Postprod improvements: denoise, phone EQ, ad muting, ducking, voice mappings

- Add host mic noise reduction (afftdn + anlmdn)
- Add phone EQ bandpass on caller stem
- Mute music during ads with 2s lookahead/tail
- Increase ducking release to 3s to reduce pumping
- Add Inworld voice mappings for all regular callers
- Recording toggle endpoint, stem sync fixes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/main.py

@@ -2377,8 +2377,8 @@ async def set_on_air(state: dict):
                 def _run_postprod():
                     try:
                         result = subprocess.run(
-                            [python, "postprod.py", str(stems_dir), "-o", str(output_file)],
+                            [python, "postprod.py", str(stems_dir), "-o", "episode.mp3"],
-                            capture_output=True, text=True, timeout=300,
+                            capture_output=True, text=True, timeout=600,
                         )
                         if result.returncode == 0:
                             add_log(f"Post-production complete -> {output_file}")
@@ -3927,44 +3927,37 @@ async def server_status():
 
 # --- Stem Recording ---
 
-@app.post("/api/recording/start")
+@app.post("/api/recording/toggle")
-async def start_stem_recording():
+async def toggle_stem_recording():
-    if audio_service.stem_recorder is not None:
+    """Toggle recording on/off. Also toggles on-air state."""
-        raise HTTPException(400, "Recording already in progress")
-    from datetime import datetime
-    dir_name = datetime.now().strftime("%Y-%m-%d_%H%M%S")
-    recordings_dir = Path("recordings") / dir_name
-    import sounddevice as sd
-    device_info = sd.query_devices(audio_service.output_device) if audio_service.output_device is not None else None
-    sr = int(device_info["default_samplerate"]) if device_info else 48000
-    recorder = StemRecorder(recordings_dir, sample_rate=sr)
-    recorder.start()
-    audio_service.stem_recorder = recorder
-    audio_service.start_stem_mic()
-    add_log(f"Stem recording started -> {recordings_dir}")
-    # Auto go on-air
     global _show_on_air
-    if not _show_on_air:
-        _show_on_air = True
-        _start_host_audio_sender()
-        audio_service.start_host_stream(_host_audio_sync_callback)
-        threading.Thread(target=_update_on_air_cdn, args=(True,), daemon=True).start()
-        add_log("Show auto-set to ON AIR")
-    return {"status": "recording", "dir": str(recordings_dir), "on_air": _show_on_air}
-
-
-@app.post("/api/recording/stop")
-async def stop_stem_recording():
     if audio_service.stem_recorder is None:
-        raise HTTPException(400, "No recording in progress")
+        # START recording
+        from datetime import datetime
+        dir_name = datetime.now().strftime("%Y-%m-%d_%H%M%S")
+        recordings_dir = Path("recordings") / dir_name
+        import sounddevice as sd
+        device_info = sd.query_devices(audio_service.output_device) if audio_service.output_device is not None else None
+        sr = int(device_info["default_samplerate"]) if device_info else 48000
+        recorder = StemRecorder(recordings_dir, sample_rate=sr)
+        recorder.start()
+        audio_service.stem_recorder = recorder
+        audio_service.start_stem_mic()
+        add_log(f"Stem recording started -> {recordings_dir}")
+        if not _show_on_air:
+            _show_on_air = True
+            _start_host_audio_sender()
+            audio_service.start_host_stream(_host_audio_sync_callback)
+            threading.Thread(target=_update_on_air_cdn, args=(True,), daemon=True).start()
+            add_log("Show auto-set to ON AIR")
+        return {"on_air": _show_on_air, "recording": True}
+    # STOP recording
     audio_service.stop_stem_mic()
     stems_dir = audio_service.stem_recorder.output_dir
     paths = audio_service.stem_recorder.stop()
     audio_service.stem_recorder = None
     add_log(f"Stem recording stopped. Running post-production...")
 
