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Jose f500937067 refactor ♻️: Refactored the audio processing and visualization tasks into separate cores, improved CPU usage monitoring, optimized memory usage, managed inter-core communication, and enhanced network functionality. 
- 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.
2025-04-25 21:13:59 +02:00

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ESP32 Piano Note Detection System

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.

Features

  • Real-time audio processing using I2S microphone
  • FFT-based frequency analysis
  • Note detection from C2 (65.41 Hz) to C6 (1046.50 Hz)
  • Dynamic threshold calibration
  • Multiple note detection (up to 7 simultaneous notes)
  • Harmonic filtering
  • Real-time spectrum visualization
  • Note timing and duration tracking
  • Interactive Serial commands for system tuning

Hardware Requirements

  • ESP32 development board
  • I2S MEMS microphone (e.g., INMP441, SPH0645)
  • USB connection for Serial monitoring

Pin Configuration

The system uses the following I2S pins by default (configurable in Config.h):

  • SCK (Serial Clock): GPIO 8
  • WS/LRC (Word Select/Left-Right Clock): GPIO 9
  • SD (Serial Data): GPIO 10

Getting Started

  1. Connect the I2S microphone to the ESP32 according to the pin configuration
  2. Build and flash the project to your ESP32
  3. Open a Serial monitor at 115200 baud
  4. Follow the calibration process on first run

Serial Commands

The system can be controlled via Serial commands:

  • h - Display help menu
  • c - Start calibration process
  • + - Increase sensitivity (threshold up)
  • - - Decrease sensitivity (threshold down)
  • s - Toggle spectrum visualization

Configuration Options

All system parameters can be adjusted in Config.h:

Audio Processing

  • SAMPLE_RATE: 8000 Hz (good for frequencies up to 4kHz)
  • BITS_PER_SAMPLE: 16-bit resolution
  • SAMPLE_BUFFER_SIZE: 1024 samples
  • FFT_SIZE: 1024 points

Note Detection

  • NOTE_FREQ_C2: 65.41 Hz (lowest detectable note)
  • NOTE_FREQ_C6: 1046.50 Hz (highest detectable note)
  • FREQUENCY_TOLERANCE: 3.0 Hz
  • MAX_SIMULTANEOUS_NOTES: 7
  • MIN_NOTE_DURATION_MS: 50ms
  • NOTE_RELEASE_TIME_MS: 100ms

Calibration

  • CALIBRATION_DURATION_MS: 5000ms
  • CALIBRATION_PEAK_PERCENTILE: 0.95 (95th percentile)

Visualization

The system provides two visualization modes:

  1. Note Display:
Current Notes:
A4 (440.0 Hz, Magnitude: 2500, Duration: 250ms)
E5 (659.3 Hz, Magnitude: 1800, Duration: 150ms)
  1. Spectrum Display (when enabled):
Frequency Spectrum:
0Hz    |▄▄▄▄▄
100Hz  |██████▄
200Hz  |▄▄▄
...

Performance Tuning

  1. Start with calibration by pressing 'c' in a quiet environment
  2. Play notes and observe the detection accuracy
  3. Use '+' and '-' to adjust sensitivity if needed
  4. Enable spectrum display with 's' to visualize frequency content
  5. Adjust Config.h parameters if needed for your specific setup

Implementation Details

  • Uses FFT for frequency analysis
  • Implements peak detection with dynamic thresholding
  • Filters out harmonics to prevent duplicate detections
  • Tracks note timing and duration
  • Uses ring buffer for real-time processing
  • Calibration collects ambient noise profile

Troubleshooting

  1. No notes detected:

    • Check microphone connection
    • Run calibration
    • Increase sensitivity with '+'
    • Verify audio input level in spectrum display

  2. False detections:

    • Run calibration in a quiet environment
    • Decrease sensitivity with '-'
    • Adjust PEAK_RATIO_THRESHOLD in Config.h

  3. Missing notes:

    • Check if notes are within C2-C6 range
    • Increase FREQUENCY_TOLERANCE
    • Decrease MIN_MAGNITUDE_THRESHOLD

Contributing

Contributions are welcome! Please read the contributing guidelines before submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Development Environment Setup

Prerequisites

  • PlatformIO IDE (recommended) or Arduino IDE
  • ESP32 board support package
  • Required libraries:
    • arduino-audio-tools
    • arduino-audio-driver
    • WiFiManager
    • AsyncTCP
    • ESPAsyncWebServer
    • arduinoFFT

