diff --git a/nodes/selva_lora_trainer.py b/nodes/selva_lora_trainer.py
index 02f6005..959fbe0 100644
--- a/nodes/selva_lora_trainer.py
+++ b/nodes/selva_lora_trainer.py
@@ -186,10 +186,26 @@ def _spectral_metrics(wav: torch.Tensor, sr: int) -> dict:
     rolloff_idx = np.searchsorted(cumsum, 0.85 * cumsum[-1])
     rolloff = float(freqs[min(rolloff_idx, len(freqs) - 1)])
 
+    # Spectral flatness (Wiener entropy): geometric_mean / arithmetic_mean of power
+    # 0.0 = pure tone, 1.0 = white noise — rising value = noise contamination
+    log_power   = np.log(power + 1e-12)
+    flatness    = float(np.exp(log_power.mean()) / (power.mean() + 1e-12))
+
+    # Temporal energy variance — how dynamic the audio is
+    # Compute RMS per frame, take std. Low value = compressed/lifeless
+    hop       = min(_SPEC_HOP, _SPEC_N_FFT)
+    window    = torch.hann_window(_SPEC_N_FFT)
+    stft_full = torch.stft(torch.from_numpy(wav_np), n_fft=_SPEC_N_FFT, hop_length=hop,
+                           window=window, return_complex=True)
+    frame_rms = stft_full.abs().pow(2).mean(dim=0).sqrt().numpy()   # [n_frames]
+    temporal_variance = float(frame_rms.std() / (frame_rms.mean() + 1e-12))
+
     return {
-        "hf_energy_ratio":    round(hf_ratio, 4),
+        "hf_energy_ratio":      round(hf_ratio, 4),
         "spectral_centroid_hz": round(centroid, 1),
         "spectral_rolloff_hz":  round(rolloff, 1),
+        "spectral_flatness":    round(flatness, 4),
+        "temporal_variance":    round(temporal_variance, 4),
     }
 
 
@@ -790,7 +806,9 @@ class SelvaLoraTrainer:
                             spectral_metrics[step] = metrics
                             print(f"[LoRA Trainer] Spectral: hf_ratio={metrics['hf_energy_ratio']:.3f}  "
                                   f"centroid={metrics['spectral_centroid_hz']:.0f}Hz  "
-                                  f"rolloff={metrics['spectral_rolloff_hz']:.0f}Hz", flush=True)
+                                  f"rolloff={metrics['spectral_rolloff_hz']:.0f}Hz  "
+                                  f"flatness={metrics['spectral_flatness']:.3f}  "
+                                  f"temporal_var={metrics['temporal_variance']:.3f}", flush=True)
                             _save_spectrogram(wav, sr, wav_path)
                         except Exception as e:
                             print(f"[LoRA Trainer] Spectral/spectrogram failed: {e}", flush=True)