diff --git a/src/senselab/audio/tasks/preprocessing/preprocessing.py b/src/senselab/audio/tasks/preprocessing/preprocessing.py
index c9d6f41f..801298e9 100644
--- a/src/senselab/audio/tasks/preprocessing/preprocessing.py
+++ b/src/senselab/audio/tasks/preprocessing/preprocessing.py
@@ -246,7 +246,7 @@ def concatenate_audios(audios: List[Audio]) -> Audio:
         if audio.waveform.shape[0] != num_channels:
             raise ValueError("All audios must have the same number of channels (mono or stereo) to concatenate.")
 
-    concatenated_waveform = torch.cat([audio.waveform for audio in audios], dim=1)
+    concatenated_waveform = torch.cat([audio.waveform.cpu() for audio in audios], dim=1)
 
     # TODO: do we want to concatenate metadata? TBD
 
diff --git a/src/senselab/audio/tasks/speech_enhancement/speechbrain.py b/src/senselab/audio/tasks/speech_enhancement/speechbrain.py
index cab822c6..6b9b2570 100644
--- a/src/senselab/audio/tasks/speech_enhancement/speechbrain.py
+++ b/src/senselab/audio/tasks/speech_enhancement/speechbrain.py
@@ -17,6 +17,7 @@ class SpeechBrainEnhancer:
     """A factory for managing SpeechBrain enhancement pipelines."""
 
     MAX_DURATION_SECONDS = 60  # Maximum duration per segment in seconds
+    MIN_LENGTH = 16  # kernel size for speechbrain/sepformer-wham16k-enhancement
     _models: Dict[str, Union[separator, enhance_model]] = {}
 
     @classmethod
@@ -105,16 +106,21 @@ def enhance_audios_with_speechbrain(
             enhanced_segments = []
 
             for segment in segments:
-                if isinstance(enhancer, enhance_model):
-                    enhanced_waveform = enhancer.enhance_batch(segment.waveform, lengths=torch.tensor([1.0]))
+                if segment.waveform.shape[-1] < cls.MIN_LENGTH:
+                    print(f"Skipping segment with length {segment.waveform.shape[-1]}")
+                    # Append it as it is
+                    enhanced_segments.append(segment)
                 else:
-                    enhanced_waveform = enhancer.separate_batch(segment.waveform)
+                    if isinstance(enhancer, enhance_model):
+                        enhanced_waveform = enhancer.enhance_batch(segment.waveform, lengths=torch.tensor([1.0]))
+                    else:
+                        enhanced_waveform = enhancer.separate_batch(segment.waveform)
 
-                enhanced_segments.append(
-                    Audio(waveform=enhanced_waveform.reshape(1, -1), sampling_rate=segment.sampling_rate)
-                )
-                # TODO: decide what to do with metadata
+                    enhanced_segments.append(
+                        Audio(waveform=enhanced_waveform.reshape(1, -1), sampling_rate=segment.sampling_rate)
+                    )
 
+            # TODO: decide what to do with metadata
             enhanced_audio = concatenate_audios(enhanced_segments)
             enhanced_audio.metadata = audio.metadata
             enhanced_audios.append(enhanced_audio)