_tools.py b/tests/test_measurement_tools.py
new file mode 100644
index 0000000..94dfff2
--- /dev/null
+++ b/tests/test_measurement_tools.py
@@ -0,0 +1,95 @@
+"""
+Tests for dither, impulsive_noise, invert_spectrum and merge_near_far of
+pytta.functions, which cover ita_dither, ita_impulsiveNoiseDetection,
+ita_invert_spk_minphase_regularization and
+ita_add_nearfield_farfield_measurements of the ITA-Toolbox. Each test pins
+one rule on a synthetic measurement.
+"""
+import unittest
+
+import numpy as np
+import scipy.signal as ss
+
+import pytta
+
+FS = 48000
+
+
+def signal(*samples):
+    # A tuple as first local variable: SignalObj compares it with itself
+    # while walking the stack, which an array cannot do before #59.
+    return pytta.SignalObj(samples[0], 'time', FS)
+
+
+class TestMeasurementTools(unittest.TestCase):
+
+    def test_triangular_dither_makes_the_error_independent(self):
+        # With triangular dither the total error of an 8-bit quantizer has
+        # the power step^2 / 4 and no mean, whatever the signal (Lipshitz
+        # et al. 1992); without it the error follows the signal.
+        label = 'x'
+        step = 2.0 ** -7
+        x = 1.5 * step * np.sin(2 * np.pi * 50 * np.arange(FS) / FS)
+        y = pytta.dither(signal(x), 8, 'tri', seed=1).timeSignal[:, 0].astype(float)
+        e = np.round(y / step) * step - x
+        power = np.mean(e ** 2) / (step ** 2 / 4)
+        bias = max(abs(np.mean(e[x > step])), abs(np.mean(e[x < -step]))) / step
+        print(f'\n  dithered error: power {power:.3f} of step^2/4, mean {bias:.1e} step', end='')
+        self.assertLess(abs(power - 1), 0.02)
+        self.assertLess(bias, 0.01)
+
+    def test_impulsive_noise_is_detected(self):
+        # Excitation of flat magnitude and random phase, so that the
+        # deconvolution is exact. A click in the recording raises L_max_rms
+        # of the rebuilt noise above 20 dB; Gaussian noise alone stays near
+        # 13 dB.
+        label = 'x'
+        rng = np.random.default_rng(2)
+        n = 2 ** 16
+        X = np.exp(2j * np.pi * rng.uniform(size=n // 2 + 1))
+        X[[0, -1]] = 1
+        x = np.fft.irfft(X, n)
+        room = np.exp(-np.arange(4800) / 600) * rng.standard_normal(4800)
+        levels = []
+        for click in (0, 0.05):
+            y = np.fft.irfft(np.fft.rfft(room, n) * X, n) + 1e-3 * rng.standard_normal(n)
+            y[30000] += click
+            h = np.fft.irfft(np.fft.rfft(y) / X, n)
+            found, level, _ = pytta.impulsive_noise(signal(h), signal(x), 0.2)
+            levels.append(level)
+            self.assertEqual(found, click > 0)
+        print(f'\n  L_max_rms: {levels[0]:.1f} dB without, {levels[1]:.1f} dB with a click', end='')
+
+    def test_inverse_is_exact_in_band_and_minimum_phase(self):
+        label = 'x'
+        b, a = ss.butter(2, [100, 8000], 'bandpass', fs=FS)
+        h = ss.lfilter(b, a, np.r_[1, np.zeros(8191)])
+        H = np.fft.rfft(h)
+        f = np.fft.rfftfreq(h.size, 1 / FS)
+        band = (f > 200) & (f < 4000)
+        for minimum in (False, True):
+            c = pytta.invert_spectrum(signal(h), [200, 4000], minimum).timeSignal[:, 0].astype(float)
+            C = np.fft.rfft(c)
+            e = np.abs(np.abs(C * H)[band] - 1).max()
+            print(f'\n  inverse, minimum phase {minimum}: in band {e:.1e}', end='')
+            self.assertLess(e, 1e-4)
+        # The minimum phase inverse has the most energy at its start among
+        # inverses with the same magnitude (here the zero phase one).
+        zero = np.roll(np.fft.irfft(np.abs(C), h.size), h.size // 2)
+        lead = np.cumsum(c ** 2) - np.cumsum(zero ** 2)
+        self.assertGreater(lead.min(), -1e-5 * np.sum(c ** 2))   # float32 samples
+
+    def test_near_and_far_field_merge_back(self):
+        # A near-field measurement 20 dB louder and 100 samples earlier than
+        # the far-field one is aligned, scaled and crossfaded back into it.
+        label = 'x'
+        h = ss.lfilter(*ss.butter(2, [40, 15000], 'bandpass', fs=FS), np.r_[1, np.zeros(16383)])
+        far = np.roll(h, 100)
+        merged = pytta.merge_near_far(signal(10 * h), signal(far), 300).timeSignal[:, 0].astype(float)
+        e = np.abs(merged - far).max() / np.abs(far).max()
+        print(f'\n  near and far field merge: {e:.1e}', end='')
+        self.assertLess(e, 1e-4)
+
+
+if __name__ == '__main__':
+    unittest.main()