-    # Auto go off-air
-    global _show_on_air
     if _show_on_air:
         _show_on_air = False
         audio_service.stop_host_stream()
@@ -3978,8 +3971,8 @@ async def stop_stem_recording():
     def _run_postprod():
         try:
             result = subprocess.run(
-                [python, "postprod.py", str(stems_dir), "-o", str(output_file)],
+                [python, "postprod.py", str(stems_dir), "-o", "episode.mp3"],
-                capture_output=True, text=True, timeout=300,
+                capture_output=True, text=True, timeout=600,
             )
             if result.returncode == 0:
                 add_log(f"Post-production complete -> {output_file}")
@@ -3989,7 +3982,7 @@ async def stop_stem_recording():
             add_log(f"Post-production error: {e}")
 
     threading.Thread(target=_run_postprod, daemon=True).start()
-    return {"status": "stopped", "stems": paths, "processing": str(output_file), "on_air": _show_on_air}
+    return {"on_air": _show_on_air, "recording": False}
 
 
 @app.post("/api/recording/process")

backend/services/audio.py

@@ -361,10 +361,6 @@ class AudioService:
             # Apply fade to prevent clicks
             audio = self._apply_fade(audio, device_sr)
 
-            # Stem recording: caller TTS
-            if self.stem_recorder:
-                self.stem_recorder.write_sporadic("caller", audio.copy(), device_sr)
-
             # Create multi-channel output with audio only on target channel
             multi_ch = np.zeros((len(audio), num_channels), dtype=np.float32)
             multi_ch[:, channel_idx] = audio
@@ -384,6 +380,9 @@ class AudioService:
                 while pos < len(multi_ch) and not self._caller_stop_event.is_set():
                     end = min(pos + chunk_size, len(multi_ch))
                     stream.write(multi_ch[pos:end])
+                    # Record each chunk as it plays so hangups cut the stem too
+                    if self.stem_recorder:
+                        self.stem_recorder.write_sporadic("caller", audio[pos:end].copy(), device_sr)
                     pos = end
 
             if self._caller_stop_event.is_set():
@@ -752,7 +751,7 @@ class AudioService:
                 mono_out = (old_samples * fade_out + new_samples * fade_in) * self._music_volume
                 outdata[:, channel_idx] = mono_out
                 if self.stem_recorder:
-                    self.stem_recorder.write("music", mono_out.copy(), device_sr)
+                    self.stem_recorder.write_sporadic("music", mono_out.copy(), device_sr)
                 self._crossfade_progress = end_progress
 
                 if self._crossfade_progress >= 1.0:
@@ -763,7 +762,7 @@ class AudioService:
                 mono_out = new_samples * self._music_volume
                 outdata[:, channel_idx] = mono_out
                 if self.stem_recorder:
-                    self.stem_recorder.write("music", mono_out.copy(), device_sr)
+                    self.stem_recorder.write_sporadic("music", mono_out.copy(), device_sr)
 
         try:
             self._music_stream = sd.OutputStream(
@@ -873,7 +872,7 @@ class AudioService:
                 chunk = self._ad_resampled[self._ad_position:self._ad_position + frames]
                 outdata[:, channel_idx] = chunk
                 if self.stem_recorder:
-                    self.stem_recorder.write("ads", chunk.copy(), device_sr)
+                    self.stem_recorder.write_sporadic("ads", chunk.copy(), device_sr)
                 self._ad_position += frames
             else:
                 if remaining > 0:

