diff --git a/pynapple/process/signal_processing.py b/pynapple/process/signal_processing.py
index da024804..457fe0c9 100644
--- a/pynapple/process/signal_processing.py
+++ b/pynapple/process/signal_processing.py
@@ -12,10 +12,17 @@
 
 def compute_fft(sig, fs):
     """
-    Performs numpy fft on sig, returns output
-    ..todo: Make sig handle TsdFrame, TsdTensor
 
-    :param sig:    :param fs:    :return:
+    Parameters
+    ----------
+    sig : pynapple.Tsd
+        The signal time series to obtain time frequency decomposition from
+    fs : int / float
+        The sampling frequency of sig
+
+    Returns
+    -------
+
     """
     if not isinstance(sig, nap.Tsd):
         raise TypeError("Currently compute_fft is only implemented for Tsd")
@@ -155,7 +162,7 @@ def compute_wavelet_transform(sig, fs, freqs, n_cycles=7, scaling=0.5, norm="amp
         mwt = np.zeros([len(freqs), len(sig)], dtype=complex)
         for ind, (freq, n_cycle) in enumerate(zip(freqs, n_cycles)):
             mwt[ind, :] = _convolve_wavelet(sig, fs, freq, n_cycle, scaling, norm=norm)
-        return nap.TsdFrame(t=sig.index, d=np.transpose(mwt))
+        return nap.TsdFrame(t=sig.index, d=np.transpose(mwt), time_support=sig.time_support)
     else:
         mwt = np.zeros(
             [sig.values.shape[0], len(freqs), sig.values.shape[1]], dtype=complex
@@ -165,7 +172,7 @@ def compute_wavelet_transform(sig, fs, freqs, n_cycles=7, scaling=0.5, norm="amp
                 mwt[:, ind, channel_i] = _convolve_wavelet(
                     sig[:, channel_i], fs, freq, n_cycle, scaling, norm=norm
                 )
-        return nap.TsdTensor(t=sig.index, d=mwt)
+        return nap.TsdTensor(t=sig.index, d=mwt, time_support=sig.time_support)
 
 
 def _convolve_wavelet(