Update time_domain.py

time_domain.py

@@ -0,0 +1,45 @@
+# time_domain.py
+# ============================
+# Time-domain statistical analysis for Audio Forensic Analyzer
+# Logic preserved exactly from original app.py
+
+import numpy as np
+import librosa
+
+
+def compute_time_domain_stats(y):
+    """Calculate time-domain statistics exactly as in original analyzer."""
+    
+    # Peak amplitude
+    peak = float(np.max(np.abs(y)))
+
+    # RMS amplitude
+    rms = float(np.sqrt(np.mean(y ** 2)))
+
+    # dB conversions (protecting against log(0))
+    peak_db = 20 * np.log10(max(peak, 1e-12))
+    rms_db = 20 * np.log10(max(rms, 1e-12))
+
+    # Crest factor (indicator of compression)
+    crest_factor = peak_db - rms_db
+
+    # Noise floor estimate (10th percentile absolute amplitude)
+    abs_y = np.abs(y)
+    noise_floor = float(np.percentile(abs_y, 10))
+
+    # Estimated SNR
+    snr_est = 20 * np.log10(max(rms, 1e-12) / max(noise_floor, 1e-12))
+
+    # Zero-crossing rate (speech/noise indicator)
+    zcr = float(np.mean(librosa.feature.zero_crossing_rate(y)))
+
+    return {
+        "peak": peak,
+        "rms": rms,
+        "peak_db": peak_db,
+        "rms_db": rms_db,
+        "crest_factor_db": crest_factor,
+        "noise_floor": noise_floor,
+        "snr_db": snr_est,
+        "zero_crossing_rate": zcr
+    }