diff --git a/src/dsp/pipeline.py b/src/dsp/pipeline.py
index 57532b1..0798894 100644
--- a/src/dsp/pipeline.py
+++ b/src/dsp/pipeline.py
@@ -319,6 +319,16 @@ class AudioPipeline:
         # Smoothing factor: ~50ms time constant at 48kHz/256 block
         self._vu_alpha: float = np.exp(-BLOCK_SIZE / (0.05 * SAMPLE_RATE))
 
+        # ── Tuner / pitch detection state ────────────────────────────────
+        self._tuner_frequency: float = 0.0      # detected fundamental freq (Hz)
+        self._tuner_note: str = "--"            # closest note name
+        self._tuner_cents: float = 0.0          # cent deviation from closest note
+        self._tuner_string: int = -1            # string number (1-6) or -1 if not matched
+        self._tuner_confidence: float = 0.0     # 0.0 to 1.0
+        # Pitch detection buffer (keep last N samples for analysis)
+        self._pitch_buffer: np.ndarray = np.array([], dtype=np.float32)
+        self._pitch_buffer_max: int = 2048      # ~43ms at 48kHz
+
         logger.info("Audio pipeline initialized (block=%d, sr=%d)",
                      BLOCK_SIZE, SAMPLE_RATE)
 
@@ -370,6 +380,26 @@ class AudioPipeline:
                 Mono mode: shape (N,) — processed output.
                 4CM mode:  shape (2, N) — [send_out, return_out].
         """
+        # ── Tuner mode: mute output, keep input tracking for pitch detection ──
+        if self._tuner_enabled:
+            # Still track input level for tuner display
+            if audio_in.ndim == 1:
+                in_rms = np.sqrt(np.mean(audio_in ** 2) + _EPS)
+                self._input_level = (
+                    self._input_level * self._vu_alpha
+                    + in_rms * (1.0 - self._vu_alpha)
+                )
+            else:
+                ch0 = audio_in[0, :]
+                in_rms = np.sqrt(np.mean(ch0 ** 2) + _EPS)
+                self._input_level = (
+                    self._input_level * self._vu_alpha
+                    + in_rms * (1.0 - self._vu_alpha)
+                )
+            # Run pitch detection on input
+            self._detect_pitch(audio_in)
+            return np.zeros_like(audio_in)
+
         if self._bypassed:
             return audio_in * self._master_volume
 
@@ -378,6 +408,139 @@ class AudioPipeline:
         else:
             return self._process_mono(audio_in)
 
