Music Emotion Recognition Based on Feature Fusion Broad Learning Method

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.

Style-conditioned music generation with Transformer-GANs

Recently,various algorithms have been developed for generating appealing music.However,the style control in the generation process has been somewhat overlooked.Music style refers to the representative and unique appearance presented by a musical work,and it is one of the most salient qualities of music.In this paper,we propose an innovative music generation algorithm capable of creating a complete musical composition from scratch based on a specified target style.A style-conditioned linear Transformer and a style-conditioned patch discriminator are introduced in the model.The style-conditioned linear Transformer models musical instrument digital interface(MIDI)event sequences and emphasizes the role of style information.Simultaneously,the style-conditioned patch discriminator applies an adversarial learning mechanism with two innovative loss functions to enhance the modeling of music sequences.Moreover,we establish a discriminative metric for the first time,enabling the evaluation of the generated music’s consistency concerning music styles.Both objective and subjective evaluations of our experimental results indicate that our method’s performance with regard to music production is better than the performances encountered in the case of music production with the use of state-of-the-art methods in available public datasets.

A survey of music emotion recognition

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.

Music and the Representation of Emotion

The claim that many musical works are representational is highly controversial. The formalist view that music is pure form and without any, or any significant, representational content is widety held. Two facts about music are, however, well-established by empirical science： Music is heard as resembling human expressive behaviour and music arouses ordinary emotions. This paper argues that it follows from these facts that music also represents human expressive behaviour and ordinary emotions.

The impact of consistency between the emotional feature of advertising music and brand personality on brand experience

Music in advertising plays a crucial role in making the audience feel beyond the multi-level visual experience.The intrinsic link between brand publicity and advertising music has long been a puzzle.This paper discusses the impact of the consistency between the emotional characteristics of music and brand personality on brand experience and expands the discussion to brand experience under market competition.We use the examples of Canon and Apple for our study.The results shows that:(1)the higher the degree of consistency between the emotional experience from music and brand personality,the greater the positive effect on brand experience;(2)this positive effect is not as significant for functional brands as it is for representative brands;(3)the consistency between the emotional experience from music and brand personality has a greater impact on brand experience for representative brands than functional brands.The results provide practical guidance for branding campaigns.