# The NIGENS General Sound Events Database

arXiv: https://arxiv.org/abs/1902.08314 · HTML: https://mlsanity.com/paper/1902.08314/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 8 of 8)

- [Diff-SAGe: End-to-End Spatial Audio Generation Using Diffusion Models](https://mlsanity.com/paper/2410.11299/) (2024)
- [Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning](https://mlsanity.com/paper/2505.07365/) (2025)
- [From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning](https://mlsanity.com/paper/2403.08525/) (2024)
- [Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning](https://mlsanity.com/paper/2503.02422/) (2025)
- [Cross-domain Voice Activity Detection with Self-Supervised Representations](https://mlsanity.com/paper/2209.11061/) (2022)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions to Fearful and Shocking Events for Enhanced Sociability](https://mlsanity.com/paper/2312.07671/) (2023)
- [AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation](https://mlsanity.com/paper/2509.02349/) (2025)

## Abstract (arXiv)

Computational auditory scene analysis is gaining interest in the last years. Trailing behind the more mature field of speech recognition, it is particularly general sound event detection that is attracting increasing attention. Crucial for training and testing reasonable models is having available enough suitable data -- until recently, general sound event databases were hardly found. We release and present a database with 714 wav files containing isolated high quality sound events of 14 different types, plus 303 `general' wav files of anything else but these 14 types. All sound events are strongly labeled with perceptual on- and offset times, paying attention to omitting in-between silences. The amount of isolated sound events, the quality of annotations, and the particular general sound class distinguish NIGENS from other databases.