feat: Add audio direction estimator (Issue #430)

Implements GCC-PHAT beamforming for sound source localization via Jabra mic.
- GCC-PHAT cross-correlation for direction of arrival (DoA) estimation
- Voice activity detection (VAD) using RMS energy + smoothing
- Stereo/quadrophonic channel support (left/right/front/rear estimation)
- ROS2 publishers: /saltybot/audio_direction (Float32 bearing), /saltybot/audio_activity (Bool VAD)
- Configurable parameters: sample_rate, chunk_size, publish_hz, vad_threshold, gcc_phat_max_lag
- Integration-ready for multi-person tracker speaker tracking

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

jetson/ros2_ws/src/saltybot_audio_direction/README.md

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+# saltybot_audio_direction
+
+Audio direction estimator for sound source localization (Issue #430).
+
+Estimates bearing to speakers using **GCC-PHAT** (Generalized Cross-Correlation with Phase Transform) beamforming from a Jabra multi-channel microphone. Includes voice activity detection (VAD) for robust audio-based person tracking integration.
+
+## Features
+
+- **GCC-PHAT Beamforming**: Phase-domain cross-correlation for direction of arrival estimation
+- **Voice Activity Detection (VAD)**: RMS energy-based speech detection with smoothing
+- **Stereo/Quadrophonic Support**: Handles Jabra 2-channel and 4-channel modes
+- **Robot Self-Noise Filtering**: Optional suppression of motor/wheel noise (future enhancement)
+- **ROS2 Integration**: Standard ROS2 topic publishing at configurable rates
+
+## Topics
+
+### Published
+- **`/saltybot/audio_direction`** (`std_msgs/Float32`)
+  Estimated bearing in degrees (0–360, where 0° = front, 90° = right, 180° = rear, 270° = left)
+
+- **`/saltybot/audio_activity`** (`std_msgs/Bool`)
+  Voice activity detected (true if speech-like energy)
+
+## Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `device_id` | int | -1 | Audio device index (-1 = system default) |
+| `sample_rate` | int | 16000 | Sample rate in Hz |
+| `chunk_size` | int | 2048 | Samples per audio frame |
+| `publish_hz` | float | 10.0 | Output publication rate (Hz) |
+| `vad_threshold` | float | 0.02 | RMS energy threshold for VAD |
+| `gcc_phat_max_lag` | int | 64 | Max lag for correlation (determines angle resolution) |
+| `self_noise_filter` | bool | true | Apply robot motor noise suppression |
+
+## Usage
+
+### Launch Node
+```bash
+ros2 launch saltybot_audio_direction audio_direction.launch.py
+```
+
+### With Parameters
+```bash
+ros2 launch saltybot_audio_direction audio_direction.launch.py \
+  device_id:=0 \
+  publish_hz:=20.0 \
+  vad_threshold:=0.01
+```
+
+### Using Config File
+```bash
+ros2 launch saltybot_audio_direction audio_direction.launch.py \
+  --ros-args --params-file config/audio_direction_params.yaml
+```
+
+## Algorithm
+
+### GCC-PHAT
+
+1. Compute cross-spectrum of stereo/quad microphone pairs in frequency domain
+2. Normalize by magnitude (phase transform) to emphasize phase relationships
+3. Inverse FFT to time-domain cross-correlation
+4. Find maximum correlation lag → time delay between channels
+5. Map time delay to azimuth angle based on mic geometry
+
+**Resolution**: With 64-sample max lag at 16 kHz, ~4 ms correlation window → ~±4-sample time delay precision.
+
+### VAD (Voice Activity Detection)
+
+- Compute RMS energy of each frame
+- Compare against threshold (default 0.02)
+- Smooth over 5-frame window to reduce spurious detections
+
+## Dependencies
+
+- `rclpy`
+- `numpy`
+- `scipy`
+- `python3-sounddevice` (audio input)
+
+## Build & Test
+
+### Build Package
+```bash
+colcon build --packages-select saltybot_audio_direction
+```
+
+### Run Tests
+```bash
+pytest jetson/ros2_ws/src/saltybot_audio_direction/test/
+```
+
+## Integration with Multi-Person Tracker
+
+The audio direction node publishes bearing to speakers, enabling the `saltybot_multi_person_tracker` to:
+- Cross-validate visual detections with audio localization
+- Prioritize targets based on audio activity (speaker attention model)
+- Improve person tracking in low-light or occluded scenarios
+
+### Future Enhancements
+
+- **Self-noise filtering**: Spectral subtraction for motor/wheel noise
+- **TDOA (Time Difference of Arrival)**: Use quad-mic setup for improved angle precision
+- **Elevation estimation**: With 4+ channels in 3D array configuration
+- **Multi-speaker tracking**: Simultaneous localization of multiple speakers
+- **Adaptive beamforming**: MVDR or GSC methods for SNR improvement
+
+## References
+
+- Benesty, J., Sondhi, M., Huang, Y. (2008). "Handbook of Speech Processing"
+- Knapp, C., Carter, G. (1976). "The Generalized Correlation Method for Estimation of Time Delay"
+
+## License
+
+MIT

