Embodiment can enhance conversational agents, such as increasing their perceived presence. This is typically achieved through visual representations of a virtual body; however, visual modalities are not always available, such as when users interact with agents using headphones or display-less glasses. In this work, we explore auditory embodiment. By introducing auditory cues of bodily presence - through spatially localized voice and situated Foley audio from environmental interactions - we investigate how audio alone can convey embodiment and influence perceptions of a conversational agent. We conducted a 2 (spatialization: monaural vs. spatialized) x 2 (Foley: none vs. Foley) within-subjects study, where participants (n=24) engaged in conversations with agents. Our results show that spatialization and Foley increase co-presence, but reduce users' perceptions of the agent's attention and other social attributes.

Live music provides a uniquely rich setting for studying creativity and interaction due to its spontaneous nature. The pursuit of live music agents—intelligent systems supporting real-time music performance and interaction—has captivated researchers across HCI, AI, and computer music for decades, and recent advancements in AI suggest unprecedented opportunities to evolve their design. However, the interdisciplinary nature of music has led to fragmented development across research communities, hindering effective communication and collaborative progress. In this work, we bring together perspectives from these diverse fields to map the current landscape of live music agents. Based on our analysis of 184 systems across both academic literature and video, we develop a comprehensive design space that categorizes dimensions spanning usage contexts, interactions, technologies, and ecosystems. By highlighting trends and gaps in live music agents, our design space offers researchers, designers, and musicians a structured lens to understand existing systems and shape future directions in real-time human-AI music co-creation. We release our annotated systems as a living artifact at https://live-music-agents.github.io.

Audio notifications provide users with an efficient way to access information beyond their current focus of attention. Current notification delivery methods, like phone ringtones, are primarily optimized for high noticeability, enhancing situational awareness in some scenarios but causing disruption and annoyance in others. In this work, we build on the observation that music listening is now a commonplace practice and present MARingBA, a novel approach that blends ringtones into background music to modulate their noticeability. We contribute a design space exploration of music-adaptive manipulation parameters, including beat matching, key matching, and timbre modifications, to tailor ringtones to different songs. Through two studies, we demonstrate that MARingBA supports content creators in authoring audio notifications that fit low, medium, and high levels of urgency and noticeability. Additionally, end users prefer music-adaptive audio notifications over conventional delivery methods, such as volume fading.

Spatial audio in Extended Reality (XR) provides users with better awareness of where virtual elements are placed, and efficiently guides them to events such as notifications, system alerts from different windows, or approaching avatars. Humans, however, are inaccurate in localizing sound cues, especially with multiple sources due to limitations in human auditory perception such as angular discrimination error and front-back confusion. This decreases the efficiency of XR interfaces because users misidentify from which XR element a sound is coming from. To address this, we propose Auptimize, a novel computational approach for placing XR sound sources, which mitigates such localization errors by utilizing the ventriloquist effect. Auptimize disentangles the sound source locations from the visual elements and relocates the sound sources to optimal positions for unambiguous identification of sound cues, avoiding errors due to inter-source proximity and front-back confusion. Our evaluation shows that Auptimize decreases spatial audio-based source identification errors compared to playing sound cues at the paired visual-sound locations. We demonstrate the applicability of Auptimize for diverse spatial audio-based interactive XR scenarios.

We propose a system for modifying spoken notifications in a manner that is sensitive to the music a user is listening to. Spoken notifications provide convenient access to rich information without the need for a screen. Virtual assistants see prevalent use in hands-free settings such as driving or exercising, activities where users also regularly enjoy listening to music. In such settings, virtual assistants will temporarily mute a user’s music to improve intelligibility. However, users may perceive these interruptions as intrusive, negatively impacting their music-listening experience. To address this challenge, we propose the concept of music-aware virtual assistants, where speech notifications are modified to resemble a voice singing in harmony with the user’s music. We contribute a system that processes user music and notification text to produce a blended mix, replacing original song lyrics with the notification content. In a user study comparing musical assistants to standard virtual assistants, participants expressed that musical assistants fit better with music, reduced intrusiveness, and provided a more delightful listening experience overall.

Sketch drawings capture the salient information of visual concepts. Previous work has shown that neural networks are capable of producing sketches of natural objects drawn from a small number of classes. While earlier approaches focus on generation quality or retrieval, we explore properties of image representations learned by training a model to produce sketches of images. We show that this generative, class-agnostic model produces informative embeddings of images from novel examples, classes, and even novel datasets in a few-shot setting. Additionally, we find that these learned representations exhibit interesting structure and compositionality.