BeepBank-500: A Synthetic Earcon Mini-Corpus for UI Sound Research and
  Psychoacoustics Research
Authors: name: Mandip Goswami

We introduce BeepBank-500, a compact, fully synthetic earcon/alert dataset
(300-500 clips) designed for rapid, rights-clean experimentation in
human-computer interaction and audio machine learning. Each clip is generated
from a parametric recipe controlling waveform family (sine, square, triangle,
FM), fundamental frequency, duration, amplitude envelope, amplitude modulation
(AM), and lightweight Schroeder-style reverberation. We use three reverberation
settings: dry, and two synthetic rooms denoted 'rir small' ('small') and 'rir
medium' ('medium') throughout the paper and in the metadata. We release mono 48
kHz WAV audio (16-bit), a rich metadata table (signal/spectral features), and
tiny reproducible baselines for (i) waveform-family classification and (ii) f0
regression on single tones. The corpus targets tasks such as earcon
classification, timbre analyses, and onset detection, with clearly stated
licensing and limitations. Audio is dedicated to the public domain via CC0-1.0;
code is under MIT. Data DOI: https://doi.org/10.5281/zenodo.17172015. Code:
https://github.com/mandip42/earcons-mini-500.