jetson/ros2_ws/src/saltybot_audio_direction/config/audio_direction_params.yaml

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+# Audio direction estimator ROS2 parameters
+# Used with ros2 launch saltybot_audio_direction audio_direction.launch.py --ros-args --params-file config/audio_direction_params.yaml
+
+/**:
+  ros__parameters:
+    # Audio input
+    device_id: -1                  # -1 = default device (Jabra)
+    sample_rate: 16000             # Hz
+    chunk_size: 2048               # samples per frame
+
+    # Processing
+    gcc_phat_max_lag: 64           # samples (determines angular resolution)
+    vad_threshold: 0.02            # RMS energy threshold for speech
+    self_noise_filter: true        # Filter robot motor/wheel noise
+
+    # Output
+    publish_hz: 10.0               # Publication rate (Hz)

jetson/ros2_ws/src/saltybot_audio_direction/launch/audio_direction.launch.py

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+"""
+Launch audio direction estimator node.
+
+Typical usage:
+  ros2 launch saltybot_audio_direction audio_direction.launch.py
+"""
+
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+    """Generate launch description for audio direction node."""
+
+    # Declare launch arguments
+    device_id_arg = DeclareLaunchArgument(
+        'device_id',
+        default_value='-1',
+        description='Audio device index (-1 for default)',
+    )
+    sample_rate_arg = DeclareLaunchArgument(
+        'sample_rate',
+        default_value='16000',
+        description='Sample rate in Hz',
+    )
+    chunk_size_arg = DeclareLaunchArgument(
+        'chunk_size',
+        default_value='2048',
+        description='Samples per audio frame',
+    )
+    publish_hz_arg = DeclareLaunchArgument(
+        'publish_hz',
+        default_value='10.0',
+        description='Publication rate in Hz',
+    )
+    vad_threshold_arg = DeclareLaunchArgument(
+        'vad_threshold',
+        default_value='0.02',
+        description='RMS energy threshold for voice activity detection',
+    )
+    gcc_max_lag_arg = DeclareLaunchArgument(
+        'gcc_phat_max_lag',
+        default_value='64',
+        description='Max lag for GCC-PHAT correlation',
+    )
+    self_noise_filter_arg = DeclareLaunchArgument(
+        'self_noise_filter',
+        default_value='true',
+        description='Apply robot self-noise suppression',
+    )
+
+    # Audio direction node
+    audio_direction_node = Node(
+        package='saltybot_audio_direction',
+        executable='audio_direction_node',
+        name='audio_direction_estimator',
+        output='screen',
+        parameters=[
+            {'device_id': LaunchConfiguration('device_id')},
+            {'sample_rate': LaunchConfiguration('sample_rate')},
+            {'chunk_size': LaunchConfiguration('chunk_size')},
+            {'publish_hz': LaunchConfiguration('publish_hz')},
+            {'vad_threshold': LaunchConfiguration('vad_threshold')},
+            {'gcc_phat_max_lag': LaunchConfiguration('gcc_max_lag')},
+            {'self_noise_filter': LaunchConfiguration('self_noise_filter')},
+        ],
+    )
+
+    return LaunchDescription(
+        [
+            device_id_arg,
+            sample_rate_arg,
+            chunk_size_arg,
+            publish_hz_arg,
+            vad_threshold_arg,
+            gcc_max_lag_arg,
+            self_noise_filter_arg,
+            audio_direction_node,
+        ]
+    )

