Automated emotion recognition: Current trends and future perspectives

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.

/pdf/supervised-machine-learning-for-audio-emotion-recognition-24ve2h25uy.pdf

Supervised machine learning for audio emotion recognition

• A significant contribution of artificial emotional intelligence in recent years. • Comparative study of conventional and deep learning techniques. • Limitations and issues in existing techniques and how to overcome them. • Research gap and challenges to obtain better emotion recognition performance. • Research opportunities and future direction in artificial emotional intelligence. Artificial intelligence substantially changes the global world, influencing technologies, machines, and objects in various encouraging aspects nowadays; emotion recognition is also one of them. This paper describes a significant contribution of emotion recognition by applying conventional and deep learning methodologies by focusing on limitations and demanding challenges. It also intends to explore the comparative study on recently applied machine learning and deep learning-based algorithms, which provide the best accuracy rates to recognize emotions. This Comparative study consists of different feature extractions, classifier models, and datasets that recognize the emotions within a facial image, speech, and non-verbal communication and describes their features and principles for future research work. We have shown the balancing accuracy, and efficiency of using hybrid classification techniques briefly explained in Speech emotion recognition. This review study would be more beneficial in enhancing automated decision-making services in various customer-based industries and observing patients in the health care sector, industries, public sectors, private sectors, and production firms. 

Artificial Emotional Intelligence: Conventional and deep learning approach

Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a collection of observable features that are taken from user data. Machine learning and neural network-based systems represent a topic of study that spans several fields. Computers can now recognize the emotions behind particular content uploaded by users to social media networks thanks to machine learning. This study examines research on machine learning and neural networks, with an emphasis on social analysis in the context of the current literature.

Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks

The goal of this study was to learn what properties of sound affect human focus the most. Participants (N=62, 18-65y) performed various tasks while listening to either no background sound (silence), popular music playlists for increasing focus (pre-recorded songs), or personalized soundscapes (audio composed in real-time to increase a specific individual’s focus). While performing tasks on a tablet, participants wore headphones and brain signals were recorded using a portable electroencephalography headband. Participants completed four one-hour long sessions, each with different audio content, at home. We successfully generated brain-based models to predict individual participant focus levels over time and used these models to analyze the effects of various audio content during different tasks. We found that while participants were working, personalized soundscapes increased their focus significantly above silence (p=0.008), while music playlists did not have a significant effect. For the young adult demographic (18-36y), silence was significantly less effective at producing focus than audio content of any type tested (p=0.001-0.009). Personalized soundscapes enhanced focus the most relative to silence, but professionally crafted playlists of pre-recorded songs also increased focus during specific time intervals, especially for the youngest audience demographic. We also found that focus levels can be predicted from physical properties of sound, enabling human and artificial intelligence composers to test and refine audio to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research includes real-time adjustment of sound for other functional objectives, such as affecting listener enjoyment, calm, or memory.
 “Now I will do nothing but listen… I hear all sounds running together, combined, fused or following, Sounds of the city and sounds out of the city, sounds of the day and night… I hear the key’d cornet, it glides quickly in through my ears, It shakes mad-sweet pangs through my belly and breast. I hear the chorus, it is a grand opera, Ah this indeed is music - this suits me.”1 WALT WHITMAN, Song of Myself

/pdf/differences-in-the-effects-on-human-focus-of-music-playlists-4h5r6bsvi1.pdf

Differences in the effects on human focus of music playlists and personalized soundscapes, as measured by brain signals.

In recent years, the field of Music Emotion Recognition has become established. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. Audio features are extracted and used as the input to two machine-learning approaches: regression modelling and artificial neural networks, in order to predict the emotional dimensions of arousal and valence. It is found that shallow neural networks perform better than a range of regression models. Consistent with existing research in emotion recognition, prediction of arousal is more reliable than that of valence. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.

Audio Emotion Recognition using Machine Learning to support Sound Design

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Supervised machine learning for audio emotion recognition

Audio Emotion Recognition using Machine Learning to support Sound Design