+    # ── Pitch detection for tuner ──────────────────────────────────────────────
+
+    # Standard tuning frequencies (E2, A2, D3, G3, B3, E4) — guitar strings
+    _STRING_FREQS = [82.41, 110.0, 146.83, 196.0, 246.94, 329.63]
+    _STRING_NAMES = ["E", "A", "D", "G", "B", "e"]
+
+    # Note names in chromatic order (C = 0)
+    _NOTE_NAMES = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
+
+    def _detect_pitch(self, audio_in: np.ndarray) -> None:
+        """Run pitch detection on the input buffer using autocorrelation.
+
+        Updates ``self._tuner_frequency``, ``self._tuner_note``,
+        ``self._tuner_cents``, and ``self._tuner_string``.
+
+        Args:
+            audio_in: Input audio block — mono (N,) or stereo (2, N).
+        """
+        # Extract mono channel
+        if audio_in.ndim == 2:
+            signal = audio_in[0, :].copy()
+        else:
+            signal = audio_in.copy()
+
+        # Append to rolling pitch buffer
+        self._pitch_buffer = np.concatenate([self._pitch_buffer, signal])
+        if len(self._pitch_buffer) > self._pitch_buffer_max:
+            self._pitch_buffer = self._pitch_buffer[-self._pitch_buffer_max:]
+
+        # Need enough signal for meaningful analysis
+        if len(self._pitch_buffer) < 512:
+            self._tuner_confidence = 0.0
+            return
+
+        # Simple autocorrelation pitch detection
+        buf = self._pitch_buffer
+        # Remove DC offset
+        buf = buf - np.mean(buf)
+
+        # Check if there's enough amplitude
+        rms = np.sqrt(np.mean(buf ** 2))
+        if rms < 0.002:  # Silence threshold
+            self._tuner_confidence = 0.0
+            self._tuner_frequency = 0.0
+            self._tuner_note = "--"
+            self._tuner_cents = 0.0
+            self._tuner_string = -1
+            return
+
+        # Autocorrelation: find the fundamental period
+        # Search lag range: 30 to 1024 samples (46.9Hz to 1600Hz at 48kHz)
+        min_lag = int(SAMPLE_RATE / 1600)  # ~30
+        max_lag = min(int(SAMPLE_RATE / 50), len(buf) // 2)  # ~960
+
+        if max_lag <= min_lag:
+            self._tuner_confidence = 0.0
+            return
+
+        corr = np.correlate(buf, buf, mode='full')
+        # Take only the second half (positive lags)
+        corr = corr[len(corr) // 2:]
+
+        # Normalize by energy at each lag (YIN-style)
+        energy = np.cumsum(buf ** 2)
+        energy = energy[max(1, min_lag):max_lag + len(buf) - len(corr) + min_lag + 1]
+        # Fallback: use simple autocorrelation
+        lag_slice = corr[min_lag:max_lag + 1]
+
+        # Find the first peak in the autocorrelation
+        diffs = np.diff(lag_slice)
+        # Look for zero crossings in diff (peaks: positive→negative)
+        peaks = []
+        for i in range(1, len(diffs)):
+            if diffs[i-1] > 0 and diffs[i] <= 0:
+                peaks.append((min_lag + i, lag_slice[i]))
+
+        if not peaks:
+            self._tuner_confidence = 0.0
+            return
+
+        # Pick the strongest peak
+        best_lag, best_val = max(peaks, key=lambda x: x[1])
+
+        # Confidence based on relative peak strength
+        noise_floor = np.mean(np.abs(corr[min_lag:max_lag + 1]))
+        confidence = best_val / (noise_floor + 1e-10)
+        # Parabolic interpolation for sub-sample accuracy
+        if best_lag > min_lag and best_lag < max_lag:
+            idx = best_lag - min_lag
+            if 0 < idx < len(lag_slice) - 1:
+                y0, y1, y2 = lag_slice[idx-1], lag_slice[idx], lag_slice[idx+1]
+                if y0 + y2 - 2 * y1 != 0:
+                    correction = (y0 - y2) / (2 * (y0 + y2 - 2 * y1))
+                    best_lag = best_lag + correction
+
+        # Fundamental frequency
+        freq = SAMPLE_RATE / best_lag if best_lag > 0 else 0
+
+        # Clip confidence to 0-1 range
+        self._tuner_confidence = min(1.0, max(0.0, confidence / 10.0))
+
+        if freq < 30 or freq > 1600:
+            self._tuner_confidence = 0.0
+            return
+
+        self._tuner_frequency = freq
+
+        # ── Convert frequency to note name ──
+        # A4 = 440Hz, MIDI note 69
+        midi_note = 12 * np.log2(freq / 440.0) + 69
+        midi_rounded = round(midi_note)
+        cents = int(100 * (midi_note - midi_rounded))
+
+        # Clamp to valid MIDI range
+        if midi_rounded < 0 or midi_rounded > 127:
+            self._tuner_note = "--"
+            self._tuner_confidence = 0.0
+            return
+
+        octave = (midi_rounded // 12) - 1
+        note_idx = midi_rounded % 12
+        note_name = self._NOTE_NAMES[note_idx]
+        self._tuner_note = f"{note_name}{octave}"
+        self._tuner_cents = cents
+
+        # ── Guess guitar string ──
+        self._tuner_string = -1
+        for si, sf in enumerate(self._STRING_FREQS):
+            # +/- 3 semitones from the string's fundamental
+            if abs(freq - sf) / sf < 0.2:
+                self._tuner_string = si + 1
+                break
+
     def _process_mono(self, audio_in: np.ndarray) -> np.ndarray:
         """Process a mono block through the full chain (all blocks)."""
         # Update input VU level (RMS with envelope smoothing)