Sep 6, 2018 · In this paper, we present a state-of-the-art system for audio event detection. The labels on the training (and evaluation) data.
In this paper, we propose a set of features based on Teager Energy Operator and a slightly modified version of x-vector system to detect replay attacks. The ...
The training of SED models used to rely upon strong labeling, which specifies the type, onset time and offset time of each sound event occurrence.
Missing: Aggregation | Show results with:Aggregation
Abstract—Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources.
In this paper, we present a state-of-the-art system for audio event detection. The labels on the training (and evaluation) data.
ABSTRACT. This extended abstract describes the design and implementation of a multiple instance learning model for sound event detection.
The purpose of this paper is to propose a two-stage classifier able to identify, in real time, a set of up to 21 urban acoustic events that may occur ...
The main motivation is that gathering weak labels is simpler, less time consuming and less ambiguous than determining events' onset/offset (i.e., strong labels) ...
Feb 5, 2018 · The k-means method provides the possibility for change detection and clustering in audio events. Though identifying the actual meaning of every ...
Oct 25, 2023 · This paper explores the challenging polyphonic sound event detection problem using machine learning architectures applied to data recorded in the Beaufort Sea.