DeepAI AI Chat
Log In Sign Up

VOICe: A Sound Event Detection Dataset For Generalizable Domain Adaptation

by   Shayan Gharib, et al.
Tampere Universities

The performance of sound event detection methods can significantly degrade when they are used in unseen conditions (e.g. recording devices, ambient noise). Domain adaptation is a promising way to tackle this problem. In this paper, we present VOICe, the first dataset for the development and evaluation of domain adaptation methods for sound event detection. VOICe consists of mixtures with three different sound events ("baby crying", "glass breaking", and "gunshot"), which are over-imposed over three different categories of acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures are also offered without any background noise. VOICe is freely available online ( In addition, using an adversarial-based training method, we evaluate the performance of a domain adaptation method on VOICe.


page 1

page 2

page 3

page 4


Unsupervised adversarial domain adaptation for acoustic scene classification

A general problem in acoustic scene classification task is the mismatche...

Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments

Our goal is to develop a sound event localization and detection (SELD) s...

Robustness against the channel effect in pathological voice detection

Many people are suffering from voice disorders, which can adversely affe...

Impulsive Sound Detection by a Novel Energy Formula and its Usage for Gunshot Recognition

There are many methods proposed for the detection of impulsive sounds in...

Foreground-Background Ambient Sound Scene Separation

Ambient sound scenes typically comprise multiple short events occurring ...

Animation Synthesis Triggered by Vocal Mimics

We propose a method leveraging the naturally time-related expressivity o...

Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study

Detection of common events and scenes from audio is useful for extractin...