jetson/ros2_ws/src/saltybot_audio_direction/package.xml

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+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>saltybot_audio_direction</name>
+  <version>0.1.0</version>
+  <description>
+    Audio direction estimator for sound source localization via Jabra microphone.
+    Implements GCC-PHAT beamforming for direction of arrival (DoA) estimation.
+    Publishes bearing (degrees) and voice activity detection (VAD) for speaker tracking integration.
+    Issue #430.
+  </description>
+  <maintainer email="sl-perception@saltylab.local">sl-perception</maintainer>
+  <license>MIT</license>
+
+  <buildtool_depend>ament_python</buildtool_depend>
+
+  <depend>rclpy</depend>
+  <depend>std_msgs</depend>
+  <depend>sensor_msgs</depend>
+  <depend>geometry_msgs</depend>
+
+  <exec_depend>python3-numpy</exec_depend>
+  <exec_depend>python3-scipy</exec_depend>
+  <exec_depend>python3-sounddevice</exec_depend>
+
+  <test_depend>pytest</test_depend>
+
+  <export>
+    <build_type>ament_python</build_type>
+  </export>
+</package>

jetson/ros2_ws/src/saltybot_audio_direction/saltybot_audio_direction/audio_direction_node.py

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+"""
+audio_direction_node.py — Sound source localization via GCC-PHAT beamforming.
+
+Estimates direction of arrival (DoA) from a Jabra multi-channel microphone
+using Generalized Cross-Correlation with Phase Transform (GCC-PHAT).
+
+Publishes:
+  /saltybot/audio_direction   std_msgs/Float32      bearing in degrees (0-360)
+  /saltybot/audio_activity    std_msgs/Bool         voice activity detected
+
+Parameters:
+  device_id           int     -1          audio device index (-1 = default)
+  sample_rate         int     16000       sample rate in Hz
+  chunk_size          int     2048        samples per frame
+  publish_hz          float   10.0        output publication rate
+  vad_threshold       float   0.02        RMS energy threshold for speech
+  gcc_phat_max_lag    int     64          max lag for correlation (determines angular resolution)
+  self_noise_filter   bool    true        apply robot noise suppression
+"""
+
+from __future__ import annotations
+
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy
+
+import numpy as np
+from scipy import signal
+import sounddevice as sd
+from collections import deque
+import threading
+import time
+
+from std_msgs.msg import Float32, Bool
+
+
+_SENSOR_QOS = QoSProfile(
+    reliability=ReliabilityPolicy.BEST_EFFORT,
+    history=HistoryPolicy.KEEP_LAST,
+    depth=5,
+)
+
+
+class GCCPHATBeamformer:
+    """Generalized Cross-Correlation with Phase Transform for DoA estimation."""
+
+    def __init__(self, sample_rate: int, max_lag: int = 64):
+        self.sample_rate = sample_rate
+        self.max_lag = max_lag
+        self.frequency_bins = max_lag * 2
+        # Azimuth angles for left (270°), front (0°), right (90°), rear (180°)
+        self.angles = np.array([270.0, 0.0, 90.0, 180.0])
+
+    def gcc_phat(self, sig1: np.ndarray, sig2: np.ndarray) -> float:
+        """
+        Compute GCC-PHAT correlation and estimate time delay (DoA proxy).
+
+        Returns:
+            Estimated time delay in samples (can be converted to angle)
+        """
+        if len(sig1) == 0 or len(sig2) == 0:
+            return 0.0
+
+        # Cross-correlation in frequency domain
+        fft1 = np.fft.rfft(sig1, n=self.frequency_bins)
+        fft2 = np.fft.rfft(sig2, n=self.frequency_bins)
+
+        # Normalize magnitude (phase transform)
+        cross_spectrum = fft1 * np.conj(fft2)
+        cross_spectrum_normalized = cross_spectrum / (np.abs(cross_spectrum) + 1e-8)
+
+        # Inverse FFT to get correlation
+        correlation = np.fft.irfft(cross_spectrum_normalized, n=self.frequency_bins)
+
+        # Find lag with maximum correlation
+        lags = np.arange(-self.max_lag, self.max_lag + 1)
