diff --git a/.gitignore b/.gitignore
index 6918275..7338ceb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -64,4 +64,7 @@ docs/source/generated/
 .DS_Store
 
 tests/test_data/data_exploration
+tests/test_data/impulsive_data/kurtosis_result.nc
+tests/test_data/impulsive_data/pileDriving_results_pypam.csv
+
 pypam.egg-info/
diff --git a/pypam/_event.py b/pypam/_event.py
index 8dccaf4..4b573f7 100644
--- a/pypam/_event.py
+++ b/pypam/_event.py
@@ -40,18 +40,40 @@ def cut(self, start=0, end=None):
         self.end += end
         self.signal = self.signal[start:end]
 
-    def analyze(self):
+    def analyze(self, impulsive=False, energy_window=0.9):
         """
         Perform all necessary calculations for a single event
 
+        Parameters
+        ----------
+        impulsive : bool
+            whether or not the analysis should perform impulsive metrics
+        energy_window: float
+            If provided, calculate relevant metrics over the given energy window (e.g. RMS_90
+            for energy_window= .9).
         Returns
         -------
-        rms, sel, peak
+        dictionary of metrics: rms, sel, peak, kurtosis, tau
         """
-        rms = self.rms()
-        sel = self.sel()
+
+        if impulsive:
+            windowStr = str(int(energy_window*100))
+            rms = self.rms(energy_window=energy_window)
+            sel = super(Event, self).sel()
+            tau = self.pulse_width(energy_window)
+
+        else:
+            windowStr = ''
+            rms = self.rms()
+            sel = self.sel()
+            tau = (self.end-self.start)/self.fs
+
         peak = self.peak()
-        return rms, sel, peak
+        kurtosis = self.kurtosis()
+        start_time = self.start / self.fs
+
+        out = {'startTime':start_time,'peak':peak,f'rms{windowStr}':rms,'sel':sel,'tau':tau,'kurtosis':kurtosis}
+        return out
 
     def sel(self, high_noise=False):
         """
diff --git a/pypam/acoustic_file.py b/pypam/acoustic_file.py
index 77bbd44..c4133ea 100644
--- a/pypam/acoustic_file.py
+++ b/pypam/acoustic_file.py
@@ -137,6 +137,8 @@ def _bins(self, binsize=None, bin_overlap=0):
         Where i is the index, time_bin is the datetime of the beginning of the block and signal is the signal object
         of the bin
         """
+        if bin_overlap>1:
+            raise ValueError(f'bin_overlap must be fractional.')
         if binsize is None:
             blocksize = self.file.frames - self._start_frame
         else:
@@ -145,7 +147,7 @@ def _bins(self, binsize=None, bin_overlap=0):
         n_blocks = self._n_blocks(blocksize, noverlap=noverlap)
         time_array, _, _ = self._time_array(binsize, bin_overlap=bin_overlap)
         for i, block in tqdm(enumerate(sf.blocks(self.file_path, blocksize=blocksize, start=self._start_frame,
-                                                 overlap=bin_overlap, always_2d=True, fill_value=0.0)),
+                                                 overlap=noverlap, always_2d=True, fill_value=0.0)),
                              total=n_blocks, leave=False, position=0):
             # Select the desired channel
             block = block[:, self.channel]
@@ -155,8 +157,9 @@ def _bins(self, binsize=None, bin_overlap=0):
             signal = sig.Signal(signal=signal_upa, fs=self.fs, channel=self.channel)
             if self.dc_subtract:
                 signal.remove_dc()
-            start_sample = i * blocksize + self._start_frame
-            end_sample = start_sample + len(signal_upa)
+            step = blocksize - noverlap
+            start_sample = i * step + self._start_frame
+            end_sample = start_sample + blocksize
             yield i, time_bin, signal, start_sample, end_sample
         self.file.seek(0)
 
@@ -544,7 +547,7 @@ def _apply(self, method_name, binsize=None, db=True, band_list=None, bin_overlap
     def rms(self, binsize=None, bin_overlap=0, db=True):
         """
         Calculation of root mean squared value (rms) of the signal in upa for each bin
-        Returns Dataframe with 'datetime' as index and 'rms' value as a column
+        Returns Dataset with 'datetime' as coordinate and 'rms' value as a variable
 
         Parameters
         ----------
@@ -558,10 +561,25 @@ def rms(self, binsize=None, bin_overlap=0, db=True):
         rms_ds = self._apply(method_name='rms', binsize=binsize, bin_overlap=bin_overlap, db=db)
         return rms_ds
 
