Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 88 | Catching Z's |
Prineet Parhar Srikar Palani Suprathik Vinayakula |
Zhuchen Shao | design_document1.pdf final_paper1.pdf photo1.png photo2.png presentation1.pptx proposal1.pdf video |
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| # Title **Catching Z’s** ## Team Members - Suprathik Vinayakula (sv53) - Srikar Palani (palani3) - Prineet Parhar (pparhar2) ## Problem Sudden environmental noises such as sirens, loud neighbors, barking dogs, or door slams are a primary cause of sleep fragmentation, which negatively impacts cognitive performance and long-term health. Conventional white noise machines operate continuously at a fixed volume, which can be unnecessary or ineffective against short, intermittent disturbances. There is a need for a smart bedside system that continuously monitors room acoustics and activates noise masking only when disruptive sounds occur, while remaining off during quiet periods. ## Solution We propose **Catching Z’s**, a bedside embedded system that monitors ambient audio in real time and adaptively generates masking noise in response to disruptive sound events. Using a high-sensitivity microphone and onboard signal processing, the system establishes a baseline ambient noise profile and detects sudden sound spikes based on amplitude and frequency characteristics. When a disturbance is detected, Catching Z’s smoothly fades in white, pink, or brown noise to mask the event, then gradually fades out once the environment returns to baseline. This adaptive response minimizes unnecessary noise while preventing the masking system itself from waking the user. ## Solution Components ### Acoustic Sensing Subsystem This subsystem continuously monitors the ambient sound environment. - **Microphone Module:** Electret microphone with pre-amplifier (MAX4466) to capture low-level room noise with sufficient gain and low distortion. - **Analog-to-Digital Conversion:** The ESP32-S3’s built-in ADC samples the microphone signal at 10–20 kHz for envelope and spectral analysis. ### Processing and Audio Output Subsystem This subsystem performs sound analysis and generates masking audio. - **Microcontroller:** ESP32-S3-WROOM-1, selected for dual-core operation, allowing one core to handle real-time audio sensing while the other manages audio synthesis and playback. - **Audio Amplifier / DAC:** I2S Class-D amplifier (MAX98357A) for efficient digital-to-audio conversion and speaker drive. - **Speaker:** 4 Ω, 3 W full-range speaker (50 mm) for producing broadband masking noise. ### User Interface and Power Subsystem This subsystem provides user control and power regulation. - **User Input:** Rotary encoder (PEC11R-4215F-S0024) to adjust detection sensitivity and masking intensity thresholds. - **Power:** 5 V USB-C input with on-board regulation to 3.3 V using an AMS1117-3.3 LDO regulator. - **Indicators:** Status LEDs to indicate detection events and system state. ## Criterion for Success 1. **Detection Latency:** The system shall trigger masking noise playback within **100 ms** of detecting a sound event exceeding the ambient baseline by **≥ 10 dB**. 2. **Output Capability:** The audio subsystem shall produce masking noise over a controllable range of **40 dB to 75 dB SPL** at the bedside. 3. **Continuous Operation:** The system shall operate continuously for overnight use without performance degradation or audible artifacts. ## Risks and Mitigation - **Overreaction to brief harmless sounds:** Mitigated by minimum-duration thresholds. - **Environmental variability:** Adaptive baseline recalibration during extended quiet periods. |
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