Audio Fingerprinting Using High-Level Feature Extraction

2021 ◽  
pp. 281-291
Author(s):  
Aousnik Gupta ◽  
Affifur Rahman ◽  
Ghazaala Yasmin
Keyword(s):  
Feature Extraction ◽  
Audio Fingerprinting ◽  
High Level ◽  
High Level Feature

The application of graph diffusion in high-level feature extraction

2013 ◽  
Author(s):  
Xiaohan Du ◽  
Honggang Zhang ◽  
Jun Guo ◽  
Xiaojun Xu
Keyword(s):  
Feature Extraction ◽  
High Level ◽  
High Level Feature

High-level feature extraction in JPEG compressed domain

2004 ◽  
Author(s):  
C. K. Narayanan ◽  
M. C. Prakash ◽  
G. V. Prabhakara Rao
Keyword(s):  
Feature Extraction ◽  
Compressed Domain ◽  
High Level ◽  
High Level Feature

scholarly journals A Hybrid Technique using CNN+LSTM for Speech Emotion Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 1126-1130
Keyword(s):  
Feature Extraction ◽  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Hybrid Technique ◽  
Proposed Model ◽  
High Level ◽  
High Level Feature ◽  
Convolutional Lstm

Automatic speech emotion recognition is a very necessary activity for effective human-computer interaction. This paper is motivated by using spectrograms as inputs to the hybrid deep convolutional LSTM for speech emotion recognition. In this study, we trained our proposed model using four convolutional layers for high-level feature extraction from input spectrograms, LSTM layer for accumulating long-term dependencies and finally two dense layers. Experimental results on the SAVEE database shows promising performance. Our proposed model is highly capable as it obtained an accuracy of 94.26%.

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