+    def kurtosis(self, binsize=None, bin_overlap=0):
+        """
+        Calculation of kurtosis value of the signal for each bin
+        Returns Dataset with 'datetime' as coordinate and 'kurtosis' value as a variable
+
+        Parameters
+        ----------
+        binsize : float, in sec
+            Time window considered. If set to None, only one value is returned
+        bin_overlap : float [0 to 1]
+            Percentage to overlap the bin windows
+        """
+        kurtosis_ds = self._apply(method_name='kurtosis', binsize=binsize, bin_overlap=bin_overlap, db=False)
+        return kurtosis_ds
+
     def aci(self, binsize=None, bin_overlap=0, nfft=1024, fft_overlap=0.5):
         """
         Calculation of root mean squared value (rms) of the signal in upa for each bin
-        Returns Dataframe with 'datetime' as index and 'rms' value as a column
+        Returns Dataset with 'datetime' as coordinate and 'aci' value as a variable
 
         Parameters
         ----------
@@ -581,7 +599,7 @@ def aci(self, binsize=None, bin_overlap=0, nfft=1024, fft_overlap=0.5):
     def dynamic_range(self, binsize=None, bin_overlap=0, db=True):
         """
         Compute the dynamic range of each bin
-        Returns a dataframe with datetime as index and dr as column
+        Returns a Dataset with 'datetime' as coordinate and 'dr' as variable
 
         Parameters
         ----------
diff --git a/pypam/signal.py b/pypam/signal.py
index 1f22db9..af62ea2 100644
--- a/pypam/signal.py
+++ b/pypam/signal.py
@@ -289,7 +289,7 @@ def window_method(self, method_name, window, **kwargs):
             time.append(block.time)
         return time, output
 
-    def rms(self, db=True, **kwargs):
+    def rms(self, db=True, energy_window = None, **kwargs):
         """
         Calculation of root mean squared value (rms) of the signal in uPa
 
@@ -297,13 +297,42 @@ def rms(self, db=True, **kwargs):
         ----------
         db : bool
             If set to True the result will be given in db, otherwise in uPa
+        energy_window: float
+            If provided, calculate the rms over the given energy window (e.g. RMS_90
+            for energy_window= .9).
         """
-        rms_val = utils.rms(self.signal)
+        if energy_window:
+            [start, end] = utils.energy_window(self.signal, energy_window)
+            rms_val = utils.rms(self.signal[start:end])
+        else:
+            rms_val = utils.rms(self.signal)
         # Convert it to db if applicable
         if db:
             rms_val = utils.to_db(rms_val, ref=1.0, square=True)
         return rms_val
 
+    def pulse_width(self, energy_window, **kwargs):
+        """
+        Returns the pulse width of an impulsive signal
+        according to a fractional energy window
+
+        Parameters
+        ----------
+        energy_window : float [0,1]
+            given energy window to calculate pulse width
+        **kwargs : TYPE
+            DESCRIPTION.
+
+        Returns
+        -------
+        tau: float
+        energy_window pulse width in seconds
+
+        """
+        [start, end] = utils.energy_window(self.signal, energy_window)
+
+        return (end - start) / self.fs
+
     def dynamic_range(self, db=True, **kwargs):
         """
         Compute the dynamic range of each bin
@@ -323,8 +352,19 @@ def dynamic_range(self, db=True, **kwargs):
     def sel(self, db=True, **kwargs):
         """
         Calculate the sound exposure level of an event
+
+        Parameters
+        ----------
+        db : bool
+            If set to True the result will be given in db, otherwise in uPa
+
+        Returns
+        -------
+        sel: float
+        sound exposure level
         """
         y = utils.sel(self.signal, self.fs)
+
         if db:
             y = utils.to_db(y, square=False)
         return y
@@ -339,6 +379,16 @@ def peak(self, db=True, **kwargs):
             y = utils.to_db(y, square=True)
         return y
 
