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.

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 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.

scholarly journals Bag-of-words from image to speech: a multi-classifier emotions recognition system

2020 ◽  
Vol 9 (3) ◽  
pp. 770 ◽  
Author(s):  
Mai Ezz-Eldin ◽  
Hesham F. A. Hamed ◽  
Ashraf A. M. Khalaf
Keyword(s):  
Emotion Recognition ◽  
Emotional Content ◽  
Speech Emotion Recognition ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Mel Frequency Cepstral Coefficients ◽  
Research Attention ◽  
Extreme Gradient Boosting ◽  
Bow Technique

Recently, recognizing the emotional content of speech signals has received considerable research attention. Consequently, systems have been developed to recognize the emotional content of a spoken utterance. Achieving high accuracy in speech emotion recognition remains a challenging problem due to issues related to feature extraction, type, and size. Central to this study is increasing emotion recognition accuracy by porting the bag-of-word (BoW) technique from image to speech for feature processing and clustering. The BoW technique is applied to features extracted from Mel frequency cepstral coefficients (MFCC) which enhances feature quality. The study considers deployment of different classification approaches to examine the performance of the embedded BoW approach. The deployed classifiers include support vector machine (SVM), K-nearest neighbor (KNN), naive Bays (NB), random forest (RF), and extreme gradient boosting (XGBoost). In this study, experiments used the standard RAVDESS audio dataset with eight emotions: angry, calm, happy, surprised, sad, disgusted, fearful and neutral. The maximum accuracy obtained in the angry class using SVM was 85%, while overall accuracy was 80.1 %. The empirical works have proved that using BoW achieves better results in terms of accuracy and processing time compared to other available methods.

scholarly journals Speech emotion recognition based on SVM and KNN classifications fusion

2021 ◽  
Vol 11 (2) ◽  
pp. 1259
Author(s):  
Mohammed Jawad Al Dujaili ◽  
Abbas Ebrahimi-Moghadam ◽  
Ahmed Fatlawi
Keyword(s):  
Speech Processing ◽  
English Language ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Zero Crossing ◽  
Wide Range

Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons.

scholarly journals Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

2020 ◽  
Vol 5 (6) ◽  
pp. 1082-1088
Author(s):  
Anton Yudhana ◽  
Akbar Muslim ◽  
Dewi Eko Wati ◽  
Intan Puspitasari ◽  
Ahmad Azhari ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Eeg Signal ◽  
K Nearest Neighbor ◽  
Human Emotion
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