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
AbstractWe advance Mixmash-AIS, a multimodal optimization music mashup creation model for loop recombination at scale. Our motivation is to (1) tackle current scalability limitations in state-of-the-art (brute force) computational mashup models while enforcing the (2) compatibility of audio loops and (3) a pool of diverse mashups that can accommodate user preferences. To this end, we adopt the artificial immune system (AIS) opt-aiNet algorithm to efficiently compute a population of compatible and diverse music mashups from loop recombinations. Optimal mashups result from local minima in a feature space representing harmonic, rhythmic, and spectral musical audio compatibility. We objectively assess the compatibility, diversity, and computational performance of Mixmash-AIS generated mashups compared to a standard genetic algorithm (GA) and a brute force (BF) approach. Furthermore, we conducted a perceptual test to validate the objective evaluation function within Mixmash-AIS in capturing user enjoyment of the computer-generated loop mashups. Our results show that while the GA stands as the most efficient algorithm, the AIS opt-aiNet outperforms both the GA and BF approaches in terms of compatibility and diversity. Our listening test has shown that Mixmash-AIS objective evaluation function significantly captures the perceptual compatibility of loop mashups (p < .001).

Abstract
In this article, a proof-of-concept study on the use of a hybrid design technique to reduce the number of phase shifters of a beam-scanning reflectarray (RA) is presented. An extended hemispherical lens antenna with feeds inspired by the retrodirective array is developed as a reflecting element, and the hybrid design technique mixes the lenses with the microstrip patch elements to realize a reflecting surface. Compared to the conventional designs that only use microstrip antennas to realize a reflecting surface, given a fixed aperture size the presented design uses 25 percent fewer array elements while shows comparable beam-steering performance. As a result of using fewer elements, the number of required phase shifters or other equivalent components such as RF switches and tunable materials is reduced by 25 percent, which leads to the reduction of the overall antenna system's complexity, cost, and power consumption. To verify the design concept, two passive prototypes with a center frequency at 12.5 GHz were designed and fabricated. The reflecting surface was fabricated by using standard PCB manufacturing and the lenses were fabricated using 3D printing. Good agreement between the simulation and measurement results is obtained. The presented design concept can be extended to the design of RAs operating at different frequency bands including millimetre-wave frequencies with similar radiation performances. The presented design method is not limited to the microstrip patch reflecting elements and can also be applied to the design of the hybrid RAs with different types of reflecting elements.

Abstract

Abstract