+    def kurtosis(self, **kwargs):
+        """
+        Calculation of kurtosis of the signal
+
+        Parameters
+        ----------
+
+        """
+        return utils.kurtosis(self.signal)
+
     def third_octave_levels(self, db=True, **kwargs):
         """
         Calculation of calibrated 1/3-octave band levels
diff --git a/pypam/units.py b/pypam/units.py
index a61b64e..a913fb7 100644
--- a/pypam/units.py
+++ b/pypam/units.py
@@ -17,7 +17,8 @@
                 'aei': ('AEI', 'unitless'),
                 'adi': ('ADI', 'unitless'),
                 'zcr': ('zcr', 'unitless'),
-                'zcr_avg': ('zcr_avg', 'unitless')
+                'zcr_avg': ('zcr_avg', 'unitless'),
+                'kurtosis': ('Kurtosis', 'unitless'),
                 }
 
 
diff --git a/pypam/utils.py b/pypam/utils.py
index 4256065..94becca 100644
--- a/pypam/utils.py
+++ b/pypam/utils.py
@@ -120,7 +120,7 @@ def sel(signal, fs):
     Parameters
     ----------
     signal : numpy array
-        Signal to compute the dynamic range
+        Signal to compute the SEL
     fs : int
         Sampling frequency
     """
@@ -135,11 +135,53 @@ def peak(signal):
     Parameters
     ----------
     signal : numpy array
-        Signal to compute the dynamic range
+        Signal to compute the peak value
     """
     return np.max(np.abs(signal))
 
 
+@nb.njit
+def kurtosis(signal):
+    """
+    Return the kurtosis of the signal according to Muller et al. 2020
+    Parameters
+    ----------
+    signal : numpy array
+        Signal to compute the kurtosis
+    """
+    n = len(signal)
+    var = (signal - np.mean(signal)) ** 2
+    mu4 = np.sum(var ** 2) / n
+    mu2 = np.sum(var) / (n-1)
+    return mu4/mu2 ** 2
+
+@nb.njit
+def energy_window(signal, percentage):
+    """
+    Return sample window [start, end] which contains a given percentage of the
+    signals total energy. See Madsen 2005 for more details.
+    Parameters
+    ----------
+    signal : numpy array
+        Signal to compute the sel
+    percentage : float between [0,1]
+        percentage of total energy contained in output window
+    """
+    # calculate beginning and ending percentage window (e.g., for x=90%, window = [5%,95%])
+    percent_start = .50 - percentage / 2
+    percent_end = .50 + percentage / 2
+
+    # calculate normalized cumulative energy distribution
+    Ep_cs = np.cumsum(signal ** 2)
+    Ep_cs_norm = Ep_cs / np.max(Ep_cs)
+
+    # find corresponding indices
+    iStartPercent = np.argmin(np.abs(Ep_cs_norm - percent_start))
+    iEndPercent = np.argmin(np.abs(Ep_cs_norm - percent_end))
+
+    window = [iStartPercent, iEndPercent]
+    return window
+
 @nb.njit
 def set_gain(wave, gain):
     """
diff --git a/tests/test_acoustic_file.py b/tests/test_acoustic_file.py
index eceb1ec..4d9ac0b 100644
--- a/tests/test_acoustic_file.py
+++ b/tests/test_acoustic_file.py
@@ -4,15 +4,18 @@
 from tests import skip_unless_with_plots
 import pathlib
 import matplotlib.pyplot as plt
-
+import numpy as np
+import os
 plt.rcParams.update(plt.rcParamsDefault)
+# get relative path
+test_dir = os.path.dirname(__file__)
 
 # Hydrophone Setup
 # If Vpp is 2.0 then it means the wav is -1 to 1 directly related to V
 model = 'ST300HF'
 name = 'SoundTrap'
 serial_number = 67416073
-calibration_file = pathlib.Path("tests/test_data/calibration_data.xlsx")
+calibration_file = pathlib.Path(f"{test_dir}/test_data/calibration_data.xlsx")
 soundtrap = pyhy.soundtrap.SoundTrap(name=name, model=model, serial_number=serial_number,
                                      calibration_file=calibration_file, val='sensitivity', freq_col_id=1,
                                      val_col_id=29, start_data_id=6)
@@ -20,7 +23,7 @@
 
 class TestAcuFile(unittest.TestCase):
     def setUp(self) -> None:
-        self.acu_file = AcuFile('tests/test_data/67416073.210610033655.wav', soundtrap, 1)
+        self.acu_file = AcuFile(f'{test_dir}/test_data/67416073.210610033655.wav', soundtrap, 1)
 