Building with PlatformIO

  1. Clone the repository
  2. Open the project in PlatformIO
  3. Install dependencies: 
    pio lib install
  4. Build and upload: 
    pio run -t upload

Memory Management

Memory Usage

  • Program Memory: ~800KB
  • RAM Usage: ~100KB
  • DMA Buffers: 4 x 512 bytes
  • FFT Working Buffer: 2048 bytes (1024 samples x 2 bytes)

Optimization Tips

  • Adjust DMA_BUFFER_COUNT based on available RAM
  • Reduce SAMPLE_BUFFER_SIZE for lower latency
  • Use PSRAM if available for larger buffer sizes

Advanced Configuration

Task Management

  • Audio processing task on Core 1:
    • I2S sample reading
    • Audio level tracking
    • Note detection and FFT analysis
  • Visualization task on Core 0:
    • WebSocket communication
    • Spectrum visualization
    • Serial interface
    • Network operations
  • Inter-core communication via FreeRTOS queue
  • Configurable priorities in Config.h

Audio Pipeline

  1. I2S DMA Input
  2. Sample Buffer Collection
  3. FFT Processing
  4. Peak Detection
  5. Note Identification
  6. Output Generation

Timing Parameters

  • Audio Buffer Processing: ~8ms
  • FFT Computation: ~5ms
  • Note Detection: ~2ms
  • Total Latency: ~15-20ms

Performance Optimization

CPU Usage

  • Core 1 (Audio Processing):
    • I2S DMA handling: ~15%
    • Audio analysis: ~20%
    • FFT processing: ~15%
  • Core 0 (Visualization):
    • WebSocket updates: ~5%
    • Visualization: ~5%
    • Network handling: ~5%

Memory Optimization

  1. Buffer Size Selection:
    • Larger buffers: Better frequency resolution
    • Smaller buffers: Lower latency
  2. DMA Configuration:
    • More buffers: Better continuity
    • Fewer buffers: Lower memory usage

Frequency Analysis

  • FFT Resolution: 7.8125 Hz (8000/1024)
  • Frequency Bins: 512 (Nyquist limit)
  • Useful Range: 65.41 Hz to 1046.50 Hz
  • Window Function: Hamming

Technical Details

Microphone Specifications

  • Supply Voltage: 3.3V
  • Sampling Rate: 8kHz
  • Bit Depth: 16-bit
  • SNR: >65dB (typical)

Signal Processing

  1. Pre-processing:
    • DC offset removal
    • Windowing function application
  2. FFT Processing:
    • 1024-point real FFT
    • Magnitude calculation
  3. Post-processing:
    • Peak detection
    • Harmonic filtering
    • Note matching

Calibration Process

  1. Ambient Noise Collection (5 seconds)
  2. Frequency Bin Analysis
  3. Threshold Calculation:
    • Base threshold from 95th percentile
    • Per-bin noise floor mapping
  4. Dynamic Adjustment

Error Handling

Common Issues

  1. I2S Communication Errors:
    • Check pin connections
    • Verify I2S configuration
    • Monitor serial output for error codes
  2. Memory Issues:
    • Watch heap fragmentation
    • Monitor stack usage
    • Check DMA buffer allocation

Error Recovery

  • Automatic I2S reset on communication errors
  • Dynamic threshold adjustment
  • Watchdog timer protection

Project Structure

Core Components

  1. AudioLevelTracker
    • Real-time audio level monitoring
    • Peak detection
    • Threshold management
  2. NoteDetector
    • Frequency analysis
    • Note identification
    • Harmonic filtering
  3. SpectrumVisualizer
    • Real-time spectrum display
    • Magnitude scaling
    • ASCII visualization

File Organization

  • /src: Core implementation files
  • /include: Header files and configurations
  • /data: Additional resources
  • /test: Unit tests

Inter-Core Communication

Queue Management

  • FreeRTOS queue for audio data transfer
  • 4-slot queue buffer
  • Zero-copy data passing
  • Non-blocking queue operations
  • Automatic overflow protection

Data Flow

  1. Core 1 (Audio Task):
    • Processes audio samples
    • Performs FFT analysis
    • Queues processed data
  2. Core 0 (Visualization Task):
    • Receives processed data
    • Updates visualization
    • Handles network communication

Network Communication

  • Asynchronous WebSocket updates
  • JSON-formatted spectrum data
  • Configurable update rate (50ms default)
  • Automatic client cleanup
  • Efficient connection management
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