Street-level noise measurements from 655 locations across Delhi.

We recorded sound meter readings while walking and riding Delhi streets over 20+ days in April-May 2026.

Summary (GoPro continuous + Rider spot measurements):

• 655 locations across Delhi (190 GoPro, 465 Rider)
• 3.4+ hours of continuous observation (12,159 GoPro readings at 1/second)
• Median GoPro noise: 70 dB | Median Rider max: 81 dB
• 4.3% of GoPro readings exceed 85 dB (NIOSH occupational limit)
• 38% of Rider stops recorded peaks ≥85 dB

How does Delhi compare to urban traffic noise elsewhere? At traffic stops and jams, you're largely hearing engine idling and slow-moving vehicles - conditions that should produce similar noise levels across cities.

Key finding: Delhi's average street noise (~70 dB) is comparable to NYC and other busy urban environments. However, Delhi shows more extreme peaks - 38% of rider stops recorded ≥85 dB, and 25% exceeded 91 dB. This suggests Delhi's noise problem is less about average levels and more about frequent high-intensity spikes (horns, engines revving).

• 69% of stops (131/190) had at least one reading ≥85 dB
• 12% of stops (23/190) hit peaks ≥100 dB
• Median peak across stops: 89 dB
• Only 1 stop had sustained high noise (>50% of readings ≥85 dB)

Most locations experience brief spikes above safe levels, but sustained harmful exposure is rare.

• Mean max dB: 84 across all stops
• Mean min dB: 66 (baseline noise level)
• 81 traffic stops and 19 traffic jams recorded
• Road type strongly predicts noise: trunk roads average 99.5 dB max vs 77.3 dB on residential streets

GoPro analysis (output/analysis/figs/):

• delhi_gopro_fig1_map.html - Interactive map of measurement locations
• delhi_gopro_fig2_histogram.pdf - Distribution of all readings
• delhi_gopro_fig3_stop_distributions.pdf - Per-location summary statistics

Rider analysis (output/rider/analysis/figs/):

• delhi_rider_fig1_map.html - Interactive map of rider stops
• delhi_rider_fig2_histogram.pdf - Distribution of max dB readings
• delhi_rider_fig4_boxplot.pdf - Noise by road type
• delhi_rider_fig5_scatter.pdf - Min vs max dB correlation

Comparison (output/comparison/figs/):

• delhi_compare_fig1_map.html - Map showing both datasets with matches
• delhi_compare_fig2_correlation.pdf - Rider max dB vs GoPro mean dB
• delhi_compare_fig4_road_type.pdf - GoPro noise by road type

Raw GoPro recordings with embedded GPS telemetry:

• Python 3.13+
• ffmpeg, exiftool (brew install ffmpeg exiftool)
• Google API key (for OCR)

git clone https://github.com/soodoku/soundscape
cd soundscape
uv sync
uv pip install -e ".[maps]"

# Generate figures and tables from existing readings
uv run soundscape analyze --readings output/readings/delhi_full_12728_3-flash-preview_20260523_190001.json

# Open interactive map
open output/analysis/figs/fig1_map_locations.html

If you download the raw videos from Dataverse:

# Extract frames, GPS, metadata
uv run soundscape extract-frames --input data/delhi
uv run soundscape extract-gps --input data/delhi
uv run soundscape extract-exif --input data/delhi
uv run soundscape downsample
uv run soundscape build-manifest

# OCR the sound meter readings
export GOOGLE_API_KEY=your_key
uv run soundscape ocr-readings --model gemini-2.5-flash

# Generate analysis
uv run soundscape analyze --readings output/readings/readings.json

• Equipment: Handheld sound level meter + GoPro Hero 12 (4K, 50fps, GPS enabled)
• Protocol: Walk to location, hold meter at arm's length, record ~1 minute
• Dates: April 30 - May 14, 2026
• Locations: 190 stops across Delhi (residential, commercial, roadside)

1. Extract frames - Sample 1 frame/second from 50fps video
2. Downsample - Resize to 720p for efficient OCR
3. Extract GPS - Pull coordinates from GoPro telemetry
4. OCR readings - Use Gemini to read decibel value from each frame
5. Filter - Remove readings outside 30-130 dB range (OCR errors)
6. Analyze - Generate tables and figures

• 95.5% of frames successfully OCR'd
• 5 readings filtered as physically implausible (<30 or >130 dB)
• Manual spot-check via HTML viewer (soundscape viewer)

For data collected via the rider route tool, conversion scripts are in that repo:

cd ../missing-women-rider-route-tool

# Sound data -> soundscape
python3 export_sound.py \
    --manifest exports/2026-05-24/manifest.json \
    --output ../soundscape/output/rider \
    --geocode

# Pollution data -> streetaqi
python3 export_pollution.py \
    --manifest exports/2026-05-24/manifest.json \
    --output ../streetaqi/data/rider \
    --city delhi \
    --geocode

# Generate rider-specific analysis (by road type, traffic conditions)
uv run soundscape analyze-rider --readings output/rider/readings.json

# Compare rider and GoPro locations for spatial overlap
uv run soundscape compare-locations \
    --rider output/rider/readings.json \
    --gopro "output/readings/delhi_full_*.json"

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