A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection
2018
Interspeech 2018
Preserved Fulltext
This paper proposes a Region-based Convolutional Recurrent Neural Network (R-CRNN) for audio event detection (AED). ...
The proposed network is inspired by Faster-RCNN [1], a wellknown region-based convolutional network framework for visual object detection. ...
Conclusions A Region-based Convolutional Recurrent Neural Network is proposed for AED. ...
doi:10.21437/interspeech.2018-2323
dblp:conf/interspeech/KaoWSW18
fatcat:x6tiaje43rfvhmf4kxxmhfzluu
MTF-CRNN: Multiscale Time-Frequency Convolutional Recurrent Neural Network For Sound Event Detection
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
To reduce neural network parameter counts and improve sound event detection performance, we propose a multiscale time-frequency convolutional recurrent neural network (MTF-CRNN) for sound event detection ...
INDEX TERMS Pattern recognition, sound event detection, multiscale learning, time-frequency transform, convolutional recurrent neural network. ...
the CNN and RNN for rare sound event detection [11] . • R-CRNN: The R-CRNN is a Region-based CRNN to detect audio event and achieved the best performing single-model method among all methods without ...
doi:10.1109/access.2020.3015047
fatcat:hzox2myax5gapo4so5wthrcd4q
Learning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection
2019
Interspeech 2019
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a temporal-frequential attention model for sound event detection (SED). ...
With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. ...
[5] proposed a Region-based Convolutional Recurrent Neural Network (R-CRNN) to improve previous work in 2018. ...
doi:10.21437/interspeech.2019-2045
dblp:conf/interspeech/ShenHZ19
fatcat:weav3oilyrctlgwdb5b5xwppni
Learning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a temporal-frequential attention model for sound event detection (SED). ...
With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. ...
[5] proposed a Region-based Convolutional Recurrent Neural Network (R-CRNN) The corresponding author is Wei-Qiang Zhang. to improve previous work in 2018. ...
arXiv:1810.11939v1
fatcat:tga6n7b7rzfere3dskpthiq4gu
Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Motivated by a lack of suitable benchmark datasets for few-shot audio event detection, we generate and evaluate on two novel episodic rare sound event datasets: one using clips of celebrity speech as the ...
In this paper, we propose novel approaches to few-shot sound event detection utilizing region proposals and the Perceiver architecture, which is capable of accurately localizing sound events with very ...
The result is a sequence of per-timestep feature vectors, with time resolution downsampled by a factor of 8. 2) Region Proposal Network: Our RPN is based on R-CRNN, another region-based convolutional recurrent ...
arXiv:2107.13616v1
fatcat:qvp553hrerfm7clpi7ggrbjxlu
Multi-Scale Time-Frequency Attention for Acoustic Event Detection
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Most attention-based methods only concentrate along the time axis, which is insufficient for Acoustic Event Detection (AED). ...
We demonstrate the proposed method on Task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge. ...
[7] , on the contrary, applied Region Proposal Network [8] to perform event-level detection and directly generate the onset/offset of target events. ...
arXiv:1904.00063v3
fatcat:dzq2n3keavhc7mpucvlzdqucya
Multi-Scale Time-Frequency Attention for Acoustic Event Detection
2019
Interspeech 2019
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Most attention-based methods only concentrate along the time axis, which is insufficient for Acoustic Event Detection (AED). ...
We demonstrate the proposed method on Task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge. ...
[7] , on the contrary, applied Region Proposal Network [8] to perform event-level detection and directly generate the onset/offset of target events. ...
doi:10.21437/interspeech.2019-1587
dblp:conf/interspeech/ZhangDKH19
fatcat:3sr7sk44azddvkejrpww3qznfi
Adaptive Multi-scale Detection of Acoustic Events
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
By taking advantage of the hourglass neural network and gated recurrent unit (GRU) module, our AdaMD produces multiple predictions at different temporal and frequency resolutions. ...
The goal of acoustic (or sound) events detection (AED or SED) is to predict the temporal position of target events in given audio segments. ...
In view of the excellent performance of convolutional recurrent neural network (CRNN) [12] in processing time sequences [13] , [14] , our high-level structure consists of a CNN and a RNN network. ...
arXiv:1911.06878v2
fatcat:mgrtw2sntjhmnojcdfbnrozcyu
An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
2021
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This research work presents an audio-based automated system for victim detection in fire emergencies, investigating two machine learning (ML) methods: support vector machines (SVM) and long short-term ...
It is time-critical to detect the victims trapped inside the burning sites for facilitating the rescue operations. ...
Data Availability Statement: The dataset samples used for this study are available at https://drive. google.com/drive/folders/1RweIZZ5T5FC2uG96kyXKlczlzVB463Ea (accessed on 9 August 2021). ...
doi:10.3390/app11188425
fatcat:modpqxd5zfditaerh6stl2kr3u