backend/services/tts.py

@@ -86,18 +86,28 @@ DEFAULT_VITS_SPEAKER = "p225"
 # Dennis, Dominus, Edward, Elizabeth, Hades, Hana, Julia, Luna, Mark, Olivia,
 # Pixie, Priya, Ronald, Sarah, Shaun, Theodore, Timothy, Wendy
 INWORLD_VOICES = {
-    # Male voices - each caller gets a unique voice matching their personality
+    # Original voice IDs
     "VR6AewLTigWG4xSOukaG": "Edward",    # Tony - fast-talking, emphatic, streetwise
     "TxGEqnHWrfWFTfGW9XjX": "Shaun",     # Rick - friendly, dynamic, conversational
     "pNInz6obpgDQGcFmaJgB": "Alex",      # Dennis - energetic, expressive, mildly nasal
     "ODq5zmih8GrVes37Dizd": "Craig",     # Earl - older British, refined, articulate
-    "IKne3meq5aSn9XLyUdCD": "Timothy",   # Marcus - lively, upbeat American
+    "IKne3meq5aSn9XLyUdCD": "Timothy",   # Marcus/Jerome - lively, upbeat American
-    # Female voices - each caller gets a unique voice matching their personality
     "jBpfuIE2acCO8z3wKNLl": "Hana",      # Jasmine - bright, expressive young female
     "EXAVITQu4vr4xnSDxMaL": "Ashley",    # Megan - warm, natural female
     "21m00Tcm4TlvDq8ikWAM": "Wendy",     # Tanya - posh, middle-aged British
     "XB0fDUnXU5powFXDhCwa": "Sarah",     # Carla - fast-talking, questioning tone
-    "pFZP5JQG7iQjIQuC4Bku": "Deborah",   # Brenda - gentle, elegant
+    "pFZP5JQG7iQjIQuC4Bku": "Deborah",   # Brenda (original) - gentle, elegant
+    # Regular caller voice IDs (backfilled)
+    "onwK4e9ZLuTAKqWW03F9": "Ronald",    # Bobby - repo man
+    "FGY2WhTYpPnrIDTdsKH5": "Julia",     # Carla (regular) - Jersey mom
+    "CwhRBWXzGAHq8TQ4Fs17": "Mark",      # Leon - male caller
+    "SOYHLrjzK2X1ezoPC6cr": "Carter",    # Carl - male caller
+    "N2lVS1w4EtoT3dr4eOWO": "Clive",     # Reggie - male caller
+    "hpp4J3VqNfWAUOO0d1Us": "Olivia",    # Brenda (regular) - ambulance driver
+    "nPczCjzI2devNBz1zQrb": "Theodore",  # Keith - male caller
+    "JBFqnCBsd6RMkjVDRZzb": "Blake",     # Andre - male caller
+    "TX3LPaxmHKxFdv7VOQHJ": "Dennis",    # Rick (regular) - male caller
+    "cgSgspJ2msm6clMCkdW9": "Priya",     # Megan (regular) - female caller
 }
 DEFAULT_INWORLD_VOICE = "Dennis"
 

data/regulars.json

@@ -60,9 +60,13 @@
         {
           "summary": "Jerome, a police officer in Texas, called from a DQ parking lot worried about AI writing police reports after his son sent him an article suggesting it might replace him. Through the conversation, he moved from fear about accountability and accuracy in criminal cases to acknowledging that AI handling routine paperwork (like cattle complaints) could free him up to do more meaningful police work in his understaffed county, though he remains uncertain about where this technology will lead.",
           "timestamp": 1770692087.560522
+        },
+        {
+          "summary": "The caller described a turbulent couple of weeks, mentioning an issue with AI writing police reports, which he suggested was just the beginning of a larger problem. He seemed concerned about the developments and wanted to discuss the topic further with the host.",
+          "timestamp": 1770892192.893108
         }
       ],
-      "last_call": 1770692087.560523,
+      "last_call": 1770892192.89311,
       "created_at": 1770692087.560523,
       "voice": "IKne3meq5aSn9XLyUdCD"
     },

frontend/js/app.js

@@ -101,17 +101,10 @@ function initEventListeners() {
     if (recBtn) {
         recBtn.addEventListener('click', async () => {
             try {
-                if (!stemRecording) {
+                const res = await safeFetch('/api/recording/toggle', { method: 'POST' });
-                    const res = await safeFetch('/api/recording/start', { method: 'POST' });
+                updateRecBtn(res.recording);
-                    updateRecBtn(true);
+                if (onAirBtn) updateOnAirBtn(onAirBtn, res.on_air);
-                    if (onAirBtn) updateOnAirBtn(onAirBtn, res.on_air);
+                log(res.recording ? 'Recording started + ON AIR' : 'Recording stopped + OFF AIR');
-                    log('Recording started + ON AIR: ' + res.dir);
-                } else {
-                    const res = await safeFetch('/api/recording/stop', { method: 'POST' });
-                    updateRecBtn(false);
-                    if (onAirBtn) updateOnAirBtn(onAirBtn, res.on_air);
-                    log('Recording stopped + OFF AIR');
-                }
             } catch (err) {
                 log('Recording error: ' + err.message);
             }