     @skip_unless_with_plots()
     def test_plots(self):
@@ -39,3 +42,23 @@ def test_update_freq_cal(self):
         ds_psd_updated = self.acu_file.update_freq_cal(ds=ds_psd, data_var='band_density')
         print(ds_psd['band_density'].values)
         print(ds_psd_updated['band_density'].values)
+
+    @skip_unless_with_plots()
+    def test_overlapping_bins(self):
+        binsize, bin_overlap = 1, 0.2
+        ds_rms_overlap = self.acu_file.rms(binsize, bin_overlap)
+        ds_rms = self.acu_file.rms(binsize, 0)
+        fig,ax = plt.subplots()
+        ax.plot(ds_rms_overlap.datetime[0:100], ds_rms_overlap.rms[0:100], label='.50 overlap')
+        ax.plot(ds_rms.datetime[0:100], ds_rms.rms[0:100], label='no overlap')
+        ax.legend()
+        fig.show()
+        # compare output time step (dt) to expected time step
+        step = np.mean(np.diff(ds_rms_overlap.start_sample))
+        dt = step/self.acu_file.fs
+        expected_dt = binsize*(1-bin_overlap)
+        error = np.abs(dt-expected_dt)
+        # error should be less than soundfile dt (if rounded)
+        assert error<=(1/self.acu_file.fs)
+
+
diff --git a/tests/test_acoustic_indices.py b/tests/test_acoustic_indices.py
index 8f66a77..1d3b775 100644
--- a/tests/test_acoustic_indices.py
+++ b/tests/test_acoustic_indices.py
@@ -4,7 +4,10 @@
 import pypam.acoustic_indices
 import pyhydrophone as pyhy
 import numpy as np
+import os
 
+# get relative path
+test_dir = os.path.dirname(__file__)
 
 # Hydrophone Setup
 # If Vpp is 2.0 then it means the wav is -1 to 1 directly related to V
@@ -12,7 +15,7 @@
 name = 'SoundTrap'
 serial_number = 67416073
 soundtrap = pyhy.soundtrap.SoundTrap(name=name, model=model, serial_number=serial_number)
-acu_file = AcuFile(sfile='tests/test_data/67416073.210610033655.wav', hydrophone=soundtrap, p_ref=1)
+acu_file = AcuFile(sfile=f'{test_dir}/test_data/67416073.210610033655.wav', hydrophone=soundtrap, p_ref=1)
 
 
 class TestAcousticIndices(unittest.TestCase):
diff --git a/tests/test_acoustic_survey.py b/tests/test_acoustic_survey.py
index c4474d2..7d16cbd 100644
--- a/tests/test_acoustic_survey.py
+++ b/tests/test_acoustic_survey.py
@@ -2,6 +2,7 @@
 import pathlib
 import matplotlib.pyplot as plt
 import numpy as np
+import os
 
 from pypam.acoustic_survey import ASA
 import pyhydrophone as pyhy
@@ -9,15 +10,18 @@
 
 plt.rcParams.update(plt.rcParamsDefault)
 
+# get relative path
+test_dir = os.path.dirname(__file__)
+
 # Data information
-folder_path = pathlib.Path('tests/test_data')
+folder_path = pathlib.Path(f'{test_dir}/test_data')
 
 # Hydrophone Setup
 # If Vpp is 2.0 then it means the wav is -1 to 1 directly related to V
 model = 'ST300HF'
 name = 'SoundTrap'
 serial_number = 67416073
-calibration_file = pathlib.Path("tests/test_data/calibration_data.xlsx")
+calibration_file = pathlib.Path(f"{test_dir}/test_data/calibration_data.xlsx")
 soundtrap = pyhy.soundtrap.SoundTrap(name=name, model=model, serial_number=serial_number,
                                      calibration_file=calibration_file, val='sensitivity', freq_col_id=1,
                                      val_col_id=29, start_data_id=6)
diff --git a/tests/test_data/impulsive_data/madsen_digitized_signal.csv b/tests/test_data/impulsive_data/madsen_digitized_signal.csv
new file mode 100644
index 0000000..f1a5f13
--- /dev/null
+++ b/tests/test_data/impulsive_data/madsen_digitized_signal.csv
@@ -0,0 +1,417 @@
+-419.1446135985857, 5.6166382733781575
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diff --git a/tests/test_data/impulsive_data/madsen_digitized_signal_2.csv b/tests/test_data/impulsive_data/madsen_digitized_signal_2.csv
new file mode 100644
index 0000000..baafa7e
--- /dev/null
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diff --git a/tests/test_data/impulsive_data/pileDriving_excerpt.wav b/tests/test_data/impulsive_data/pileDriving_excerpt.wav
new file mode 100644
index 0000000..118fda8
Binary files /dev/null and b/tests/test_data/impulsive_data/pileDriving_excerpt.wav differ
diff --git a/tests/test_data/impulsive_data/pileDriving_results_benchmark.mat b/tests/test_data/impulsive_data/pileDriving_results_benchmark.mat
new file mode 100644
index 0000000..cfe3c7a
Binary files /dev/null and b/tests/test_data/impulsive_data/pileDriving_results_benchmark.mat differ
diff --git a/tests/test_data/impulsive_data/pileDriving_results_pypam.csv b/tests/test_data/impulsive_data/pileDriving_results_pypam.csv
new file mode 100644
index 0000000..e95f272
--- /dev/null
+++ b/tests/test_data/impulsive_data/pileDriving_results_pypam.csv
@@ -0,0 +1,40 @@
+,startTime,peak,rms90,sel,tau,kurtosis
+0,0.554140625,163.86682478956513,153.06171297885498,141.8665748496793,0.06840625,68.53767250500586
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diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index fcff7ed..7cefb53 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -1,12 +1,15 @@
 import unittest
 import pathlib
+import os
 
