diff --git a/torchcomp/__init__.py b/torchcomp/__init__.py
index ffbf225..105003f 100644
--- a/torchcomp/__init__.py
+++ b/torchcomp/__init__.py
@@ -1,14 +1,42 @@
 import torch
 import torch.nn.functional as F
 from typing import Union
+from torchaudio.functional import lfilter
 
 from .core import compressor_core
 
-__all__ = ["compexp_gain", "limiter_gain", "ms2coef"]
+__all__ = ["compexp_gain", "limiter_gain", "ms2coef", "avg"]
 
 amp2db = lambda x: 20 * torch.log10(x)
 db2amp = lambda x: 10 ** (x / 20)
 ms2coef = lambda ms, sr: (1 - torch.exp(-2200 / ms / sr))
+coef2ms = lambda coef, sr: -2200 / (sr * torch.log(1 - coef))
+
+
+def avg(rms: torch.Tensor, avg_coef: Union[torch.Tensor, float]):
+    """Compute the running average of a signal.
+
+    Args:
+        rms (torch.Tensor): Input signal.
+        avg_coef (torch.Tensor): Coefficient for the average RMS.
+
+    Shape:
+        - rms: :math:`(B, T)` where :math:`B` is the batch size and :math:`T` is the number of samples.
+        - avg_coef: :math:`(B,)` or a scalar.
+
+    """
+
+    avg_coef = torch.as_tensor(
+        avg_coef, dtype=rms.dtype, device=rms.device
+    ).broadcast_to(rms.shape[0])
+    assert torch.all(avg_coef > 0) and torch.all(avg_coef <= 1)
+
+    return lfilter(
+        rms,
+        torch.stack([torch.ones_like(avg_coef), avg_coef - 1], 1),
+        torch.stack([avg_coef, torch.zeros_like(avg_coef)], 1),
+        False,
+    )
 
 
 def compexp_gain(
@@ -64,7 +92,8 @@ def compexp_gain(
     log_x_rms = amp2db(x_rms)
     g = (
         torch.minimum(
-            comp_slope * (comp_thresh - log_x_rms), exp_slope * (exp_thresh - log_x_rms)
+            comp_slope[:, None] * (comp_thresh[:, None] - log_x_rms),
+            exp_slope[:, None] * (exp_thresh[:, None] - log_x_rms),
         )
         .neg()
         .relu()