This project presents a compact, affordable system capable of determining the direction and elevation of a sound source using a Raspberry Pi Pico microcontroller.

Operational Mechanics

Upon detecting a sufficiently loud sound, the system:

1. Captures audio signals from all four microphones.

2. Applies Fast Fourier Transforms (FFT) to convert time-domain signals into the frequency domain.

3. Computes cross-correlations between microphone pairs in the frequency domain using the formula:

   R(τ) = INV_F{F{x(t)} · F{y(t)}*}

   where F denotes the Fourier Transform, INV_F its inverse, and * indicates the complex conjugate.

4. Identifies time delays by locating the maxima in the cross-correlation functions.

5. Determines the sound source's direction by referencing a precomputed lookup table based on these delays.

6. Displays the result on the touchscreen, indicating the direction with a graphical pointer.

This method offers computational efficiency with a complexity of O(n log n), suitable for real-time processing on resource-constrained hardware.

Demonstration

A demonstration video showcases the system's responsiveness, with processing times of just a few hundred milliseconds. The red LED indicates the processing phase, and the touchscreen provides immediate visual feedback on the detected sound direction.

Limitations

• Static Sound Sources: The system assumes stationary sound sources; moving sources can introduce inaccuracies due to unknown velocities.

• Single Source Detection: Designed for single-source scenarios; multiple simultaneous sources may complicate localization.

Potential Enhancements

• ESP32 Integration: Utilizing an ESP32 microcontroller with I2S ports and DSP capabilities can enhance performance and allow the use of MEMS microphones.

• LoRa Communication: Implementing LoRa modules enables multiple units to communicate, facilitating triangulation over larger areas.

• Drone Deployment: Mounting the system on drones can provide rapid, wide-area sound source localization.

• Deep Learning for Sound Classification: Incorporating neural networks can allow the system to classify different sound types, such as distinguishing between gunshots, machinery noise, or human voices.

Applications

This low-cost, efficient system has potential applications in:

• Robotics: Enhancing environmental awareness through sound direction detection.

• Security: Detecting and localizing unusual sounds in surveillance scenarios.

• Search and Rescue: Locating individuals in distress through sound cues

Example of On-Device Deep Learning Classification

A keyword spotting application showcasing this approach is presented in the following video: