Speech Emotion Recognition Using Feature Fusion of TEO and MFCC on Multilingual Databases

Qadri, Syed Asif Ahmad; Gunawan, Teddy Surya; Kartiwi, Mira; Mansor, Hasmah

doi:10.1007/978-981-33-4597-3_61

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Furthermore, MFCC-based background music processing techniques have been applied to analyze the mood and emotions of background music in films and videos [8]- [9], [10]. By classifying the emotional states of movie background music based on features extracted through MFCC, it is possible to analyze the overall emotional flow of the movie.…”

Section: A Mfcc Technique For Background Music Processingmentioning

confidence: 99%

Emotion Recognition and Multi-class Classification in Music with MFCC and Machine Learning

Yoo,

Hong,

Kim

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Background music in OTT services significantly enhances narratives and conveys emotions, yet users with hearing impairments might not fully experience this emotional context. This paper illuminates the pivotal role of background music in user engagement on OTT platforms. It introduces a novel system designed to mitigate the challenges the hearing-impaired face in appreciating the emotional nuances of music. This system adeptly identifies the mood of background music and translates it into textual subtitles, making emotional content accessible to all users. The proposed method extracts key audio features, including Mel Frequency Cepstral Coefficients (MFCC), Root Mean Square (RMS), and MEL Spectrograms. It then harnesses the power of leading machine learning algorithms Logistic Regression, Random Forest, AdaBoost, and Support Vector Classification (SVC) to analyze the emotional traits embedded in the music and accurately identify its sentiment. Among these, the Random Forest algorithm, applied to MFCC features, demonstrated exceptional accuracy, reaching 94.8% in our tests. The significance of this technology extends beyond mere feature identification; it promises to revolutionize the accessibility of multimedia content. By automatically generating emotionally resonant subtitles, this system can enrich the viewing experience for all, particularly those with hearing impairments. This advancement not only underscores the critical role of music in storytelling and emotional engagement but also highlights the vast potential of machine learning in enhancing the inclusivity and enjoyment of digital entertainment across diverse audiences.

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Section: A Mfcc Technique For Background Music Processingmentioning

confidence: 99%

Emotion Recognition and Multi-class Classification in Music with MFCC and Machine Learning

Yoo,

Hong,

Kim

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Wang et al (2022), the multi-resolution idea of wavelet analysis is combined with different forms of TEO and MFCC, and five non-linear features are proposed for speech emotion recognition. Qadri et al (2022) proposed that Teager-energy based MFCC (TEMFCCs) was classified on Berlin database by Gaussian mixture model (GMM), and experimental results showed that TEMFCCs had better performance than MFCC. In order to facilitate the subsequent speech emotion recognition, the speech processing tool OpenSMILE is used to extract the speech features, and the extracted features are saved as .csv files.…”

Section: Features Of Dance Emotionmentioning

confidence: 99%

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Shen

Xiao-xi²,

Jiang³

et al. 2022

Front. Neurorobot.

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Dance emotion recognition is an important research direction of automatic speech recognition, especially in the robot environment. It is an important research content of dance emotion recognition to extract the features that best represent speech emotion and to construct an acoustic model with strong robustness and generalization. The dance emotion data set is small in size and high in dimension. The traditional recurrent neural network (RNN) has the problem of long-range dependence disappearance, and due to the focus on local information of convolutional neural network (CNN), the mining of potential relationships between frames in the input sequence is insufficient. To solve the above problems, this paper proposes a novel linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory (LSTM) for dance emotion recognition. In this paper, the linear prediction coefficient (LPC) and Meier frequency cepstrum coefficient (MFCC) are combined to obtain a new feature, namely the linear prediction Meier frequency cepstrum coefficient (LPMFCC). Then, the combined feature obtained by combining LPMFCC with energy feature is used as the extracted dance feature. The extracted features are input into the bidirectional LSTM network for training. Finally, support vector machine (SVM) is used to classify the obtained features through the full connection layer. Finally, we conduct experiments on public data sets and obtain the better effectiveness compared with the state-of-art dance motion recognition methods.

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AudioFormer: Channel Audio Encoder Based on Multi-granularity Features

Wang,

Xu,

Miao

et al. 2023

Communications in Computer and Information Science

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Speech Emotion Recognition Using Feature Fusion of TEO and MFCC on Multilingual Databases

Cited by 6 publications

References 8 publications

Emotion Recognition and Multi-class Classification in Music with MFCC and Machine Learning

Emotion Recognition and Multi-class Classification in Music with MFCC and Machine Learning

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

AudioFormer: Channel Audio Encoder Based on Multi-granularity Features

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