Jose
f500937067
- Refactor the audio processing and visualization tasks into separate cores, improve CPU usage monitoring, optimize memory usage, manage inter-core communication, and enhance network functionality. - This code snippet provides a basic implementation of a piano note detection system using an Arduino. The system includes a setup phase where calibration and initialization are performed, along with serial communication for user interaction. The main loop is empty, as all the work is handled by separate tasks created on different cores. The `setup()` function sets up the serial connection, initializes the piano note detector, and creates two separate tasks: `audioProcessingTask` and `visualizationTask`. These tasks handle the audio processing and visualization of the detected notes, respectively. The main loop runs in a paused state to allow for task execution on different cores. The audio processing task (`audioProcessingTask`) reads analog signals from an I2S microphone (C2-C6), processes them using Fourier Transform, and detects note frequencies. It then updates a spectrum visualization and sends the results over the serial interface to the host PC for further analysis. The visualization task (`visualizationTask`) receives the processed data from the audio processing task, visualizes the spectrum, and sends updates over the serial interface. The main loop in the `loop()` function is empty, as all the work is handled by these tasks.
318 lines
7.8 KiB
Markdown
318 lines
7.8 KiB
Markdown
# ESP32 Piano Note Detection System
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A real-time piano note detection system implemented on ESP32 using I2S microphone input. This system can detect musical notes from C2 to C6 with adjustable sensitivity and visualization options.
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## Features
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- Real-time audio processing using I2S microphone
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- FFT-based frequency analysis
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- Note detection from C2 (65.41 Hz) to C6 (1046.50 Hz)
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- Dynamic threshold calibration
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- Multiple note detection (up to 7 simultaneous notes)
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- Harmonic filtering
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- Real-time spectrum visualization
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- Note timing and duration tracking
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- Interactive Serial commands for system tuning
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## Hardware Requirements
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- ESP32 development board
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- I2S MEMS microphone (e.g., INMP441, SPH0645)
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- USB connection for Serial monitoring
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## Pin Configuration
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The system uses the following I2S pins by default (configurable in Config.h):
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- SCK (Serial Clock): GPIO 8
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- WS/LRC (Word Select/Left-Right Clock): GPIO 9
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- SD (Serial Data): GPIO 10
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## Getting Started
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1. Connect the I2S microphone to the ESP32 according to the pin configuration
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2. Build and flash the project to your ESP32
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3. Open a Serial monitor at 115200 baud
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4. Follow the calibration process on first run
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## Serial Commands
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The system can be controlled via Serial commands:
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- `h` - Display help menu
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- `c` - Start calibration process
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- `+` - Increase sensitivity (threshold up)
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- `-` - Decrease sensitivity (threshold down)
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- `s` - Toggle spectrum visualization
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## Configuration Options
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All system parameters can be adjusted in `Config.h`:
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### Audio Processing
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- `SAMPLE_RATE`: 8000 Hz (good for frequencies up to 4kHz)
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- `BITS_PER_SAMPLE`: 16-bit resolution
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- `SAMPLE_BUFFER_SIZE`: 1024 samples
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- `FFT_SIZE`: 1024 points
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### Note Detection
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- `NOTE_FREQ_C2`: 65.41 Hz (lowest detectable note)
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- `NOTE_FREQ_C6`: 1046.50 Hz (highest detectable note)
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- `FREQUENCY_TOLERANCE`: 3.0 Hz
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- `MAX_SIMULTANEOUS_NOTES`: 7
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- `MIN_NOTE_DURATION_MS`: 50ms
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- `NOTE_RELEASE_TIME_MS`: 100ms
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### Calibration
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- `CALIBRATION_DURATION_MS`: 5000ms
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- `CALIBRATION_PEAK_PERCENTILE`: 0.95 (95th percentile)
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## Visualization
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The system provides two visualization modes:
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1. Note Display:
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```
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Current Notes:
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A4 (440.0 Hz, Magnitude: 2500, Duration: 250ms)
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E5 (659.3 Hz, Magnitude: 1800, Duration: 150ms)
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```
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2. Spectrum Display (when enabled):
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```
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Frequency Spectrum:
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0Hz |▄▄▄▄▄
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100Hz |██████▄
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200Hz |▄▄▄
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...
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```
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## Performance Tuning
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1. Start with calibration by pressing 'c' in a quiet environment
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2. Play notes and observe the detection accuracy
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3. Use '+' and '-' to adjust sensitivity if needed
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4. Enable spectrum display with 's' to visualize frequency content
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5. Adjust `Config.h` parameters if needed for your specific setup
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## Implementation Details
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- Uses FFT for frequency analysis
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- Implements peak detection with dynamic thresholding
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- Filters out harmonics to prevent duplicate detections
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- Tracks note timing and duration
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- Uses ring buffer for real-time processing
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- Calibration collects ambient noise profile
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## Troubleshooting
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1. No notes detected:
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- Check microphone connection
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- Run calibration
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- Increase sensitivity with '+'
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- Verify audio input level in spectrum display
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2. False detections:
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- Run calibration in a quiet environment
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- Decrease sensitivity with '-'
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- Adjust `PEAK_RATIO_THRESHOLD` in Config.h
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3. Missing notes:
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- Check if notes are within C2-C6 range
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- Increase `FREQUENCY_TOLERANCE`
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- Decrease `MIN_MAGNITUDE_THRESHOLD`
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## Contributing
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Contributions are welcome! Please read the contributing guidelines before submitting pull requests.
