English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Music-video emotion is a high-level semantics of human internal feeling through singing music lyrics, musical instrument performance and visual expression. Any online and off-line music video are rich source to analysis the emotion using modern machine learning technologies. In this research we make music-video emotion dataset and extract music and video features from pre-trained neural networks. Two pre-trained audio and video networks are first fine-tuned and then extract the low level and high-level features. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short-term memory (LSTM) network is used for long-term dynamic feature characterization and then various machine learning algorithms evaluates emotions. We also use late fusion to fuse the learned features of audio and video network. The proposed network performs better for music-video emotion analysis. Introduction Music is a language that communicates some emotion to anyone, even to plants or animals. The music composers try to speak through musical instruments so exact music emotion measurement is not possible even for music experts. The music makers generally use dynamics (tempo, meters) or articulation to evoke emotions in music but everybody have different strategies to includes certain feel. The human emotion is a very vague and person dependent situation that may have influence of visual or acoustic information, human though and environmental changes. In music-video composition, the composer always tries to make some feel through musical instrument, singing methods and visual expression. The various music-video emotion expresses various high-level semantics of human moods. This makes the automatic music-video emotion content analysis highly challenging for computer. Human emotion representation is still an area of research and music video emotion is a specific to various human emotion specified in [1]. Individual audio or video emotion bases studies [6, 7] are done in past decades and in recently many [4, 8] research primarily focus on both audio and video emotion with early or late fusion. The primary limitation in this area of research is lack of labeled data and the methods based on data driven are not properly conducted. In this research our primary task is introduction of a small music-video dataset that can attract new researcher in this area and solve various problems related to music-video in television data center and several online video banks. Organization of the Text The DEAP [2] is a first music-video emotion annotated dataset with only 120 data sample, which is not enough for data driven emotion analysis. In this research we make six classes music-video emotion dataset including some DEAP dataset data samples. The number of class for our music-video emotion dataset are chosen according to [3, 4, 5] dataset representation but proper annotation of each data sample was great challenge to us. Finally, we make a dataset including various human emotion adjectives as in Table 1 and the V-A space representation is shown in Fig. 1.

Music-Video Emotion Analysis Using Late Fusion of Multimodal