Current sound-based practices and systems developed in both academia and industry point to convergent research trends that bring together the field of sound and music Computing with that of the Internet of Things. This article proposes a vision for the emerging field of the Internet of Sounds (IoS), which stems from such disciplines. The IoS relates to the network of Sound Things, i.e., devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In the IoS paradigm, which merges under a unique umbrella the emerging fields of the Internet of Musical Things and the Internet of Audio Things, heterogeneous devices dedicated to musical and nonmusical tasks can interact and cooperate with one another and with other things connected to the Internet to facilitate sound-based services and applications that are globally available to the users. We survey the state-of-the-art in this space, discuss the technological and nontechnological challenges ahead of us and propose a comprehensive research agenda for the field.

This paper presents a system for identifying the room in an audio or video recording through the analysis of acoustical properties. The room identification system was tested using a corpus of 13440 reverberant audio samples. With no common content between the training and testing data, an accuracy of 61% for musical signals and 85% for speech signals was achieved. This approach could be applied in a variety of scenarios where knowledge about the acoustical environment is desired, such as location estimation, music recommendation, or emergency response systems.

Spatialization, the synthesis of spaces and spatialproperties of sounds for a listener, is a growing fieldof interest for researchers, sound engineers, com-posers, and audiophiles. Due to broad and diverseviewpoints and requirements, the understandingand application of spatial sound is developing inmany ways. To benefit from varying viewpoints,individuals involved in artistic practice and thoseinvolved in theoretical or applied research needto engage in regular dialogue. Blesser and Salter(2006, p. 184) reported on the long-term relationshipbetween artists and audio researchers regardingvirtual spaces, which is “the story of an evolvingrelationship between sophisticated audio engineers,creating tools, and impatient artists, incorporat-ing such tools long before they are fully defined.” [...

With numerous conversational AI (CAI) systems being deployed in homes, cars, and public spaces, people are faced with an increasing number of privacy and security decisions. They need to decide which personal information to disclose and how their data can be processed by providers and developers. On the other hand, designers, developers, and integrators of conversational AI systems must consider users’ privacy and security during development and make appropriate choices. However, users as well as other actors in the CAI ecosystem can suffer from cognitive biases and other mental flaws in their decision-making resulting in adverse privacy and security choices. Debiasing strategies can help to mitigate these biases and improve decision-making. In this position paper, we establish a novel framework for categorizing debiasing strategies, show how existing privacy debiasing strategies can be adapted to the context of CAI, and assign them to relevant stakeholders of the CAI ecosystem. We highlight the unique possibilities of CAI to foster debiasing, discuss limitations of the strategies, and identify research challenges.

A new TV audio system based on the MPEG-H 3D audio standard has been designed, tested, and implemented for ATSC 3.0 broadcasting. The system offers immersive sound to increase the realism and immersion of programming, and offers audio objects that enable interactivity or personalization by viewers. Immersive sound may be broadcast using loudspeaker channel-based signals or scene-based components in combination with static or dynamic audio objects. Interactivity can be enabled through broadcaster-authored preset mixes or through user control of object gains and positions. Improved loudness and dynamic range control allows tailoring the sound for best reproduction on a variety of consumer devices and listening environments. The system includes features to allow operation in HD-SDI broadcast plants, storage, and editing of complex audio programs on existing video editor software or digital audio workstations, frame-accurate switching of programs, and new technologies to adapt current mixing consoles for live broadcast production of immersive and interactive sound. Field tests at live broadcast events were conducted during system design and a live demonstration test bed was constructed to prove the viability of the system design. The system also includes receiver-side components to enable interactivity, binaural rendering for headphone, or tablet computer listening, a “3D soundbar” for immersive playback without overhead speakers, and transport over HDMI 1.4 connections in consumer equipment. The system has been selected as a proposed standard of ATSC 3.0 and is the sole audio system of the UHD ATSC 3.0 broadcasting service currently being deployed in South Korea.

Conversational AI (CAI) systems are on the rise and have been widely adopted in homes, cars and public spaces. Yet, people report privacy concerns and mistrust in these systems. Current data protection regulations ask providers to communicate data practices transparently and provide users with options to control their data. However, even if users are given control, their decisions can be subject to heuristics and biases leaving people frustrated and regretful. Based on the idea of conversational privacy and debiasing, we design three privacy strategies for CAI that allow people to have their data deleted while at the same time promoting rational decision-making. We conduct a user study to test our strategies in two widespread scenarios using a text-based CAI system and evaluate their impact on peoples’ privacy perception, usability and attitude-behaviour alignment. We find that our strategies can significantly change people’s behaviour, but do not influence peoples’ privacy perception. Finally, we discuss evaluation metrics and future research directions to investigate privacy controls in Conversational AI systems.