bowdet 0.2.0

Audio-based bow-change detection for string instruments

bowdet

Audio-based bow-change detection for string instruments.

bowdet detects bow changes (down-bow ↔ up-bow transitions) in bowed string instrument recordings using a two-stage mel-spectrogram boundary approach.

Install

pip install bowdet

Usage

from bowdet import detect

times = detect("recording.wav")
print(times)  # array of bow-change times in seconds

Options

times = detect(
    "recording.wav",
    threshold=0.40,      # classification threshold (default: 0.40)
    min_dist_sec=0.12,   # minimum distance between events in seconds
)

Method

The system uses a two-stage pipeline:

1. Stage 1 — Boundary Proposal: Computes log-mel spectrogram boundary strength (L2 norm of frame differences), and proposes candidate bow-change positions at spectral peaks.

2. Stage 2 — Candidate Classification: A compact CNN fused with 15 acoustic boundary features classifies each candidate as a bow change or not.

Key insight: Bow changes produce a broadband energy transition visible in the log-mel spectrogram, spanning approximately 100–300 ms across all frequency bands.

Performance

Evaluated under leave-one-performer-out (LOPO) on 6 viola performers (9 recordings, ~17.7 min, 1,020 annotated bow changes):

Metric	Score
IoU@0.1 F1	0.645 ± 0.012
Point F1 @100ms	0.635

Citation

If you use bowdet in your research, please cite:

Yuan, H. and Su, Y. (2026). Audio-Based Bow-Change Detection in Viola Performance:
A Two-Stage Mel-Spectrogram Boundary Approach.
Late-Breaking/Demo Session, ISMIR 2026.

License

MIT