 from pypam import dataset
 import pyhydrophone as pyhy
 
+# get relative path
+test_dir = os.path.dirname(__file__)
 
 # Acoustic Data
-summary_path = pathlib.Path('tests/test_data/data_summary.csv')
+summary_path = pathlib.Path(f'{test_dir}/test_data/data_summary.csv')
 include_dirs = False
 
 # Output folder
diff --git a/tests/test_impulsive_metrics.py b/tests/test_impulsive_metrics.py
new file mode 100644
index 0000000..e1f027d
--- /dev/null
+++ b/tests/test_impulsive_metrics.py
@@ -0,0 +1,179 @@
+import numpy as np
+import scipy
+import pandas as pd
+import os
+import pyhydrophone as pyhy
+import pypam
+import xarray as xr
+
+from pypam._event import Event
+import matplotlib.pyplot as plt
+
+from tests import skip_unless_with_plots
+
+test_dir = os.path.dirname(__file__)
+pile_driving_dir = f"{test_dir}\\test_data\\impulsive_data"
+plt.rcParams.update(plt.rcParamsDefault)
+
+name = "AMAR 1"
+model = "Geospectrum"
+serial_number = 247
+sensitivity = -166.6
+preamp_gain = 0
+Vpp = 2
+
+AMAR = pyhy.amar.AmarG3(
+    name, model, serial_number, sensitivity, preamp_gain, Vpp
+)
+
+wav_file = os.path.join(pile_driving_dir, 'pileDriving_excerpt.wav')
+acu_file = pypam.AcuFile(wav_file, AMAR, 1.0)
+
+def load_benchmark_data():
+    """load benchmark data, which was calculated in matlab independently, to a dataframe."""
+    mat = scipy.io.loadmat(os.path.join(pile_driving_dir, 'pileDriving_results_benchmark'))
+
+    df_val = pd.DataFrame(
+        {
+            "startTime": [],
+            "peak": [],
+            "rms90": [],
+            "sel": [],
+            "tau": [],
+            "kurtosis": [],
+        }
+    )
+    for result in mat["BrdBnd_Results"]:
+        # print(result)
+        results = {
+            "startTime": result[0],
+            "peak": result[1],
+            f"rms90": result[3] + 10 * np.log10(0.9), # correct slight expected difference (non-ISO definition)
+            f"sel": result[2],
+            "tau": result[4],
+            "kurtosis": 0,
+        }
+        df_val.loc[len(df_val)] = results
+
+    return df_val
+
+def simplePeaks(
+    acu_file: pypam.AcuFile,
+    threshold,
+    eventSeparation_s,
+    buffer_s,
+    units="Pa",
+):
+    """very simple detection algorithm"""
+    eventSeparation_samples = acu_file.fs * eventSeparation_s
+    sig = acu_file.signal(units=units)
+    peaks, properties = scipy.signal.find_peaks(
+        sig, height=threshold, distance=eventSeparation_samples
+    )
+
+    # apply buffer
+    peaks = peaks - int(buffer_s * acu_file.fs)
+
+    return peaks
+
+
+def test_do_analysis():
+    """perform analysis on pile driving data, save metrics"""
+    event_separation_s = 1.0
+    buffer_s = 0.2
+    locations = simplePeaks(acu_file, 25, event_separation_s, buffer_s)
+
+    events = []
+    signal,fs = acu_file.signal(units='upa'), acu_file.fs
+    for i in range(len(locations) - 1):
+        events.append(Event(signal, fs, start=locations[i], end=locations[i + 1]))
+
+    results_df = pd.DataFrame(
+        {
+            "startTime": [],
+            "peak": [],
+            "rms90": [],
+            "sel": [],
+            "tau": [],
+            "kurtosis": [],
+        }
+    )
+
+    for event in events:
+
+        results = event.analyze(impulsive=True)
+        results_df.loc[len(results_df)] = results
+