postprod.py

@@ -61,23 +61,30 @@ def compute_rms(audio: np.ndarray, window_samples: int) -> np.ndarray:
 
 
 def remove_gaps(stems: dict[str, np.ndarray], sr: int,
-                threshold_s: float = 1.5, crossfade_ms: float = 30) -> dict[str, np.ndarray]:
+                threshold_s: float = 2.0, max_gap_s: float = 8.0,
+                crossfade_ms: float = 30, pad_s: float = 0.5) -> dict[str, np.ndarray]:
     window_ms = 50
     window_samples = int(sr * window_ms / 1000)
     crossfade_samples = int(sr * crossfade_ms / 1000)
 
-    dialog = stems["host"] + stems["caller"]
+    # Detect gaps in everything except music (which always plays).
-    rms = compute_rms(dialog, window_samples)
+    # This catches TTS latency gaps while protecting ad breaks and SFX transitions.
+    content = stems["host"] + stems["caller"] + stems["sfx"] + stems["ads"]
+    rms = compute_rms(content, window_samples)
 
-    # Threshold: -60dB or adaptive based on mean RMS
+    # Threshold: percentile-based to sit above the mic noise floor
-    mean_rms = np.mean(rms[rms > 0]) if np.any(rms > 0) else 1e-4
+    nonzero_rms = rms[rms > 0]
-    silence_thresh = min(mean_rms * 0.05, 0.001)
+    if len(nonzero_rms) == 0:
+        print("  No audio detected")
+        return stems
+    noise_floor = np.percentile(nonzero_rms, 20)
+    silence_thresh = noise_floor * 3
 
-    # Find silent regions
     is_silent = rms < silence_thresh
     min_silent_windows = int(threshold_s / (window_ms / 1000))
+    max_silent_windows = int(max_gap_s / (window_ms / 1000))
 
-    # Build list of regions to cut (in samples)
+    # Only cut gaps between 1.5-8s — targets TTS latency, not long breaks
     cuts = []
     i = 0
     while i < len(is_silent):
@@ -86,10 +93,11 @@ def remove_gaps(stems: dict[str, np.ndarray], sr: int,
             while i < len(is_silent) and is_silent[i]:
                 i += 1
             length = i - start
-            if length >= min_silent_windows:
+            if min_silent_windows <= length <= max_silent_windows:
-                # Keep a small buffer at edges
+                # Leave pad_s of silence so the edit sounds natural
-                cut_start = (start + 1) * window_samples
+                pad_samples = int(pad_s * sr)
-                cut_end = (i - 1) * window_samples
+                cut_start = (start + 1) * window_samples + pad_samples
+                cut_end = (i - 1) * window_samples - pad_samples
                 if cut_end > cut_start + crossfade_samples * 2:
                     cuts.append((cut_start, cut_end))
         else:
@@ -102,18 +110,18 @@ def remove_gaps(stems: dict[str, np.ndarray], sr: int,
     total_cut = sum(end - start for start, end in cuts) / sr
     print(f"  Removing {len(cuts)} gaps ({total_cut:.1f}s total)")
 
-    # Apply cuts to dialog stems (host, caller, sfx, ads) — not music
+    # Cut dialog/sfx/ads at gap points. Leave music uncut — just trim to fit.
-    cut_stems = ["host", "caller", "sfx", "ads"]
     result = {}
 