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.
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## Development Environment Setup
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### Prerequisites
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- PlatformIO IDE (recommended) or Arduino IDE
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- ESP32 board support package
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- Required libraries:
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- arduino-audio-tools
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- arduino-audio-driver
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- WiFiManager
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- AsyncTCP
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- ESPAsyncWebServer
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- arduinoFFT
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### Building with PlatformIO
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1. Clone the repository
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2. Open the project in PlatformIO
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3. Install dependencies:
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```
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pio lib install
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```
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4. Build and upload:
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```
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pio run -t upload
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```
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## Memory Management
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### Memory Usage
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- Program Memory: ~800KB
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- RAM Usage: ~100KB
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- DMA Buffers: 4 x 512 bytes
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- FFT Working Buffer: 2048 bytes (1024 samples x 2 bytes)
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### Optimization Tips
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- Adjust `DMA_BUFFER_COUNT` based on available RAM
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- Reduce `SAMPLE_BUFFER_SIZE` for lower latency
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- Use `PSRAM` if available for larger buffer sizes
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## Advanced Configuration
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### Task Management
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- Audio processing task on Core 1:
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- I2S sample reading
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- Audio level tracking
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- Note detection and FFT analysis
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- Visualization task on Core 0:
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- WebSocket communication
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- Spectrum visualization
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- Serial interface
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- Network operations
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- Inter-core communication via FreeRTOS queue
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- Configurable priorities in `Config.h`
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### Audio Pipeline
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1. I2S DMA Input
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2. Sample Buffer Collection
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3. FFT Processing
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4. Peak Detection
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5. Note Identification
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6. Output Generation
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### Timing Parameters
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- Audio Buffer Processing: ~8ms
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- FFT Computation: ~5ms
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- Note Detection: ~2ms
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- Total Latency: ~15-20ms
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## Performance Optimization
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### CPU Usage
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- Core 1 (Audio Processing):
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- I2S DMA handling: ~15%
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- Audio analysis: ~20%
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- FFT processing: ~15%
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- Core 0 (Visualization):
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- WebSocket updates: ~5%
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- Visualization: ~5%
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- Network handling: ~5%
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### Memory Optimization
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1. Buffer Size Selection:
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- Larger buffers: Better frequency resolution
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- Smaller buffers: Lower latency
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2. DMA Configuration:
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- More buffers: Better continuity
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- Fewer buffers: Lower memory usage
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### Frequency Analysis
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- FFT Resolution: 7.8125 Hz (8000/1024)
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- Frequency Bins: 512 (Nyquist limit)
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- Useful Range: 65.41 Hz to 1046.50 Hz
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- Window Function: Hamming
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## Technical Details
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### Microphone Specifications
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- Supply Voltage: 3.3V
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- Sampling Rate: 8kHz
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- Bit Depth: 16-bit
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- SNR: >65dB (typical)
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### Signal Processing
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1. Pre-processing:
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- DC offset removal
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- Windowing function application
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2. FFT Processing:
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- 1024-point real FFT
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- Magnitude calculation
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3. Post-processing:
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- Peak detection
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- Harmonic filtering
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- Note matching
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### Calibration Process
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1. Ambient Noise Collection (5 seconds)
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2. Frequency Bin Analysis
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3. Threshold Calculation:
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- Base threshold from 95th percentile
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- Per-bin noise floor mapping
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4. Dynamic Adjustment
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## Error Handling
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### Common Issues
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1. I2S Communication Errors:
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- Check pin connections
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- Verify I2S configuration
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- Monitor serial output for error codes
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2. Memory Issues:
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- Watch heap fragmentation
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- Monitor stack usage
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- Check DMA buffer allocation
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### Error Recovery
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- Automatic I2S reset on communication errors
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- Dynamic threshold adjustment
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- Watchdog timer protection
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## Project Structure
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### Core Components
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1. AudioLevelTracker
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- Real-time audio level monitoring
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- Peak detection
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- Threshold management
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2. NoteDetector
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- Frequency analysis
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- Note identification
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- Harmonic filtering
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3. SpectrumVisualizer
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- Real-time spectrum display
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- Magnitude scaling
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- ASCII visualization
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### File Organization
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- `/src`: Core implementation files
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- `/include`: Header files and configurations
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- `/data`: Additional resources
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- `/test`: Unit tests
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## Inter-Core Communication
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### Queue Management
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- FreeRTOS queue for audio data transfer
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- 4-slot queue buffer
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- Zero-copy data passing
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- Non-blocking queue operations
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- Automatic overflow protection
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### Data Flow
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1. Core 1 (Audio Task):
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- Processes audio samples
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- Performs FFT analysis
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- Queues processed data
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2. Core 0 (Visualization Task):
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- Receives processed data
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- Updates visualization
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- Handles network communication
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### Network Communication
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- Asynchronous WebSocket updates
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- JSON-formatted spectrum data
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- Configurable update rate (50ms default)
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- Automatic client cleanup
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- Efficient connection management
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