Project Explanation and Analysis

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Project Overview
The Excess Tomato Translation Collar interprets cat vocalizations in real time and presents them as human-readable phrases. It’s a wearable collar that captures audio, processes it with an on-board machine-learning model, and then displays or transmits the “translation.”
High-Level Block Diagram

Image

Audio Front End
ADC
Processing Unit
ML Classification Engine
Output Interface
Wireless Interface
Power Management

Core Components

BlockComponentFunction
Audio Front EndMEMS MicrophoneCaptures cat vocalizations (20 Hz–20 kHz).
MAX9814 (Mic Preamplifier)Provides programmable gain and AGC to normalize input levels.
ADCADS111516-bit I²C ADC to digitize the conditioned audio signal.
Processing Unit (MCU)ESP32Runs real-time DSP + ML inference; handles I²S (audio), I²C (ADC/Display), BLE/Wi-Fi.
ML EngineOn-device classification model (TensorFlow Lite Micro)Maps spectral features to predefined vocalization categories (“hungry,” “play,” “pet me,” etc.).
Output Interface0.96" OLED DisplayShows the translated phrase; low-power, high-contrast.
Wireless InterfaceBLE via ESP32Optionally streams raw audio or translation events to a smartphone app.
Power ManagementMCP73831 (LiPo Charger)Manages USB charging of Li-ion battery.
MCP1700-3302E (3.3 V LDO)Regulates the battery down to stable 3.3 V rail.
Energy StorageLiPo BatteryPowers the collar; capacity sized for ≥8 hrs of continuous operation.
Design Choices & Trade-offs
  • On-Device vs. Cloud Inference
    • On-Device (ESP32 + TFLM): Low latency, offline operation, privacy.
    • Cloud (smartphone/server): More complex models possible, but requires BLE/Wi-Fi and adds latency/power draw.
  • Microphone & AGC
    • MAX9814’s AGC simplifies gain-staging across different cat volumes, but can introduce pumping artifacts if thresholds not tuned.
  • ADC Resolution
    • ADS1115’s 16-bit resolution ensures capture of subtle vocal nuances, at the cost of slightly higher power than a built-in 12-bit ADC.
  • Display vs. App Output
    • On-collar OLED display requires additional current (~ 3 mA active), but gives instant feedback without a phone.
    • BLE streaming saves display power but requires a companion app.
Implementation & Functionality Flow
  1. Audio Capture
    • Collar microphone picks up meows.
    • Preamplifier applies AGC to normalize amplitude.
  2. Digitization
    • Conditioned analog audio → ADS1115 → digital samples (e.g., 16 kHz, 16 bit).
  3. Pre-processing
    • MCU computes Mel-frequency cepstral coefficients (MFCCs) or log-mel spectrogram in real time.
  4. Classification
    • TFLM model on ESP32 infers the most likely vocalization category.
  5. Output
    • Displays phrase on OLED or sends BLE notification to paired device.
  6. Power Management
    • LiPo battery supplies all blocks; charger and LDO maintain safe charging/discharging.
Challenges & Performance Considerations
  • Model Accuracy
    • Cat vocalizations are subtle and overlap; requires a well-curated dataset and careful augmentation (noise, distance).
  • Real-Time Constraints
    • MFCC + inference must complete within ~ 50 ms; optimize code and leverage ESP32’s DSP instructions.
  • Power Budget
    • Balance sampling rate, display updates, and BLE radio duty cycling to achieve multi-hour operation on a small LiPo.
  • Mechanical & Environmental
    • Collar must be rugged, water-resistant, and lightweight (<50 g).
Real-World Applications
  • Pet Behavior Research: Quantify cat moods in shelters.
  • Assisted Living: Help hearing-impaired pet owners “hear” their cat.
  • Veterinary Diagnostics: Correlate vocal patterns with health issues.
Future Enhancements & Scalability
  • Advanced Models: Offload to Edge TPU or migrate to more powerful MCUs (e.g., Raspberry Pi Pico W + Coral TPU) for finer classification.
  • Custom Phrases: Allow owners to train and upload custom mappings via smartphone app.
  • Multi-Sensor Fusion: Combine audio with accelerometer/thermistor data for richer context (“purr + cat on lap = content”).
  • Solar-Assist Charging: Integrate flexible PV cells into the collar band for extended field use.
  • OTA Updates: Secure firmware and model updates over BLE or Wi-Fi.
This architecture balances latency, power, and on-device intelligence to deliver a standalone collar that “speaks cat.” Adjust component selections, model complexity, and output methods based on your target use case, weight constraints, and desired autonomy.
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C1
Capacitance
10u F
R4
Resistance
500 Ω
R3
Resistance
500 Ω
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Excess Tomato Translation Collar

Excess Tomato Translation Collar
Description

Created
Last updated by rintoj
1 Contributor(s)
rintoj

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