-    for name in cut_stems:
+    for name in STEM_NAMES:
+        if name == "music":
+            continue  # handled below
         audio = stems[name]
         pieces = []
         prev_end = 0
         for cut_start, cut_end in cuts:
             if prev_end < cut_start:
                 piece = audio[prev_end:cut_start].copy()
-                # Apply crossfade at join point
                 if pieces and len(piece) > crossfade_samples:
                     fade_in = np.linspace(0, 1, crossfade_samples, dtype=np.float32)
                     piece[:crossfade_samples] *= fade_in
@@ -135,18 +143,49 @@ def remove_gaps(stems: dict[str, np.ndarray], sr: int,
 
         result[name] = np.concatenate(pieces) if pieces else np.array([], dtype=np.float32)
 
-    # Trim music to match new duration, with fade-out at end
+    # Music: leave uncut, just trim to match new duration with fade-out
     new_len = len(result["host"])
-    music = stems["music"][:new_len].copy() if len(stems["music"]) >= new_len else np.pad(stems["music"], (0, max(0, new_len - len(stems["music"]))))
+    music = stems["music"]
-    fade_samples = int(sr * 2)  # 2s fade out
+    if len(music) >= new_len:
+        music = music[:new_len].copy()
+    else:
+        music = np.pad(music, (0, new_len - len(music)))
+    fade_samples = int(sr * 3)
     if len(music) > fade_samples:
-        fade_out = np.linspace(1, 0, fade_samples, dtype=np.float32)
+        music[-fade_samples:] *= np.linspace(1, 0, fade_samples, dtype=np.float32)
-        music[-fade_samples:] *= fade_out
     result["music"] = music
 
     return result
 
 
+def denoise(audio: np.ndarray, sr: int, tmp_dir: Path) -> np.ndarray:
+    """High-quality noise reduction using ffmpeg afftdn (adaptive Wiener filter)."""
+    in_path = tmp_dir / "host_pre_denoise.wav"
+    out_path = tmp_dir / "host_post_denoise.wav"
+    sf.write(str(in_path), audio, sr)
+
+    # afftdn: adaptive FFT denoiser with Wiener filter
+    #   nt=w  - Wiener filter (best quality)
+    #   om=o  - output cleaned signal
+    #   nr=10 - noise reduction in dB (10 = moderate, preserves voice naturalness)
+    #   nf=-30 - noise floor estimate in dB
+    # anlmdn: non-local means denoiser for residual broadband noise
+    #   s=4   - patch size
+    #   p=0.002 - strength (gentle to avoid artifacts)
+    af = (
+        "afftdn=nt=w:om=o:nr=12:nf=-30,"
+        "anlmdn=s=4:p=0.002"
+    )
+    cmd = ["ffmpeg", "-y", "-i", str(in_path), "-af", af, str(out_path)]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        print(f"  WARNING: denoise failed: {result.stderr[:200]}")
+        return audio
+
+    denoised, _ = sf.read(str(out_path), dtype="float32")
+    return denoised
+
+
 def compress_voice(audio: np.ndarray, sr: int, tmp_dir: Path,
                    stem_name: str) -> np.ndarray:
     in_path = tmp_dir / f"{stem_name}_pre_comp.wav"
@@ -156,7 +195,7 @@ def compress_voice(audio: np.ndarray, sr: int, tmp_dir: Path,
 
     cmd = [
         "ffmpeg", "-y", "-i", str(in_path),
-        "-af", "acompressor=threshold=-24dB:ratio=3:attack=5:release=100:makeup=6dB",
+        "-af", "acompressor=threshold=-24dB:ratio=2.5:attack=10:release=800:makeup=6dB",
         str(out_path),
     ]
     result = subprocess.run(cmd, capture_output=True, text=True)
@@ -168,9 +207,32 @@ def compress_voice(audio: np.ndarray, sr: int, tmp_dir: Path,
     return compressed
 
