Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

SameSameButDifferent v.02 – Iceland

The history of computer music is to a great extent the history of algorithmic composition. Here generative approaches are seen as an artistic technique. However, the generation of algorithmic music is normally done in the studio, where the music is aesthetically valued by the composer. The public only gets to know one, or perhaps few, variations of the expressive scope of the algorithmic system itself. In this paper, we describe a generative music system of infinite compositions, where the system itself is aimed for distribution and to be used on personal computers. This system has a dual structure of a compositional score and a performer that performs the score in real-time every time a piece is played. We trace the contextual background of such systems and potential future applications

hpDJ: An automated DJ with floorshow feedback

Many radio stations and nightclubs employ Disk-Jockeys (DJs) to provide a continuous uninterrupted stream or “mix” of dance music, built from a sequence of individual song-tracks. In the last decade, commercial pre-recorded compilation CDs of DJ mixes have become a growth market. DJs exercise skill in deciding an appropriate sequence of tracks and in mixing 'seamlessly' from one track to the next. Online access to large-scale archives of digitized music via automated music information retrieval systems offers users the possibility of discovering many songs they like, but the majority of consumers are unlikely to want to learn the DJ skills of sequencing and mixing. This paper describes hpDJ, an automatic method by which compilations of dance-music can be sequenced and seamlessly mixed by computer, with minimal user involvement. The user may specify a selection of tracks, and may give a qualitative indication of the type of mix required. The resultant mix can be presented as a continuous single digital audio file, whether for burning to CD, or for play-out from a personal playback device such as an iPod, or for play-out to rooms full of dancers in a nightclub. Results from an early version of this system have been tested on an audience of patrons in a London nightclub, with very favourable results. Subsequent to that experiment, we designed technologies which allow the hpDJ system to monitor the responses of crowds of dancers/listeners, so that hpDJ can dynamically react to those responses from the crowd. The initial intention was that hpDJ would monitor the crowd’s reaction to the song-track currently being played, and use that response to guide its selection of subsequent song-tracks tracks in the mix. In that version, it’s assumed that all the song-tracks existed in some archive or library of pre-recorded files. However, once reliable crowd-monitoring technology is available, it becomes possible to use the crowd-response data to dynamically “remix” existing song-tracks (i.e, alter the track in some way, tailoring it to the response of the crowd) and even to dynamically “compose” new song-tracks suited to that crowd. Thus, the music played by hpDJ to any particular crowd of listeners on any particular night becomes a direct function of that particular crowd’s particular responses on that particular night. On a different night, the same crowd of people might react in a different way, leading hpDJ to create different music. Thus, the music composed and played by hpDJ could be viewed as an “emergent” property of the dynamic interaction between the computer system and the crowd, and the crowd could then be viewed as having collectively collaborated on composing the music that was played on that night. This en masse collective composition raises some interesting legal issues regarding the ownership of the composition (i.e.: who, exactly, is the author of the work?), but revenue-generating businesses can nevertheless plausibly be built from such technologies

Exciting Instrumental Data: Toward an Expanded Action-Oriented Ontology for Digital Music Performance

Musical performance using digital musical instruments has obfuscated the relationship between observable musical gestures and the resultant sound. This is due to the sound producing mechanisms of digital musical instruments being hidden within the digital music making system. The difficulty in observing embodied artistic expression is especially true for musical instruments that are comprised of digital components only. Despite this characteristic of digital music performance practice, this thesis argues that it is possible to bring digital musical performance further within our action-oriented ontology by understanding the digital musician through the lens of Lévi-Strauss’ notion of the bricoleur. Furthermore, by examining musical gestures with these instruments through a multi-tiered analytical framework that accounts for the physical computing elements necessarily present in all digital music making systems, we can further understand and appreciate the intricacies of digital music performance practice and culture