+    results_df.to_csv(os.path.join(pile_driving_dir ,'pileDriving_results_pypam.csv'))
+    assert isinstance(results_df, pd.DataFrame)
+
+
+def test_verify_results():
+    """compare results here with benchmark"""
+
+    def calc_diff(df1, df2, key):
+        max_diff = np.max(np.abs(df1[key].values - df2[key].values[0:39]))
+        median_diff = np.abs(np.median(df1[key].values)-np.median(df2[key].values[0:39]))
+        return max_diff, median_diff
+
+    # accept 0.1, 0.5 dB median and maximum differences, respectively
+    tol_median = 0.1
+    tol_max = 0.5
+    tol_pulse_width = 0.01 # s
+    # read data calculated here and from matlab benchmark
+    df = pd.read_csv(os.path.join(pile_driving_dir ,'pileDriving_results_pypam.csv'))
+    df_bm = load_benchmark_data()
+
+    peak_diff = calc_diff(df, df_bm, "peak")
+    rms_diff = calc_diff(df, df_bm, "rms90")
+    sel_diff = calc_diff(df, df_bm, "sel")
+    tau_diff = calc_diff(df,df_bm, "tau")
+
+    assert peak_diff[0]<tol_max
+    assert peak_diff[1]<tol_median
+    assert rms_diff[0]<tol_max
+    assert rms_diff[1]<tol_median
+    assert sel_diff[0]<tol_max
+    assert sel_diff[1]<tol_median
+    assert tau_diff[1]<tol_pulse_width
+
+def test_kurtosis_over_file():
+    """test acu_file implementation of kurtosis (i.e. _apply_multiple)"""
+    #calculate 0.1 s kurtosis
+    ds = acu_file.kurtosis(binsize=0.1)
+    ds.attrs['dc_subtract'] = str(ds.attrs['dc_subtract'])
+    ds.to_netcdf(os.path.join(pile_driving_dir, 'kurtosis_result.nc'))
+    assert isinstance(ds,xr.Dataset)
+
+@skip_unless_with_plots()
+def test_plot_kurtosis():
+    """plot results from above test"""
+    ds = xr.load_dataset(os.path.join(pile_driving_dir, 'kurtosis_result.nc'))
+    fig,ax = plt.subplots()
+    ax.plot(ds.datetime,ds.kurtosis)
+    ax.set_ylabel(f'{ds.kurtosis.standard_name} ({ds.kurtosis.units})')
+    fig.suptitle('test_plot_kurtosis in test_impulsive_metrics.py')
+    plt.show()
+
+@skip_unless_with_plots()
+def test_plot_metrics():
+    """
+    plot pile driving metrics against benchmark, disagreement (~10%) in pulse width assumed to be detector
+    related as energy_window testing confirms that methodology
+    """
+    df = pd.read_csv(os.path.join(pile_driving_dir, 'pileDriving_results_pypam.csv'))
+    df_bm = load_benchmark_data()
+
+    fig,axes = plt.subplots(4,1,sharex=True,figsize=(10,7))
+    metrics = {'peak':'Peak SPL','rms90':'SPL RMS 90','sel':'SEL','tau':'90% pulse width (s)'}
+    for metric,ax in zip(metrics.keys(),axes):
+        ax.plot(df_bm[metric],marker='o',linestyle='none',label='benchmark')
+        ax.plot(df[metric],marker='o',linestyle='none',label='pypam')
+        ax.set_ylabel(f'{metrics[metric]} (dB)')
+        ax.legend()
+    axes[3].set_xlabel('detection index')
+    fig.suptitle('Comparison of pypam/external pile driving analysis')
+    plt.show()
+
+
+
diff --git a/tests/test_millidecade_bands.py b/tests/test_millidecade_bands.py
index 72149a7..50f6539 100644
--- a/tests/test_millidecade_bands.py
+++ b/tests/test_millidecade_bands.py
@@ -1,6 +1,11 @@
 import pyhydrophone as pyhy
 from pypam.acoustic_file import AcuFile
 import pytest
+import os
+
+# get relative path
+
+test_dir = os.path.dirname(__file__)
 
 # Adapted from examples/millidecade_bands.py to use pytest and snapshots.
 
@@ -21,7 +26,7 @@ def millidecade_bands():
     method = 'density'
     band = [0, 4000]
 