 
+def phone_eq(audio: np.ndarray, sr: int, tmp_dir: Path) -> np.ndarray:
+    """Apply telephone EQ to make caller sound like a phone call."""
+    in_path = tmp_dir / "caller_pre_phone.wav"
+    out_path = tmp_dir / "caller_post_phone.wav"
+    sf.write(str(in_path), audio, sr)
+
+    # Bandpass 300-3400Hz (telephone bandwidth) + slight mid boost for presence
+    af = (
+        "highpass=f=300:poles=2,"
+        "lowpass=f=3400:poles=2,"
+        "equalizer=f=1000:t=q:w=0.8:g=4"
+    )
+    cmd = ["ffmpeg", "-y", "-i", str(in_path), "-af", af, str(out_path)]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        print(f"  WARNING: phone EQ failed: {result.stderr[:200]}")
+        return audio
+
+    filtered, _ = sf.read(str(out_path), dtype="float32")
+    return filtered
+
+
 def apply_ducking(music: np.ndarray, dialog: np.ndarray, sr: int,
-                  duck_db: float = -12, attack_ms: float = 200,
+                  duck_db: float = -20, attack_ms: float = 200,
-                  release_ms: float = 500) -> np.ndarray:
+                  release_ms: float = 3000,
+                  mute_signal: np.ndarray | None = None) -> np.ndarray:
     window_ms = 50
     window_samples = int(sr * window_ms / 1000)
     rms = compute_rms(dialog, window_samples)
@@ -184,6 +246,22 @@ def apply_ducking(music: np.ndarray, dialog: np.ndarray, sr: int,
     is_speech = rms > speech_thresh
     target_gain = np.where(is_speech, duck_gain, 1.0).astype(np.float32)
 
+    # Mute music completely during ads with lookahead and tail
+    if mute_signal is not None:
+        mute_rms = compute_rms(mute_signal, window_samples)
+        mute_thresh = np.mean(mute_rms[mute_rms > 0]) * 0.1 if np.any(mute_rms > 0) else 1e-4
+        is_ads = mute_rms > mute_thresh
+        # Expand ad regions: 2s before (fade out music before ad) and 2s after (don't resume immediately)
+        lookahead_windows = int(2000 / window_ms)
+        tail_windows = int(2000 / window_ms)
+        expanded_ads = is_ads.copy()
+        for i in range(len(is_ads)):
+            if is_ads[i]:
+                start = max(0, i - lookahead_windows)
+                end = min(len(expanded_ads), i + tail_windows + 1)
+                expanded_ads[start:end] = True
+        target_gain[expanded_ads] = 0.0
+
     # Smooth the envelope
     attack_windows = max(1, int(attack_ms / window_ms))
     release_windows = max(1, int(release_ms / window_ms))
@@ -206,10 +284,30 @@ def apply_ducking(music: np.ndarray, dialog: np.ndarray, sr: int,
     return music * gain_samples
 
 
+def match_voice_levels(stems: dict[str, np.ndarray], target_rms: float = 0.1) -> dict[str, np.ndarray]:
+    """Normalize host, caller, and ads stems to the same RMS level."""
+    for name in ["host", "caller", "ads"]:
+        audio = stems[name]
+        # Only measure non-silent portions
+        active = audio[np.abs(audio) > 0.001]
+        if len(active) == 0:
+            continue
+        current_rms = np.sqrt(np.mean(active ** 2))
+        if current_rms < 1e-6:
+            continue
+        gain = target_rms / current_rms
+        # Clamp gain to avoid extreme boosts on very quiet stems
+        gain = min(gain, 10.0)
+        stems[name] = np.clip(audio * gain, -1.0, 1.0).astype(np.float32)
+        db_change = 20 * np.log10(gain) if gain > 0 else 0
+        print(f"  {name}: RMS {current_rms:.4f} -> {target_rms:.4f} ({db_change:+.1f}dB)")
+    return stems
+
+
 def mix_stems(stems: dict[str, np.ndarray],
               levels: dict[str, float] | None = None) -> np.ndarray:
     if levels is None:
-        levels = {"host": 0, "caller": 0, "music": -6, "sfx": -3, "ads": 0}
+        levels = {"host": 0, "caller": 0, "music": -6, "sfx": -6, "ads": 0}
 
