An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model

Agarwal, Gaurav; Om, Hari

doi:10.1049/sil2.12015

Cited by 21 publications

(11 citation statements)

References 33 publications

(42 reference statements)

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“…Reference [ 18 ] used the improved back propagation neural network to analyze music data and introduced the artificial bee colony algorithm to improve the structure of BP neural network and realize emotion recognition and classification. Reference [ 19 ] adopted the support vector regression model based on the optimization of automatic encoder to develop a framework for emotion recognition to realize the music emotion recognition. In the stage of feature extraction, reference [ 20 ] used a random convolution neural network to analyze the music emotion of the input Mel spectrum, so as to improve the accuracy of corresponding emotion identification.…”

Section: Related Workmentioning

confidence: 99%

Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network

Jia

2022

Computational Intelligence and Neuroscience

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In order to improve the accuracy of music emotion recognition and classification, this study combines an explicit sparse attention network with deep learning and proposes an effective emotion recognition and classification method for complex music data sets. First, the method uses fine-grained segmentation and other methods to preprocess the sample data set, so as to provide a high-quality input data sample set for the classification model. The explicit sparse attention network is introduced into the deep learning network to reduce the influence of irrelevant information on the recognition results and improve the emotion classification and recognition ability of music sample data set. The simulation experiment is based on the actual data set of the network. The experimental results show that the recognition accuracy of the proposed method is 0.71 for happy emotions and 0.688 for sad emotions. It has a good ability of music emotion recognition and classification.

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Section: Related Workmentioning

confidence: 99%

Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network

Jia

2022

Computational Intelligence and Neuroscience

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“…Based on the above analysis, the dance movement characteristics are regarded as a set containing positive and negative sample data [11], which is set as…”

Section: Dance Movement Feature Extractionmentioning

confidence: 99%

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Zhu

2022

Security and Communication Networks

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Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.

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“…The analysis of different moods through music was implemented in Gaurav and Hari 25 through an optimized model of SVR (support vector regression) using an autoencoder. The main objective of this research was to understand different music moods by gathering the features of text and nontext dependent.…”

Section: Related Workmentioning

confidence: 99%

A learning framework of modified deep recurrent neural network for classification and recognition of voice mood

Agarwal

Gupta

2022

Adaptive Control & Signal

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Recognition of human emotions is a basic requirement in many real-time applications. Detection of exact emotions through voice provides relevant information for various purposes. Several computational methods have been employedfor the clear analysis of human emotions. Most of the previous approaches face complexities due to certain drawbacks like degraded signal quality, a requirement of high storage space, increased computational complexity, and deteriorated outcomes of classification accuracy. The proposed work was implemented to gather the accurate classification result of embedded emotions and minimize the computational complexities of MDDTRNN (modified deep duck and traveler recurrent neural network). The proposed work includes four steps: preprocessing, feature extraction, feature selection, and classification. In feature extraction, the spectral and frequency features are extracted using the adopting boosted MFCC (Mel frequency cepstral coefficients) method to improve training speed. In feature selection, the best features are selected using an algorithm of AAVOA (adaptive African vulture optimization algorithm). To provide optimal emotion results, the classification step is undertaken by the MDDTRNN technique. The proposed work shows better classification outcomes of emotions when compared to the existing approaches by holding the accuracy of (95.86%), precision as (93.79%), specificity as (94.28%), sensitivity as (92.89%) and the error rate is attained to be 5.266 in terms of IEMOCAP dataset. The accuracy result (96.27%), precision (94.83%), specificity (93.16%), sensitivity (94%) and the error rate is achieved to be 4.982 in terms of the EMODB dataset.

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An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model

Cited by 21 publications

References 33 publications

Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network

Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

A learning framework of modified deep recurrent neural network for classification and recognition of voice mood

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