USM-SED - A Dataset for Polyphonic Sound Event Detection in Urban Sound Monitoring Scenarios

By Jakob Abeßer. 2021

7 citations recorded by the source corpus.

This paper introduces a novel dataset for polyphonic sound event detection in urban sound monitoring use-cases. Based on isolated sounds taken from the FSD50k dataset, 20,000 polyphonic soundscapes are synthesized with sounds being randomly positioned in the stereo panorama using different loudness levels. The paper gives a detailed discussion of possible application scenarios, explains the dataset generation process in detail, and discusses current limitations of the proposed USM-SED dataset.

Subfields: Speech and Hearing, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Speech and Hearing

1. Jordi Pons
2. Xavier Serra
3. Eduardo Fonseca
4. Xavier Favory
5. Frederic Font

In Signal Processing

1. Eduardo Fonseca
2. Xavier Serra
3. Frederic Font
4. Xavier Favory
5. Jordi Pons
6. Andrés Ferraro
7. Jakob Abeßer
8. D. Ellis

Citations in the corpus

• FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “A subset of the data has been used for a number of smaller datasets for classification [32, 33, 58, 100] and source separation [63].”