Pengenalan Emosi Berdasarkan Suara Menggunakan Algoritma HMM

Prasetio, Barlian Henryranu; Kurniawan, Wijaya; Ichsan, Mochammad Hannats Hanafi

doi:10.25126/jtiik.201743339

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…Emosi merupakan perasaan yang terjadi pada suatu individu yang melibatkan perubahan fisik maupun karakter seperti denyut nadi, produksi kelenjar, suara dan sebagainya serta biassanya ditujukan terhadap individu yang lain atau sesuatu yang dapat diartikan juga sebagai kondisi reaksi yang ditimbulkan akibat perbuatan atau kejadian tertentu. adapun dari sudut mental dapat ditandai dengan perasaan yang kuat daripada suatu keadaan senang atau cemas yang biasanya didorong menuuju bentuk nyata dari tingkah laku organisme itu sendiri (Nadhiroh, 2015;Prasetio and Kurniawan, 2017).…”

Section: Pengenalan Emosiunclassified

“…Sejak lahir manusia telah dikenalkan dan secara tidak langsung belajar mengenai kemampuan emosi yang dapat dirasakan oleh manusia tersebut. Emosi merupakan suatu perasaan yang dirasakan oleh seseorang dan dapat ditujukan kepada orang lain atau sesuatu dan dapat timbul sebagai suatu reaksi akibat kejadian tertentu (Prasetio and Kurniawan, 2017). menurut A. Noguieras pada Barlian terdapat penelitian yang menyatakan bahwa beberapa parameter statistik memiliki korelasi yang tinggi antara ucapan dengan keadaan emosional diantaranya seperti pitch, energi, artikulasi dan bentuk spektral yang secara tidak lansgung dapat mempengaruhi keadaan suara yang diidentifikasi (Prasetio and Kurniawan, 2017).…”

Section: Introductionunclassified

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Mengenali pengaruh emosional pada suara manusia menggunakan metode Fast Fourier Transform (FFT)

Ummami¹

2019

INISTA

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Emotion is a feeling that is felt by someone and can be addressed to other people or something and can arise as a reaction due to certain events. Some previous studies have stated that some statistical parameters have a high correlation between speech and emotional states that can indirectly influence the state of sound identified. In this study a simple experiment was carried out by taking nine samples with three different emotional characters namely sad, happy, and angry taken from three respondents who were randomly selected with the aim of identifying differences in emotions generated through sound data using the Fast Fourier Transform method ( FFT).

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Section: Pengenalan Emosiunclassified

Section: Introductionunclassified

Mengenali pengaruh emosional pada suara manusia menggunakan metode Fast Fourier Transform (FFT)

Ummami¹

2019

INISTA

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“…Pada MOODSIC versi pertama dikembangkan menggunakan metode eigenface dengan performa yang belum optimal dengan akurasi dibawah 90%. Metode eigenface merupakan suatu representasi wajah menggunakan Principal Component Analysis (PCA) yang banyak diaplikasikan untuk pengenalan wajah karena mudah diaplikasikan (Pratiwi & Harjoko, 2013), (Wijaya dkk, 2013 (Prasetio et al 2017). Oleh karena itu, penelitian mengenai pengenalan ekspresi wajah masih sangat menantang mengingat belum optimalnya akurasi dari metode sebelumnya.…”

Section: Pendahuluanunclassified

Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC)

Wijaya

Firdaus

Dwitama

et al. 2018

JTIIK

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Masyarakat modern dengan kesibukan sehari-harinya tentu akan mendapat tekanan emosional yang cukup tinggi. Hal yang dilakukan untuk meredakan emosi tersebut adalah salah satu dengan mendengarkan musik. MOODSIC merupakan sebuah aplikasi yang dapat memutar musik sesuai dengan ekspresi wajah pengguna. Aplikasi MOODSIC dibangun menggunakan mesin pengenalan ekspres wajah berbasis DCT dan LDA serta algoritma klasifikasi statistik. Berdasarkan hasil pengujian secara off-line mesin pengenalan ekspresi wajah berhasil memberikan performa yang baik, dengan akurasi sebesar 100% untuk data masukkan terdiri atas fitur DCT 144 elemen, 6 eigen vektor LDA dan klasifikasi statistik jenis LDA. Mesin pengenalan ekspresi wajah memerlukan waktu pengenalan yang pendek yaitu 1 milidetik. Secara real-time MOODSIC memberikan hasil yang cukup baik dengan akurasi pengenalan ekspresi sebesar 91.51% atau dengan tingkat kesalahan pengenalan 9.49%. AbstractModern society lifestyles face many activities every day, which make people receive a fairly high emotional stress. To reduce such kind of emotions can be treated by listening music. MOODSIC is an application that can play music according to the user's face expression. MOODSIC is developed using face expression recognition machine based on DCT, LDA and statistical classification algorithm. Based on offline testing result, face expression recognition machine successfully give good performance with accuracy of 100% when DCT features are 144 elements, 6 eigen vectors of LDA and kind of statistical classifier is LDA. The face expression recognition engine took shorter time to classification about 1 milliseconds. MOODSIC also give good performance with the accuracy of expression recognition about 91.51% or recognition error of 9,49% for real-time testing.

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“…It helps develop the interface of software that is applicable in community service centers, banks, and education and others. The main component of emotion recognition is feature extraction and classification [5]. Selection of the right feature extraction algorithm is the main focus of SER activities.…”

Section: Introductionmentioning

confidence: 99%

“…The observed characteristics include the pitch of the voice, length of sounds, loudness, and timbre. A study to recognize human voice speech emotions used Hidden Markov Model (HMM) to analyze the pitch, energy, and formant [5]. Another research extracted energy feature, pitch, ZCC, and entropy and examined the extracted measure using the Mel Frequency Cepstrum Coefficients (MFCC) and the K-Nearest Neighbor (KNN) classification [6].…”

Section: Introductionmentioning

confidence: 99%

Speech Classification to Recognize Emotion Using Artificial Neural Network

Helmiyah

Riadi

Umar

et al. 2021

khif

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This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centers, banks, education, and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral, and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.

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Pengenalan Emosi Berdasarkan Suara Menggunakan Algoritma HMM

Cited by 5 publications

References 3 publications

Mengenali pengaruh emosional pada suara manusia menggunakan metode Fast Fourier Transform (FFT)

Mengenali pengaruh emosional pada suara manusia menggunakan metode Fast Fourier Transform (FFT)

Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC)

Speech Classification to Recognize Emotion Using Artificial Neural Network

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