     gains = {name: 10 ** (db / 20) for name, db in levels.items()}
 
@@ -282,8 +380,8 @@ def main():
     parser = argparse.ArgumentParser(description="Post-production for AI podcast stems")
     parser.add_argument("stems_dir", type=Path, help="Directory containing stem WAV files")
     parser.add_argument("-o", "--output", type=str, default="episode.mp3", help="Output filename")
-    parser.add_argument("--gap-threshold", type=float, default=1.5, help="Min silence to cut (seconds)")
+    parser.add_argument("--gap-threshold", type=float, default=2.0, help="Min silence to cut (seconds)")
-    parser.add_argument("--duck-amount", type=float, default=-12, help="Music duck in dB")
+    parser.add_argument("--duck-amount", type=float, default=-20, help="Music duck in dB")
     parser.add_argument("--target-lufs", type=float, default=-16, help="Target loudness (LUFS)")
     parser.add_argument("--bitrate", type=str, default="128k", help="MP3 bitrate")
     parser.add_argument("--no-gap-removal", action="store_true", help="Skip gap removal")
@@ -313,18 +411,27 @@ def main():
         return
 
     # Step 1: Load
-    print("\n[1/6] Loading stems...")
+    print("\n[1/9] Loading stems...")
     stems, sr = load_stems(stems_dir)
 
     # Step 2: Gap removal
-    print("\n[2/6] Gap removal...")
+    print("\n[2/9] Gap removal...")
     if not args.no_gap_removal:
         stems = remove_gaps(stems, sr, threshold_s=args.gap_threshold)
     else:
         print("  Skipped")
 
-    # Step 3: Voice compression
+    # Step 3: Host mic noise reduction
-    print("\n[3/6] Voice compression...")
+    print("\n[3/9] Host mic noise reduction...")
+    if np.any(stems["host"] != 0):
+        with tempfile.TemporaryDirectory() as tmp:
+            stems["host"] = denoise(stems["host"], sr, Path(tmp))
+            print("  Applied")
+    else:
+        print("  No host audio")
+
+    # Step 4: Voice compression
+    print("\n[4/9] Voice compression...")
     if not args.no_compression:
         with tempfile.TemporaryDirectory() as tmp:
             tmp_dir = Path(tmp)
@@ -335,25 +442,39 @@ def main():
     else:
         print("  Skipped")
 
-    # Step 4: Music ducking
+    # Step 5: Phone EQ on caller
-    print("\n[4/6] Music ducking...")
+    print("\n[5/9] Phone EQ on caller...")
+    if np.any(stems["caller"] != 0):
+        with tempfile.TemporaryDirectory() as tmp:
+            stems["caller"] = phone_eq(stems["caller"], sr, Path(tmp))
+            print("  Applied")
+    else:
+        print("  No caller audio")
+
+    # Step 6: Match voice levels
+    print("\n[6/9] Matching voice levels...")
+    stems = match_voice_levels(stems)
+
+    # Step 7: Music ducking
+    print("\n[7/9] Music ducking...")
     if not args.no_ducking:
         dialog = stems["host"] + stems["caller"]
         if np.any(dialog != 0) and np.any(stems["music"] != 0):
-            stems["music"] = apply_ducking(stems["music"], dialog, sr, duck_db=args.duck_amount)
+            stems["music"] = apply_ducking(stems["music"], dialog, sr, duck_db=args.duck_amount,
+                                           mute_signal=stems["ads"])
             print("  Applied")
         else:
             print("  No dialog or music to duck")
     else:
         print("  Skipped")
 
-    # Step 5: Mix
+    # Step 8: Mix
-    print("\n[5/6] Mixing...")
+    print("\n[8/9] Mixing...")
     stereo = mix_stems(stems)
     print(f"  Mixed to stereo: {len(stereo)} samples ({len(stereo)/sr:.1f}s)")
 
-    # Step 6: Normalize + export
+    # Step 9: Normalize + export
-    print("\n[6/6] Loudness normalization + export...")
+    print("\n[9/9] Loudness normalization + export...")
     with tempfile.TemporaryDirectory() as tmp:
         normalize_and_export(stereo, sr, output_path,
                              target_lufs=args.target_lufs,