We introduce Prompt-to-Touch, a proof-of-concept of a multi-step pipeline that generates haptic effects based on the textual description of a haptic experience. Our pipeline first translates the haptic effect description to a sound effect description using our Foley-Interpreter component. It then uses a text-to-audio model to generate a sound effect from the sound description. Afterwards, the sound effect is converted into a perceivable haptic effect using our Dynamic-Audio-Processor . Finally, the haptic effect is post-processed to compensate for actuator-specific frequency response characteristics. We validate our concept in two preliminary human evaluation studies (n = 20, n = 10) and a technical analysis. Our results indicate that our pipeline can generate effects to enhance immersive multimedia experiences, abstract desktop/XR interactions, and social communication applications. They also still reveal significant potential for further improvement. Our pipeline could be used to develop future text-driven haptic design and automation tools. We provide open-source code to support future extensions.

Sound morphing is the process of gradually and smoothly transforming one sound into another to generate novel and perceptually hybrid sounds that simultaneously resemble both. Recently, diffusion-based text-to-audio models have produced high-quality sounds using text prompts. However, granularly controlling the semantics of the sound, which is necessary for morphing, can be challenging using text. In this paper, we propose MorphFader, a controllable method for morphing sounds generated by disparate prompts using text-to-audio models. By intercepting and interpolating the components of the cross-attention layers within the diffusion process, we can create smooth morphs between sounds generated by different text prompts. Using both objective metrics and perceptual listening tests, we demonstrate the ability of our method to granularly control the semantics in the sound and generate smooth morphs.

The practice of sound design involves creating and manipulating environmental sounds for music, films, or games. Recently, an increasing number of studies have adopted generative AI to assist in sound design co-creation. Most of these studies focus on the needs of novices, and less on the pragmatic needs of sound design practitioners. In this paper, we aim to understand how generative AI models might support sound designers in their practice. We designed two interactive generative AI models as Creative Support Tools (CSTs) and invited nine professional sound design practitioners to apply the CSTs in their practice. We conducted semi-structured interviews and reflected on the challenges and opportunities of using generative AI in mixed-initiative interfaces for sound design. We provide insights into sound designers’ expectations of generative AI and highlight opportunities to situate generative AI-based tools within the design process. Finally, we discuss design considerations for human-AI interaction researchers working with audio.

Controllable generation in StyleGANs is usually achieved by training the model using labeled data. For audio textures, however, there is currently a lack of large semantically labeled datasets. Therefore, to control generation, we develop a method for semantic control over an unconditionally trained StyleGAN in the absence of such labeled datasets. In this paper, we propose an example-based framework to determine guidance vectors for audio texture generation based on user-defined semantic attributes. Our approach leverages the semantically disentangled latent space of an unconditionally trained StyleGAN. By using a few synthetic examples to indicate the presence or absence of a semantic attribute, we infer the guidance vectors in the latent space of the StyleGAN to control that attribute during generation. Our results show that our framework can find user-defined and perceptually relevant guidance vectors for controllable generation for audio textures. Furthermore, we demonstrate an application of our framework to other tasks, such as selective semantic attribute transfer.

In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes. We demonstrate that interpolation between such conditions or control vectors provide smooth morphing between the generated audio textures, and show similar or better audio texture morphing capability compared to the state-of-the-art methods. The proposed approach results in a well-organized latent space that generates novel audio outputs while remaining consistent with the semantics of the conditioning parameters. This is a step towards a general data-driven approach to designing generative audio models with customized controls capable of traversing out-of-distribution regions for novel sound synthesis.

Novel AI-generated audio samples are evaluated for descriptive qualities such as the smoothness of a morph using crowdsourced human listening tests. However, the methods to design interfaces for such experiments and to effectively articulate the descriptive audio quality under test receive very little attention in the evaluation metrics literature. In this paper, we explore the use of visual metaphors of image-schema to design interfaces to evaluate AI-generated audio. Furthermore, we highlight the importance of framing and contextualizing a descriptive audio quality under measurement using such constructs. Using both pitched sounds and textures, we conduct two sets of experiments to investigate how the quality of responses vary with audio and task complexities. Our results show that, in both cases, by using image-schemas we can improve the quality and consensus of AI-generated audio evaluations. Our findings reinforce the importance of interface design for listening tests and stationary visual constructs to communicate temporal qualities of AI-generated audio samples, especially to naïve listeners on crowdsourced platforms.