Image Representation of Acoustic Features for the Automatic Recognition of Underwater Noise Targets

Classical Music Specific Mood Automatic Recognition Model Proposal

Electronics ◽

10.3390/electronics10202489 ◽

2021 ◽

Vol 10 (20) ◽

pp. 2489

Author(s):

Suyeon Lee ◽

Haemin Jeong ◽

Hyeyoung Ko

Keyword(s):

Classical Music ◽

Visual Media ◽

Automatic Recognition ◽

Effective Model ◽

Acoustic Features ◽

Segmentation Method ◽

Recognition Model ◽

The Subject

The purpose of this study was to propose an effective model for recognizing the detailed mood of classical music. First, in this study, the subject classical music was segmented via MFCC analysis by tone, which is one of the acoustic features. Short segments of 5 s or under, which are not easy to use in mood recognition or service, were merged with the preceding or rear segment using an algorithm. In addition, 18 adjective classes that can be used as representative moods of classical music were defined. Finally, after analyzing 19 kinds of acoustic features of classical music segments using XGBoost, a model was proposed that can automatically recognize the music mood through learning. The XGBoost algorithm that is proposed in this study, which uses the automatic music segmentation method according to the characteristics of tone and mood using acoustic features, was evaluated and shown to improve the performance of mood recognition. The result of this study will be used as a basis for the production of an affect convergence platform service where the mood is fused with similar visual media when listening to classical music by recognizing the mood of the detailed section.

Download Full-text

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Algorithms ◽

10.3390/a13030070 ◽

2020 ◽

Vol 13 (3) ◽

pp. 70 ◽

Cited By ~ 5

Author(s):

Kudakwashe Zvarevashe ◽

Oludayo Olugbara

Keyword(s):

Emotion Recognition ◽

Ensemble Learning ◽

Automatic Recognition ◽

Speech Emotion Recognition ◽

Spectral Features ◽

Intelligent Robot ◽

Acoustic Features ◽

Random Decision Forest ◽

Facial Images ◽

Decision Forest

Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.

Download Full-text