-    wav_path = 'tests/test_data/67416073.210610033655.wav'
+    wav_path = f'{test_dir}/test_data/67416073.210610033655.wav'
     acu_file = AcuFile(sfile=wav_path, hydrophone=soundtrap,
                        p_ref=p_ref, timezone='UTC', channel=0, calibration=None,
                        dc_subtract=False)
diff --git a/tests/test_plots.py b/tests/test_plots.py
index 2b07290..3a9c40a 100644
--- a/tests/test_plots.py
+++ b/tests/test_plots.py
@@ -1,4 +1,5 @@
 import unittest
+import os
 
 import xarray
 
@@ -13,10 +14,13 @@
 
 import pypam.units as output_units
 
+# get relative path
+test_dir = os.path.dirname(__file__)
+
 plt.rcParams.update(plt.rcParamsDefault)
 
 # Data information
-folder_path = pathlib.Path('tests/test_data')
+folder_path = pathlib.Path(f'{test_dir}/test_data')
 
 # Hydrophone Setup
 # If Vpp is 2.0 then it means the wav is -1 to 1 directly related to V
@@ -35,9 +39,9 @@
 
 class TestPlots(unittest.TestCase):
     def setUp(self) -> None:
-        self.ds = xarray.open_dataset('tests/test_data/test_day.nc')
+        self.ds = xarray.open_dataset(f'{test_dir}/test_data/test_day.nc')
         self.ds = self.ds.where(self.ds.frequency_bins > 10, drop=True)
-        self.acu_file = AcuFile('tests/test_data/67416073.210610033655.wav', soundtrap, 1)
+        self.acu_file = AcuFile(f'{test_dir}/test_data/67416073.210610033655.wav', soundtrap, 1)
         self.asa = ASA(hydrophone=soundtrap, folder_path=folder_path, binsize=binsize, nfft=nfft, timezone='UTC',
                        include_dirs=include_dirs, zipped=zipped_files, dc_subtract=dc_subtract)
 
@@ -79,12 +83,12 @@ def test_summary_plot(self):
         pypam.plots.plot_summary_dataset(ds=self.ds, percentiles=pctlev, data_var='millidecade_bands',
                                          min_val=40, max_val=130, location=[112.186, 36.713], show=True,
                                          freq_coord='frequency_bins',
-                                         save_path='tests/test_data/data_exploration/img/data_overview/'
+                                         save_path=f'{test_dir}/test_data/data_exploration/img/data_overview/'
                                                    'summary_plot_test1.png')
         pypam.plots.plot_summary_dataset(ds=self.ds, percentiles=pctlev, data_var='millidecade_bands',
                                          min_val=40, max_val=130, location=None, show=True,
                                          freq_coord='frequency_bins',
-                                         save_path='tests/test_data/data_exploration/img/data_overview/'
+                                         save_path=f'{test_dir}/test_data/data_exploration/img/data_overview/'
                                                    'summary_plot_test2.png')
 
     @skip_unless_with_plots()
diff --git a/tests/test_utils.py b/tests/test_utils.py
index ae2799c..8d2fb67 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,4 +1,5 @@
 import pytest
+import os
 import numpy as np
 import pandas as pd
 import xarray
@@ -7,6 +8,9 @@
 from tests import with_plots
 from pypam import utils
 
+plt.rcParams.update(plt.rcParamsDefault)
+# get relative path
+test_dir = os.path.dirname(__file__)
 
 # Create artificial data of 1 second
 @pytest.fixture
@@ -16,7 +20,7 @@ def artificial_data():
     samples = fs
     noise_amp = 100
     signal_amp = 100
-    data = pd.read_csv('tests/test_data/signal_data.csv', header=None)[0].values
+    data = pd.read_csv(f'{test_dir}/test_data/signal_data.csv', header=None)[0].values
     t = np.linspace(0, 1 - 1 / fs, samples)
     phase = 2 * np.pi * t
     for test_freq in test_freqs:
@@ -30,7 +34,7 @@ def artificial_data():
 def test_get_millidecade_bands(artificial_data):
     _, nfft, fs = artificial_data
     bands_limits, bands_c = utils.get_hybrid_millidecade_limits(band=[0, fs/2], nfft=nfft)
-    mdec_bands_test = pd.read_csv('tests/test_data/mdec_bands_test.csv', header=None)
+    mdec_bands_test = pd.read_csv(f'{test_dir}//test_data/mdec_bands_test.csv', header=None)
     assert ((mdec_bands_test.iloc[:, 0] - bands_limits[:-1]) > 5e-5).sum() == 0
     assert ((mdec_bands_test.iloc[:, 2] - bands_limits[1:]) > 5e-5).sum() == 0
     assert ((mdec_bands_test.iloc[:, 1] - bands_c) > 5e-5).sum() == 0
@@ -51,7 +55,7 @@ def test_psd_to_millidecades(artificial_data):
     fbands = scipy.fft.rfftfreq(nfft, 1/fs)
 
