Classification Performance Analysis of Weight Update Method Applied to Various ConvNet Models

Kim, Seung Hyun; Park, Sungwook; Lim, Su-Chang; Kim, Doyeon

doi:10.1109/iccr.2018.8534487

Search citation statements

Order By: Relevance

Paper Sections

Select...

Figure 9 Difference Between Multi-label and Combined Single1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

2022

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, CapsNet also appears to shows lesser generalization ability as compared to CNNs [80]. Finally, AdaDelta which was used as the gradient optimizer in their architecture takes a longer time to converge as compared to Adam due to its iterative operation [85], [86].…”

Section: Figure 9 Difference Between Multi-label and Combined Singlementioning

confidence: 99%

A Comprehensive Review of Polyphonic Sound Event Detection

Chan

Chin

2020

IEEE Access

View full text Add to dashboard Cite

One of the most amazing functions of the human auditory system is the ability to detect all kinds of sound events in the environment. With the technologies and hardware advances, polyphonic Sound Event Detection (SED) can be developed to mimic the ability of the human auditory system. However, the development of a SED system is no trivial task, and several different factors often hinder accuracy. Although there are several overview papers available, most of them only provide a theoretical overview of algorithms used with little discussion. Thus, to the best of the authors' knowledge, there is no comprehensive review that covers this particular domain. Therefore, this paper aims to provide an in-depth discussion of different methodologies proposed by various authors that include the features used, detection algorithms, and their corresponding accuracy and limitations. Additional information on possible trends is also discussed that can be useful for future development works.

show abstract

Section: Figure 9 Difference Between Multi-label and Combined Singlementioning

confidence: 99%