Computing interfaces are becoming increasingly sophisticated, with systems that engage multiple sensory channels simultaneously. Deformable and shape-changing interfaces offer rich tactile experiences, but there is limited understanding of how they can be combined with other modes of sensory feedback. We systematically explored the audio, visual and tactile cross-modal correspondences of deformable shapes with a particular focus on auditory feedback. 50 participants were asked to associate deformable tactile stimuli, varying in stiffness and shape, with the sound qualities pitch, brightness, fade-in time and fade-out time, under visuo-tactile and tactile-only conditions. Our findings provide the first insights on how (1) shape, both its form and visibility, play a significant role in associations for pitch and brightness; (2) stiffness plays a dominant role in associations over a sound's fade-in and fade-out times. These findings are distilled into the first design guidelines for integrating auditory feedback into physical interfaces.

Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established and mature, offering the opportunity to observe designers’ work on highly demanding human-computer interfaces. This paper explores how and why digital luthiers choose their tools and how these tools relate to the challenges they face. Findings from 27 standardised open-ended interviews with prominent digital luthiers from commercial, research, independent and artistic backgrounds are analysed through reflexive thematic analysis. Our discussion explores their perspectives, finding that a process of pragmatic rationalisation and environmental influences play a significant role in tool selection. We also present how challenges faced by digital luthiers relate to social creativity and meta-design. These findings build upon the existing literature that examines the designer-tool relationship.

Abstract Manually configuring synthesizer parameters to reproduce a particular sound is a complex and challenging task. Researchers have previously used different optimization algorithms, including evolutionary algorithms to find optimal sound matching solutions. However, a major drawback to these algorithms is that they typically require large amounts of computational resources, making them slow to execute. This article proposes an optimized design for matching sounds generated by frequency modulation (FM) audio synthesis using the graphics processing unit (GPU). A benchmarking suite is presented for profiling the performance of three implementations: serial CPU, data‐parallel CPU, and data‐parallel GPU. Results have been collected and discussed from a high‐end NVIDIA desktop and a mid‐range AMD laptop. Using the default configuration for simple FM, the GPU accelerated design had a speedup of 128 over the naive serial implementation and 8.88 over the parallel CPU version on a desktop with an Intel i7 9800X CPU and NVIDIA RTX GeForce 2080Ti GPU. Furthermore, the relative speedup over the naive serial implementation continues to increase beyond simple FM to more advanced structures. Further observations include comparisons between integrated and discrete GPUs, toggling optimizations, and scaling evolutionary strategy population size.

With the ever-increasing complexity of sound synthesisers, there is a growing demand for automated parameter estimation and sound space navigation techniques. Recent research in this domain has focused on the application of general-purpose evolutionary algorithms to match specific types of target sounds. However, it is difficult to establish whether success or failure of a particular match is due to the inefficiency of the optimisation engine, or the limitations of the matching synthesiser. In this paper the distinction between optimiser inefficiency and synthesiser limitations is elucidated with a contrived target test methodology that enables the performance of different optimisation techniques to be measured and compared. The methodology is applied to a Frequency Modulation synthesiser, in order to compare the performance of different Evolution Strategy-based algorithms. The algorithm producing the best results with contrived targets is then used to match a non-contrived acoustic instrument tone.