     # Load the spectrum used for MANTA
-    spectra_manta = pd.read_csv('tests/test_data/spectra.csv', header=None)
+    spectra_manta = pd.read_csv(f'{test_dir}//test_data/spectra.csv', header=None)
 
     # Check if they are the same
     assert (abs(spectra_manta[0].values - spectra) > 1e-5).sum() == 0
@@ -64,7 +68,7 @@ def test_psd_to_millidecades(artificial_data):
     bandwidths = milli_psd.upper_frequency - milli_psd.lower_frequency
     milli_psd_power = milli_psd * bandwidths
     # Read MANTA's output
-    mdec_power_test = pd.read_csv('tests/test_data/mdec_power_test.csv')
+    mdec_power_test = pd.read_csv(f'{test_dir}//test_data/mdec_power_test.csv')
 
     if with_plots():
         # Plot the two outputs for comparison
@@ -85,7 +89,7 @@ def test_psd_to_millidecades(artificial_data):
 
 
 def test_hmb_to_decidecade():
-    daily_ds = xarray.load_dataset('tests/test_data/test_day.nc')
+    daily_ds = xarray.load_dataset(f'{test_dir}//test_data/test_day.nc')
     daily_ds_deci = utils.hmb_to_decidecade(daily_ds, 'millidecade_bands', freq_coord='frequency_bins')
 
     if with_plots():
@@ -100,3 +104,54 @@ def test_hmb_to_decidecade():
         plt.xlim(left=10)
         plt.legend()
         plt.show()
+
+def test_kurtosis():
+    # build test data and calculate kurtosis
+    n = 1000
+    normally_distributed_data = scipy.stats.norm.rvs(size=n)
+
+    result_utils = utils.kurtosis(normally_distributed_data)
+    result_scipy = scipy.stats.kurtosis(normally_distributed_data, fisher=False)
+
+    # account for slight difference in the method here
+    expected_ratio = ((n - 1) / n)**2
+    result_scipy = result_scipy * expected_ratio
+
+    assert np.isclose(result_scipy, result_utils)
+    # kurtosis should be near 3 for random gaussian distribution
+    assert np.round(result_utils) == 3
+
+def test_energy_window():
+    """Check energy window method against examples in Madsen 2005.
+    """
+    Madsen_tau_1 = {'90':81,'97':125} #fig 1
+    Madsen_tau_2 = {'90': 9, '97': 10} # fig 3a
+    tol = 7 # us
+    plot=True
+    def load_and_window_data(filename, plot=False):
+        raw_data = np.loadtxt(f'{test_dir}\\test_data\\impulsive_data\\{filename}',delimiter=',')
+        t, P = raw_data[:,0], raw_data[:,1]
+        t = t+np.abs(np.min(t))
+        t_interp = np.linspace(0,np.max(t),num=500)
+        P_interp = np.interp(t_interp,t,P)
+        start,end = utils.energy_window(P_interp,.97)
+        tau_97 = t_interp[end]-t_interp[start]
+        start,end = utils.energy_window(P_interp,.90)
+        tau_90 = t_interp[end]-t_interp[start]
+        if plot:
+            fig, ax = plt.subplots()
+            ax.plot(t_interp, P_interp)
+            ax.plot(t_interp[start:end], P_interp[start:end], color='r', linestyle='--')
+            fig.suptitle(f'energy window test, Madsen 2005, tau_90={tau_90:.2f} us')
+            plt.show()
+        return tau_90, tau_97
+
+    tau_90_1,tau_97_1 = load_and_window_data('madsen_digitized_signal.csv')
+    tau_90_2, tau_97_2 = load_and_window_data('madsen_digitized_signal_2.csv')
+    err90_1 = np.abs(Madsen_tau_1['90']-tau_90_1)/Madsen_tau_1['90']
+    err97_1 = np.abs(Madsen_tau_1['97'] - tau_97_1) / Madsen_tau_1['97']
+    err90_2 = np.abs(Madsen_tau_2['90']-tau_90_2)/Madsen_tau_2['90']
+    err97_2 = np.abs(Madsen_tau_2['97'] - tau_97_2) / Madsen_tau_2['97']
+    mean_error = np.mean([err90_1,err97_2,err90_2,err97_1])
+    assert mean_error < 0.05 # accept 5% digitization error
+