Music player based on emotion recognition of voice signals

Lukose, Sneha; Upadhya, Savitha S.

doi:10.1109/icicict1.2017.8342835

Cited by 20 publications

(7 citation statements)

References 9 publications

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“…The accuracy results of [9] and [16] for EMO-DB are 7.59% and 9.16% lower than the accuracy results of the 1BTPDN method. The accuracy result of [24] for EMO-DB is 1.16% and 10.46% lower than the accuracy result of 1BTPDN method.…”

Section: Discussionmentioning

confidence: 63%

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A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Sönmez

Varol

2020

IEEE Access

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Interpreting a speech signal is quite challenging because it consists of different frequencies and features that vary according to emotions. Although different algorithms are being developed in the speech emotion recognition (SER) domain, the success rates vary according to the spoken languages, emotions, and databases. In this study, a new lightweight effective SER method has been developed that has low computational complexity. This method, called 1BTPDN, is applied on RAVDESS, EMO-DB, SAVEE, and EMOVO databases. First, low-pass filter coefficients are obtained by applying a one-dimensional discrete wavelet transform on the raw audio data. The features are extracted by applying textural analysis methods, a one-dimensional local binary pattern, and a one-dimensional local ternary pattern to each filter. Using neighborhood component analysis, the most dominant 1024 features are selected from 7680 features while the other features are discarded. These 1024 features are selected as the input of the classifier which is a thirddegree polynomial kernel-based support vector machine. The success rates of the 1BTPDN reached 95.16%, 89.16%, 76.67%, and 74.31% in the RAVDESS, EMO-DB, SAVEE, and EMOVO databases, respectively. The recognition rates are higher compared to many textural, acoustic, and deep learning state-of-the-art SER methods. INDEX TERMS Discrete wavelet transform, local binary pattern, local ternary pattern, neighborhood component analysis, speech emotion recognition.

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Section: Discussionmentioning

confidence: 63%

“…• Time-Based Features They are zero-crossing rate (ZCR) [8] and amplitudebased features, such as amplitude descriptor, log attack time, attach, delay, sustain, release envelop, short-time energy (STE) [9], shimmer [10], rhythm-based features [8], [11], volume, and temporal centroid [3].…”

Section: Acoustic Features In Ser Literaturementioning

confidence: 99%

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Sönmez

Varol

2020

IEEE Access

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“…An emotion-specific multilevel dichotomous classification (EMDC) is employed to compare the performance with direct multiclass classification. Proposal [ 107 ], uses a speech emotion recognition (SER) system that captures human emotion using voice speech signals as an input. Five emotions are recognized, they are, anger, anxiety, boredom, happiness, and sadness.…”

Section: Types Of Activity Monitoring and Methodologiesmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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“…In contrast with existing state-of-the-art solutions, architecture proposed in other strategies improves recognition effectiveness by 64%. In 2017 Lukose et al [9], Griol et al [10], Mohammadi et al [12] used MFCC, endpoint detection feature selection methods i.e., SVM, ANN, Naive Bayes for SER modules. Finally, 76.31% of devices used the GM model and overall accuracy improved by 1.57% using SVM models.…”

Section: Emo-db Databasementioning

confidence: 99%

“…Each speech dataset is transferred through the pre-processing function to extract the necessary function vector for features from the data. The vector training set is passed on to the correct classifier and the classifier then forecasts the emotion to validate a model [9]. The identification of speech emotions is carried out in four significant steps to generate speech-based output, i.e., acquisition, processing, output generation and the application of the extracted voice feature.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Neural Network Based Speech Emotion Recognition for燬mart燗ssistance

Kumar¹,

Haq²,

Jain³

et al. 2023

Computers, Materials &Amp; Continua

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Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate and love. The key elements for the implementation of speech emotion recognition are voice processing, and once the emotion is recognized, the machine interface automatically detects the actions by buzzer and LED. The proposed system is trained and tested on various benchmark datasets, i.e., Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) database, Acoustic-Phonetic Continuous Speech Corpus (TIMIT) database, Emotional Speech database (Emo-DB) database and evaluated based on various parameters, i.e., accuracy, error rate, and time. While comparing with existing technologies, the proposed algorithm gave a better error rate and less time. Error rate and time is decreased by 19.79%, 5.13 s. for the RAVDEES dataset, 15.77%, 0.01 s for the Emo-DB dataset and 14.88%, 3.62 for the TIMIT database. The proposed model shows better accuracy of 81.02% for the RAVDEES dataset, 84.23% for the TIMIT dataset and 85.12% for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM) and Support Vector Machine (SVM) Model.

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Music player based on emotion recognition of voice signals

Cited by 20 publications

References 9 publications

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Multilayer Neural Network Based Speech Emotion Recognition for燬mart燗ssistance

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