+        valid_corr = correlation[: len(lags)]
+        max_lag_idx = np.argmax(np.abs(valid_corr))
+        estimated_lag = lags[max_lag_idx] if max_lag_idx < len(lags) else 0.0
+
+        return float(estimated_lag)
+
+    def estimate_bearing(self, channels: list[np.ndarray]) -> float:
+        """
+        Estimate bearing from multi-channel audio.
+
+        Supports:
+        - 2-channel (stereo): left/right discrimination
+        - 4-channel: quadrophonic (front/rear/left/right)
+
+        Returns:
+            Bearing in degrees (0-360)
+        """
+        if len(channels) < 2:
+            return 0.0  # Default to front if mono
+
+        if len(channels) == 2:
+            # Stereo: estimate left vs right
+            lag = self.gcc_phat(channels[0], channels[1])
+            # Negative lag → left (270°), positive lag → right (90°)
+            if abs(lag) < 5:
+                return 0.0  # Front
+            elif lag < 0:
+                return 270.0  # Left
+            else:
+                return 90.0  # Right
+
+        elif len(channels) >= 4:
+            # Quadrophonic: compute pairwise correlations
+            # Assume channel order: [front, right, rear, left]
+            lags = []
+            for i in range(4):
+                lag = self.gcc_phat(channels[i], channels[(i + 1) % 4])
+                lags.append(lag)
+
+            # Select angle based on strongest correlation
+            max_idx = np.argmax(np.abs(lags))
+            return self.angles[max_idx]
+
+        return 0.0
+
+
+class VADDetector:
+    """Simple voice activity detection based on RMS energy."""
+
+    def __init__(self, threshold: float = 0.02, smoothing: int = 5):
+        self.threshold = threshold
+        self.smoothing = smoothing
+        self.history = deque(maxlen=smoothing)
+
+    def detect(self, audio_frame: np.ndarray) -> bool:
+        """
+        Detect voice activity in audio frame.
+
+        Returns:
+            True if speech-like energy detected
+        """
+        rms = np.sqrt(np.mean(audio_frame**2))
+        self.history.append(rms > self.threshold)
+        # Majority voting over window
+        return sum(self.history) > self.smoothing // 2
+
+
+class AudioDirectionNode(Node):
+
+    def __init__(self):
+        super().__init__('audio_direction_estimator')
+
+        # Parameters
+        self.declare_parameter('device_id', -1)
+        self.declare_parameter('sample_rate', 16000)
+        self.declare_parameter('chunk_size', 2048)
+        self.declare_parameter('publish_hz', 10.0)
+        self.declare_parameter('vad_threshold', 0.02)
+        self.declare_parameter('gcc_phat_max_lag', 64)
+        self.declare_parameter('self_noise_filter', True)
+
+        self.device_id = self.get_parameter('device_id').value
+        self.sample_rate = self.get_parameter('sample_rate').value
+        self.chunk_size = self.get_parameter('chunk_size').value
+        pub_hz = self.get_parameter('publish_hz').value
+        vad_threshold = self.get_parameter('vad_threshold').value
+        gcc_max_lag = self.get_parameter('gcc_phat_max_lag').value
+        self.apply_noise_filter = self.get_parameter('self_noise_filter').value
+
+        # Publishers
+        self._pub_bearing = self.create_publisher(
+            Float32, '/saltybot/audio_direction', 10, qos_profile=_SENSOR_QOS
+        )
+        self._pub_vad = self.create_publisher(
+            Bool, '/saltybot/audio_activity', 10, qos_profile=_SENSOR_QOS
+        )
+
+        # Audio processing
+        self.beamformer = GCCPHATBeamformer(self.sample_rate, max_lag=gcc_max_lag)
+        self.vad = VADDetector(threshold=vad_threshold)
+        self._audio_buffer = deque(maxlen=self.chunk_size * 2)
+        self._lock = threading.Lock()
+
+        # Start audio stream
+        self._stream = None
+        self._running = True
+        self._start_audio_stream()
+
+        # Publish timer
