Semi-Supervised NMF-CNN For Sound Event Detection

07/02/2020
by   Chan Teck Kai, et al.
0

For the DCASE 2020 Challenge Task 4, this paper pro-posed a combinative approach using Nonnegative Matrix Factorization (NMF) and Convolutional Neural Network (CNN). The main idea begins with utilizing NMF to ap-proximate strong labels for the weakly labeled data. Sub-sequently, based on the approximated strongly labeled data, two different CNNs are trained using a semi-supervised framework where one CNN is used for clip-level prediction and the other for frame-level prediction. Using this idea, the best model trained can achieve an event-based F1-score of 45.7 models, the event-based F1-score can be increased to 48.6 the base-line model, the proposed model outperforms the baseline model by a margin of over 8

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/22/2020

Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) For Sound Event Detection

The main scientific question of this year DCASE challenge, Task 4 - Soun...
research
09/21/2020

Detecting Acoustic Events Using Convolutional Macaron Net

In this paper, we propose to address the issue of the lack of strongly l...
research
10/07/2021

Peer Collaborative Learning for Polyphonic Sound Event Detection

This paper describes that semi-supervised learning called peer collabora...
research
10/16/2018

Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement

In this paper, we present a gated convolutional recurrent neural network...
research
05/23/2020

Power Pooling Operators and Confidence Learning for Semi-Supervised Sound Event Detection

In recent years, the involvement of synthetic strongly labeled data,weak...
research
11/30/2018

Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision

As open-ended human-chatbot interaction becomes commonplace, sensitive c...
research
06/01/2019

Super-resolution of Time-series Labels for Bootstrapped Event Detection

Solving real-world problems, particularly with deep learning, relies on ...

Please sign up or login with your details

Forgot password? Click here to reset