With the rapid development in the field of artificial intelligence and natural language processing(NLP),research on music retrieval has gained importance.Music messages express emotional signals.The emotional classifi...With the rapid development in the field of artificial intelligence and natural language processing(NLP),research on music retrieval has gained importance.Music messages express emotional signals.The emotional classification of music can help in conveniently organizing and retrieving music.It is also the premise of using music for psychological intervention and physiological adjustment.A new chord-to-vector method was proposed,which converted the chord information of music into a chord vector of music and combined the weight of the Mel-frequency cepstral coefficient(MFCC) and residual phase(RP) with the feature fusion of a cochleogram.The music emotion recognition and classification training was carried out using the fusion of a convolution neural network and bidirectional long short-term memory(BiLSTM).In addition,based on the self-collected dataset,a comparison of the proposed model with other model structures was performed.The results show that the proposed method achieved a higher recognition accuracy compared with other models.展开更多
Music is the language of emotions.In recent years,music emotion recognition has attracted widespread attention in the academic and industrial community since it can be widely used in fields like recommendation systems...Music is the language of emotions.In recent years,music emotion recognition has attracted widespread attention in the academic and industrial community since it can be widely used in fields like recommendation systems,automatic music composing,psychotherapy,music visualization,and so on.Especially with the rapid development of artificial intelligence,deep learning-based music emotion recognition is gradually becoming mainstream.This paper gives a detailed survey of music emotion recognition.Starting with some preliminary knowledge of music emotion recognition,this paper first introduces some commonly used evaluation metrics.Then a three-part research framework is put forward.Based on this three-part research framework,the knowledge and algorithms involved in each part are introduced with detailed analysis,including some commonly used datasets,emotion models,feature extraction,and emotion recognition algorithms.After that,the challenging problems and development trends of music emotion recognition technology are proposed,and finally,the whole paper is summarized.展开更多
基金National Natural Science Foundation of China (No.61801106)。
文摘With the rapid development in the field of artificial intelligence and natural language processing(NLP),research on music retrieval has gained importance.Music messages express emotional signals.The emotional classification of music can help in conveniently organizing and retrieving music.It is also the premise of using music for psychological intervention and physiological adjustment.A new chord-to-vector method was proposed,which converted the chord information of music into a chord vector of music and combined the weight of the Mel-frequency cepstral coefficient(MFCC) and residual phase(RP) with the feature fusion of a cochleogram.The music emotion recognition and classification training was carried out using the fusion of a convolution neural network and bidirectional long short-term memory(BiLSTM).In addition,based on the self-collected dataset,a comparison of the proposed model with other model structures was performed.The results show that the proposed method achieved a higher recognition accuracy compared with other models.
基金supported by the National Nature Science Foundation of China (Grant Nos.61672144,61872072,61173029)the National Key R&D Program of China (2019YFB1405302)。
文摘Music is the language of emotions.In recent years,music emotion recognition has attracted widespread attention in the academic and industrial community since it can be widely used in fields like recommendation systems,automatic music composing,psychotherapy,music visualization,and so on.Especially with the rapid development of artificial intelligence,deep learning-based music emotion recognition is gradually becoming mainstream.This paper gives a detailed survey of music emotion recognition.Starting with some preliminary knowledge of music emotion recognition,this paper first introduces some commonly used evaluation metrics.Then a three-part research framework is put forward.Based on this three-part research framework,the knowledge and algorithms involved in each part are introduced with detailed analysis,including some commonly used datasets,emotion models,feature extraction,and emotion recognition algorithms.After that,the challenging problems and development trends of music emotion recognition technology are proposed,and finally,the whole paper is summarized.
