Abstract

Abstract
Professional theatre actors are highly specialized in controlling their own expressive behaviour and non-verbal emotional expressiveness, so they are of particular interest in fields of study such as affective computing. We present Acting Emotions, an experimental protocol to investigate the physiological correlates of emotional valence and arousal within professional theatre actors. Ultimately, our protocol examines the physiological agreement of valence and arousal amongst several actors. Our main contribution lies in the open selection of the emotional set by the participants, based on a set of four categorical emotions, which are self-assessed at the end of each experiment. The experiment protocol was validated by analyzing the inter-rater agreement (> 0.261 arousal, > 0.560 valence), the continuous annotation trajectories, and comparing the box plots for different emotion categories. Results show that the participants successfully induced the expected emotion set to a significant statistical level of distinct valence and arousal distributions. © 2022 Owner/Author.

Abstract
Digital musical instruments have become increasingly prevalent in musical creation and production. Optimizing their usability and, particularly, their expressiveness, has become essential to their study and practice. The absence of multimodal feedback, present in traditional acoustic instruments, has been identified as an obstacle to complete performer-instrument interaction in particular due to the lack of embodied control. Mobile-based digital musical instruments present a particular case by natively providing the possibility of enriching basic auditory feedback with additional multimodal feedback. In the experiment presented in this article, we focused on using visual and haptic feedback to support and enrich auditory content to evaluate the impact on basic musical tasks (i.e., note pitch tuning accuracy and time). The experiment implemented a protocol based on presenting several musical note examples to participants and asking them to reproduce them, with their performance being compared between different multimodal feedback combinations. Collected results show that additional visual feedback was found to reduce user hesitation in pitch tuning, allowing users to reach the proximity of desired notes in less time. Nonetheless, neither visual nor haptic feedback was found to significantly impact pitch tuning time and accuracy compared to auditory-only feedback.

Abstract
Emotional Machines is an interactive installation that builds affective virtual environments through spoken language. In response to the existing limitations of emotion recognition models incorporating computer vision and electrophysiological activity, whose sources are hindered by a head-mounted display, we propose the adoption of speech emotion recognition (from the audio signal) and semantic sentiment analysis. In detail, we use two machine learning models to predict three main emotional categories from high-level semantic and low-level speech features. Output emotions are mapped to audiovisual representation by an end-To-end process. We use a generative model of chord progressions to transfer speech emotion into music and a synthesized image from the text (transcribed from the user's speech). The generated image is used as the style source in the style-Transfer process onto an equirectangular projection image target selected for each emotional category. The installation is an immersive virtual space encapsulating emotions in spheres disposed into a 3D environment. Thus, users can create new affective representations or interact with other previous encoded instances using joysticks. © 2022 Owner/Author.

Abstract
The extraction of harmonic information from musical audio is fundamental for several music information retrieval tasks. In this paper, we propose novel harmonic audio features based on the perceptually-inspired tonal interval vector space, computed as the Fourier transform of chroma vectors. Our contribution includes mid-level features for musical dissonance, chromaticity, dyadicity, triadicity, diminished quality, diatonicity, and whole-toneness. Moreover, we quantify the perceptual relationship between short- and long-term harmonic structures, tonal dispersion, harmonic changes, and complexity. Beyond the computation on fixed-size windows, we propose a context-sensitive harmonic segmentation approach. We assess the robustness of the new harmonic features in style classification tasks regarding classical music periods and composers. Our results align with, slightly outperforming, existing features and suggest that other musical properties than those in state-of-the-art literature are partially captured. We discuss the features regarding their musical interpretation and compare the different feature groups regarding their effectiveness for discriminating classical music periods and composers. © F. Almeida, G. Bernardes, and C. Weiû.

Abstract
Language is closely related to how we perceive ourselves and signify our reality. In this scope, we created Desiring Machines, an interactive media art project that allows the experience of affective virtual environments adopting speech emotion recognition as the leading input source. Participants can share their emotions by speaking, singing, reciting poetry, or making any vocal sounds to generate virtual environments on the run. Our contribution combines two machine learning models. We propose a long-short term memory and a convolutional neural network to predict four main emotional categories from high-level semantic and low-level paralinguistic acoustic features. Predicted emotions are mapped to audiovisual representations by an end-to-end process encoding emotion in virtual environments. We use a generative model of chord progressions to transfer speech emotion into music based on the tonal interval space. Also, we implement a generative adversarial network to synthesize an image from the transcribed speech-to-text. The generated visuals are used as the style image in the style-transfer process onto an equirectangular projection of a spherical panorama selected for each emotional category. The result is an immersive virtual space encapsulating emotions in spheres disposed into a 3D environment. Users can create new affective representations or interact with other previously encoded instances (This ArtsIT publication is an extended version of the earlier abstract presented at the ACM MM22 [1]). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.