Speech Emotion Recognition Using K-Nearest Neighbor Classifiers

2021 ◽  
pp. 123-131
Author(s):  
M. Venkata Subbarao ◽  
Sudheer Kumar Terlapu ◽  
Nandigam Geethika ◽  
Kudupudi Durga Harika
Keyword(s):  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Speech Emotion Recognition ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers

scholarly journals Song Recommendation Application Using Speech Emotion Recognition

2021 ◽  
Vol 10 (1) ◽  
pp. 15-22
Author(s):  
Eko Budi Setiawan ◽  
Al Ghani Iqbal Dzulfiqar
Keyword(s):  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Text Message ◽  
Functional Test ◽  
Speech Emotion Recognition ◽  
K Nearest Neighbor ◽  
Spoken Words ◽  
The Voice

This research was conducted to facilitate the interaction between radio broadcasters and radio listeners during the song request process.  This research was triggered by the difficulty of the broadcasters in monitoring song requests from listeners. The system is made to accommodate all song requests by listeners. The application produced in this study uses speech emotion recognition technology based on a person's mood obtained from the spoken words.  This technology can change the voice into one of the mood categories: neutral, angry, sad, and afraid.  The k-Nearest Neighbor method is used to get recommendations for recommended song titles by looking for the closeness of the value between the listener's mood and the availability of song playlists. kNN is used because this method is suitable for user-based collaborative problems. kNN will recommend three songs which then be offered to listeners by broadcasters. Based on tests conducted to the broadcasters and radio listeners, this study has produced a song request application by recommending song titles according to the listener's mood,  the text message, the searching songs, and the song requests and the song details that have been requested. Functional test that has been carried out has received 100 because all test components have succeeded as expected.

scholarly journals Two Stepped Majority Voting for Efficient EEG based Emotion Classification

2020 ◽  
Author(s):  
aras Masood Ismael ◽  
Ömer F Alçin ◽  
Karmand H Abdalla ◽  
Abdulkadir k sengur
Keyword(s):  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Noise Removal ◽  
Majority Voting ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
Discrimination Accuracy ◽  
Novel Approach ◽  
Knn Classifier ◽  
Low Pass

Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG based emotion classification. Emotion recognition is important for human-machine interactions. Facial-features and body-gestures based approaches have been generally proposed for emotion recognition. Recently, EEG based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG based emotion classification.

Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers

2010 ◽  
Vol 5 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Mohammed J. Islam ◽  
Q. M. Jonathan Wu ◽  
Majid Ahmadi ◽  
Maher A. SidAhmed
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers

scholarly journals Speculate-correct error bounds for k-nearest neighbor classifiers

2019 ◽  
Vol 108 (12) ◽  
pp. 2087-2111 ◽  
Author(s):  
Eric Bax ◽  
Lingjie Weng ◽  
Xu Tian
Keyword(s):  
Error Bounds ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers
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