Frequency-dependent auto-pooling function for weakly supervised sound event detection
Abstract Sound event detection (SED), which is typically treated as a supervised problem, aims at detecting types of sound events and corresponding temporal information. It requires to estimate onset and offset annotations for sound events at each frame. Many available sound event datasets only cont...
Main Authors: | Sichen Liu, Feiran Yang, Yin Cao, Jun Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2021-05-01
|
Series: | EURASIP Journal on Audio, Speech, and Music Processing |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13636-021-00206-7 |
Similar Items
-
Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features
by: Shing-Yun Jung, et al.
Published: (2021-04-01) -
Research on Semi-Supervised Sound Event Detection Based on Mean Teacher Models Using ML-LoBCoD-NET
by: Jinjia Wang, et al.
Published: (2020-01-01) -
Semi-Supervised NMF-CNN for Sound Event Detection
by: Teck Kai Chan, et al.
Published: (2021-01-01) -
LW-Sketch-Net on Mobile Phones
by: Ni Kong, et al.
Published: (2019-04-01) -
Polyphonic Sound Event Detection Based on Residual Convolutional Recurrent Neural Network With Semi-Supervised Loss Function
by: Nam Kyun Kim, et al.
Published: (2021-01-01)