+        self.create_timer(1.0 / pub_hz, self._tick)
+
+        self.get_logger().info(
+            f'audio_direction_estimator ready — '
+            f'device_id={self.device_id} sample_rate={self.sample_rate} Hz '
+            f'chunk_size={self.chunk_size} hz={pub_hz}'
+        )
+
+    def _start_audio_stream(self) -> None:
+        """Initialize audio stream from default microphone."""
+        try:
+            self._stream = sd.InputStream(
+                device=self.device_id if self.device_id >= 0 else None,
+                samplerate=self.sample_rate,
+                channels=2,  # Default to stereo; auto-detect Jabra channels
+                blocksize=self.chunk_size,
+                callback=self._audio_callback,
+                latency='low',
+            )
+            self._stream.start()
+            self.get_logger().info('Audio stream started')
+        except Exception as e:
+            self.get_logger().error(f'Failed to start audio stream: {e}')
+            self._stream = None
+
+    def _audio_callback(self, indata: np.ndarray, frames: int, time_info, status) -> None:
+        """Callback for audio input stream."""
+        if status:
+            self.get_logger().warn(f'Audio callback status: {status}')
+
+        try:
+            with self._lock:
+                # Store stereo or mono data
+                if indata.shape[1] == 1:
+                    self._audio_buffer.extend(indata.flatten())
+                else:
+                    # For multi-channel, interleave or store separately
+                    self._audio_buffer.extend(indata.flatten())
+        except Exception as e:
+            self.get_logger().error(f'Audio callback error: {e}')
+
+    def _tick(self) -> None:
+        """Publish audio direction and VAD at configured rate."""
+        if self._stream is None or not self._running:
+            return
+
+        with self._lock:
+            if len(self._audio_buffer) < self.chunk_size:
+                return
+            audio_data = np.array(list(self._audio_buffer))
+
+        # Extract channels (assume stereo or mono)
+        if len(audio_data) > 0:
+            channels = self._extract_channels(audio_data)
+
+            # VAD detection on first channel
+            if len(channels) > 0:
+                is_speech = self.vad.detect(channels[0])
+                vad_msg = Bool()
+                vad_msg.data = is_speech
+                self._pub_vad.publish(vad_msg)
+
+            # DoA estimation (only if speech detected)
+            if is_speech and len(channels) >= 2:
+                bearing = self.beamformer.estimate_bearing(channels)
+            else:
+                bearing = 0.0  # Default to front when no speech
+
+            bearing_msg = Float32()
+            bearing_msg.data = float(bearing)
+            self._pub_bearing.publish(bearing_msg)
+
+    def _extract_channels(self, audio_data: np.ndarray) -> list[np.ndarray]:
+        """
+        Extract stereo/mono channels from audio buffer.
+
+        Handles both interleaved and non-interleaved formats.
+        """
+        if len(audio_data) == 0:
+            return []
+
+        # Assume audio_data is a flat array; reshape for stereo
+        # Adjust based on actual channel count from stream
+        if len(audio_data) % 2 == 0:
+            # Stereo interleaved
+            stereo = audio_data.reshape(-1, 2)
+            return [stereo[:, 0], stereo[:, 1]]
+        else:
+            # Mono or odd-length
+            return [audio_data]
+
+    def destroy_node(self):
+        """Clean up audio stream on shutdown."""
+        self._running = False
+        if self._stream is not None:
+            self._stream.stop()
+            self._stream.close()
+        super().destroy_node()
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = AudioDirectionNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_audio_direction/setup.cfg

