2020
DOI: 10.1016/j.specom.2020.03.006
|View full text |Cite
|
Sign up to set email alerts
|

Discriminative neural network pruning in a multiclass environment: A case study in spoken emotion recognition

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
9
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 12 publications
(9 citation statements)
references
References 19 publications
0
9
0
Order By: Relevance
“…The advantages of pruning a bigger network were discussed in a recent article [ 5 ]. The question arises as to whether the solution to an audio signal multi-class classification task is still helpful to medical data and medical data analytics.…”
Section: Materials and Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…The advantages of pruning a bigger network were discussed in a recent article [ 5 ]. The question arises as to whether the solution to an audio signal multi-class classification task is still helpful to medical data and medical data analytics.…”
Section: Materials and Methodsmentioning
confidence: 99%
“…The question arises as to whether the solution to an audio signal multi-class classification task is still helpful to medical data and medical data analytics. The algorithm, as described in [ 5 ], requires a RBM trained in an unsupervised fashion to obtain the propagated values for each training sample from the different classes in the dataset. The outputs of each hidden neuron are then used to calculate their discriminative value.…”
Section: Materials and Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Both the growing and pruning approach based architectures repeat three key operations until acceptable performance (low complexity) is achieved [108]: i) training the model, ii) changing the weights (parameters) based on the growing or pruning criteria, and iii) re-training the model. In recent years, the field of growing and pruning has received considerable attention from the research community and several studies have discussed its effectiveness in various research domains, including health service improvement [109], self-care activities [108], and speech emotion recognition [110]. Therefore, the implementation of growing and pruning approaches for multiscale models in a smart city environment is still an open direction for researchers and industry.…”
Section: Growing and Pruning Multiscale Modelsmentioning
confidence: 99%
“…Pruning is the process of removing unused connections and, eventually, neurons from a network. It has been widely used in a range of speech processing tasks, for example in speech recognition [10,11,12]; denoising, and enhancement [13,14,15]; and, emotion recognition [16]. The efficacy of quantisation, a lowering in the resolution of a network's weights and biases, has been established in similar applications [17,18,19].…”
Section: Introductionmentioning
confidence: 99%