Development of music emotion classification system using convolution neural network

Chaudhary, Deepti; Singh, Niharika; Singh, Sukhwinder

doi:10.1007/s10772-020-09781-0

Cited by 27 publications

(14 citation statements)

References 28 publications

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“…Note recognition is the core and key aspect of music score recognition; Huang et al [12] proposed an endto-end detection model based on a deep convolutional neural network and feature fusion, and this model is able to directly process the entire image and then output the symbol categories and the pitch and duration of notes. Although the aforementioned research results can be innovative in the evaluation of symphony performance effect links, they still cannot effectively solve the intelligent analysis of performance effect evaluation and management [13][14][15][16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA‐MLP‐NN)

Yan

2021

Computational Intelligence and Neuroscience

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Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience’s evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA‐MLP‐NN)

Yan

2021

Computational Intelligence and Neuroscience

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“…Noroozi et al [33] imported acoustic information into a support vector machine to construct a Chinese music classification model. Chaudhary et al [34] extracted information from different audio tracks as the sample data of the BP neural network model for music emotion classification. Huang et al [35] used support vector machines to analyse music emotions by summarizing words with specific meanings.…”

Section: Complexitymentioning

confidence: 99%

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

2021

Complexity

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In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.

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“…The accuracy is 20.32% and 15.23% higher than those of the SVM with ReliefF (baseline) [44] and VGG-16, respectively. In comparison with Chaudhary [59], the accuracy of the proposed model improved by 3.65% and FLOPs of the model were reduced by 68%. In addition, comparing our model with CCP [54], the accuracy rose by 5.56%, and the training parameters and FLOPs by 89.8% and 89.3%, respectively.…”

Section: Results On the 4q Emotion Datasetmentioning

confidence: 86%

A Music Playback Algorithm Based on Residual-Inception Blocks for Music Emotion Classification and Physiological Information

Liao

Wang

Ruan

et al. 2022

Sensors

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Music can generate a positive effect in runners’ performance and motivation. However, the practical implementation of music intervention during exercise is mostly absent from the literature. Therefore, this paper designs a playback sequence system for joggers by considering music emotion and physiological signals. This playback sequence is implemented by a music selection module that combines artificial intelligence techniques with physiological data and emotional music. In order to make the system operate for a long time, this paper improves the model and selection music module to achieve lower energy consumption. The proposed model obtains fewer FLOPs and parameters by using logarithm scaled Mel-spectrogram as input features. The accuracy, computational complexity, trainable parameters, and inference time are evaluated on the Bi-modal, 4Q emotion, and Soundtrack datasets. The experimental results show that the proposed model is better than that of Sarkar et al. and achieves competitive performance on Bi-modal (84.91%), 4Q emotion (92.04%), and Soundtrack (87.24%) datasets. More specifically, the proposed model reduces the computational complexity and inference time while maintaining the classification accuracy, compared to other models. Moreover, the size of the proposed model for network training is small, which can be applied to mobiles and other devices with limited computing resources. This study designed the overall playback sequence system by considering the relationship between music emotion and physiological situation during exercise. The playback sequence system can be adopted directly during exercise to improve users’ exercise efficiency.

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Development of music emotion classification system using convolution neural network

Cited by 27 publications

References 28 publications

[Retracted] Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA‐MLP‐NN)

[Retracted] Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA‐MLP‐NN)

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

A Music Playback Algorithm Based on Residual-Inception Blocks for Music Emotion Classification and Physiological Information

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