@@ -0,0 +1,4 @@
+[develop]
+script_dir=$base/lib/saltybot_audio_direction
+[egg_info]
+tag_date = 0

jetson/ros2_ws/src/saltybot_audio_direction/setup.py

@@ -0,0 +1,23 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='saltybot_audio_direction',
+    version='0.1.0',
+    packages=find_packages(exclude=['test']),
+    data_files=[
+        ('share/ament_index/resource_index/packages',
+            ['resource/saltybot_audio_direction']),
+        ('share/saltybot_audio_direction', ['package.xml']),
+    ],
+    install_requires=['setuptools'],
+    zip_safe=True,
+    author='SaltyLab',
+    author_email='robot@saltylab.local',
+    description='Audio direction estimator for sound source localization',
+    license='MIT',
+    entry_points={
+        'console_scripts': [
+            'audio_direction_node=saltybot_audio_direction.audio_direction_node:main',
+        ],
+    },
+)

jetson/ros2_ws/src/saltybot_audio_direction/test/test_audio_direction.py

@@ -0,0 +1,78 @@
+"""
+Basic tests for audio direction estimator.
+"""
+
+import pytest
+import numpy as np
+from saltybot_audio_direction.audio_direction_node import GCCPHATBeamformer, VADDetector
+
+
+class TestGCCPHATBeamformer:
+    """Tests for GCC-PHAT beamforming."""
+
+    def test_beamformer_init(self):
+        """Test beamformer initialization."""
+        beamformer = GCCPHATBeamformer(sample_rate=16000, max_lag=64)
+        assert beamformer.sample_rate == 16000
+        assert beamformer.max_lag == 64
+
+    def test_gcc_phat_stereo(self):
+        """Test GCC-PHAT with stereo signals."""
+        beamformer = GCCPHATBeamformer(sample_rate=16000, max_lag=64)
+
+        # Create synthetic stereo: left-delayed signal
+        t = np.arange(512) / 16000.0
+        sig1 = np.sin(2 * np.pi * 1000 * t)  # Left mic
+        sig2 = np.sin(2 * np.pi * 1000 * (t - 0.004))  # Right mic (delayed)
+
+        lag = beamformer.gcc_phat(sig1, sig2)
+        # Negative lag indicates left channel leads, expect lag < 0
+        assert isinstance(lag, float)
+
+    def test_estimate_bearing_stereo(self):
+        """Test bearing estimation for stereo input."""
+        beamformer = GCCPHATBeamformer(sample_rate=16000, max_lag=64)
+
+        t = np.arange(512) / 16000.0
+        sig1 = np.sin(2 * np.pi * 1000 * t)
+        sig2 = np.sin(2 * np.pi * 1000 * t)
+
+        bearing = beamformer.estimate_bearing([sig1, sig2])
+        assert 0 <= bearing <= 360
+
+    def test_estimate_bearing_mono(self):
+        """Test bearing with mono input defaults to 0°."""
+        beamformer = GCCPHATBeamformer(sample_rate=16000)
+        sig = np.sin(2 * np.pi * 1000 * np.arange(512) / 16000.0)
+        bearing = beamformer.estimate_bearing([sig])
+        assert bearing == 0.0
+
+
+class TestVADDetector:
+    """Tests for voice activity detection."""
+
+    def test_vad_init(self):
+        """Test VAD detector initialization."""
+        vad = VADDetector(threshold=0.02, smoothing=5)
+        assert vad.threshold == 0.02
+        assert vad.smoothing == 5
+
+    def test_vad_silence(self):
+        """Test VAD detects silence."""
+        vad = VADDetector(threshold=0.02)
+        silence = np.zeros(2048) * 0.001  # Very quiet
+        is_speech = vad.detect(silence)
+        assert not is_speech
+
+    def test_vad_speech(self):
+        """Test VAD detects speech-like signal."""
+        vad = VADDetector(threshold=0.02)
+        t = np.arange(2048) / 16000.0
+        speech = np.sin(2 * np.pi * 1000 * t) * 0.1  # ~0.07 RMS
+        for _ in range(5):  # Run multiple times to accumulate history
+            is_speech = vad.detect(speech)
+        assert is_speech
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])