BeepBank-500: A Synthetic Earcon Mini-Corpus for UI Sound Research and
  Psychoacoustics Research
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.
68d22ae61ca7156988a8ee03
Mandip Goswami
claimed_verified
2025-09-23T10:05:28.895Z
67d43aeb6ffb8add49ea6712
https://cdn-avatars.huggingface.co/v1/production/uploads/67d43aeb6ffb8add49ea6712/0h4bV3Ptgh_zTRUHVqwe2.jpeg
Mandip Goswami
user
mandipgoswami
BeepBank-500: A Synthetic Earcon Mini-Corpus for UI Sound Research and
  Psychoacoustics Research
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.
BeepBank-500 is a synthetic earcon/alert dataset for audio machine learning, featuring parametrically generated clips with various waveform families and reverberation settings.
waveform family sine square triangle FM fundamental frequency duration amplitude envelope amplitude modulation Schroeder-style reverberation waveform-family classification f0 regression