Environmental sound scene and sound event recognition is important for the
recognition of suspicious events in indoor and outdoor environments (such as
nurseries, smart homes, nursing homes, etc.) and is a fundamental task involved
in many audio surveillance applications. In particular, there is no public
common data set for the research field of sound event recognition for the data
set of the indoor environmental sound scene. Therefore, this paper proposes a
data set (called as AGS) for the home environment sound. This data set
considers various types of overlapping audio in the scene, background noise.
Moreover, based on the proposed data set, this paper compares and analyzes the
advanced methods for sound event recognition, and then illustrates the
reliability of the data set proposed in this paper, and studies the challenges
raised by the new data set. Our proposed AGS and the source code of the
corresponding baselines at this https URL .

本文提出了一个家庭环境声音数据集（称为 AGS），考虑了场景中各种类型的重叠音频和背景噪音，比较分析了先进的声音事件识别方法，并展示了该数据集的可靠性和新数据集带来的挑战。

AGS: 国内场景声音事件识别的数据集和分类系统

AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition

Most existing datasets for sound event recognition (SER) are relatively small
and/or domain-specific, with the exception of AudioSet, based on over 2M tracks
from YouTube videos and encompassing over 500 sound classes. However, AudioSet
is not an open dataset as its official release consists of pre-computed audio
features. Downloading the original audio tracks can be problematic due to
YouTube videos gradually disappearing and usage rights issues. To provide an
alternative benchmark dataset and thus foster SER research, we introduce
FSD50K, an open dataset containing over 51k audio clips totalling over 100h of
audio manually labeled using 200 classes drawn from the AudioSet Ontology. The
audio clips are licensed under Creative Commons licenses, making the dataset
freely distributable (including waveforms). We provide a detailed description
of the FSD50K creation process, tailored to the particularities of Freesound
data, including challenges encountered and solutions adopted. We include a
comprehensive dataset characterization along with discussion of limitations and
key factors to allow its audio-informed usage. Finally, we conduct sound event
classification experiments to provide baseline systems as well as insight on
the main factors to consider when splitting Freesound audio data for SER. Our
goal is to develop a dataset to be widely adopted by the community as a new
open benchmark for SER research.

介绍了一个新的开放音频数据集 FSD50K，其包含了来自 AudioSet 本体中的 200 个分类中的超过 51k 个手动标记的音频片段，旨在成为声音事件识别（SER）领域的新开放基准数据集。

FSD50K：一个带有人工标记的声音事件开放数据集

FSD50K: An Open Dataset of Human-Labeled Sound Events

Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.

本研究利用卷积神经网络 (CNN) 和循环神经网络 (RNN) 实现了声音事件监测，取得了比之前方法显著的效果提升。