Audio Location: Accurate Low-Cost Location Sensing

Scott, James R.; Dragovic, Boris

doi:10.1007/11428572_1

Cited by 48 publications

(30 citation statements)

References 22 publications

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“…For this reason, the map was hard-coded to match the space in which we would conduct the study. Also, our study used Wizard-of-Oz to enter sound location (a full implementation would require additional software and hardware, including either a microphone array (Asano et al 2000, Bian et al 2005 or a PC with multiple sound cards (Scott and Dragovic 2005)). In particular, we provided the Wizard with a second window showing the map.…”

Section: Mapmentioning

confidence: 99%

Evaluating non-speech sound visualizations for the deaf

Matthews¹,

Fong²,

Ho-Ching³

et al. 2006

Behaviour & Information Technology

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Sounds such as co-workers chatting nearby or a dripping faucet help us maintain awareness of and respond to our surroundings. Without a tool that communicates ambient sounds in a nonauditory manner, maintaining this awareness is difficult for people who are deaf. We present an iterative investigation of peripheral, visual displays of ambient sounds. Our major contributions are: (1) a rich understanding of what ambient sounds are useful to people who are deaf, (2) a set of visual and functional requirements for a peripheral sound display, based on feedback from people who are deaf, (3) lab-based evaluations investigating the characteristics of four prototypes, and (4) a set of design guidelines for successful ambient audio displays, based on a comparison of four implemented prototypes and user feedback. Our work provides valuable information about the sound awareness needs of the deaf and can help to inform further design of such applications.

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Section: Mapmentioning

confidence: 99%

Evaluating non-speech sound visualizations for the deaf

Matthews¹,

Fong²,

Ho-Ching³

et al. 2006

Behaviour & Information Technology

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“…A wide variety of solutions have been proposed. The early work of Want et al [29] and several subsequent works [30,3,25,6,27] require that sensors or beacons be installed throughout the environment. This requirement is onerous, but the resulting localization accuracy and reliability are generally excellent.…”

Section: Related Workmentioning

confidence: 99%

“…Scott et al present perhaps the first acoustic localization work; they place microphones at key locations and users snap their fingers to alert the system of their presence [27]. We previously used an ultrasonic sonar sensing system to silently detect the presence of users at computers [28].…”

Section: Beyond Radiomentioning

confidence: 99%

Indoor localization without infrastructure using the acoustic background spectrum

Tarzia

Dinda

Dick

et al. 2011

Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services

254

119

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We introduce a new technique for determining a mobile phone's indoor location even when Wi-Fi infrastructure is unavailable or sparse. Our technique is based on a new ambient sound fingerprint called the Acoustic Background Spectrum (ABS). An ABS serves well as a room fingerprint because it is compact, easily computed, robust to transient sounds, and surprisingly distinctive. As with other fingerprint-based localization techniques, location is determined by measuring the current fingerprint and then choosing the "closest" fingerprint from a database. An experiment involving 33 rooms yielded 69% correct fingerprint matches meaning that, in the majority of observations, the fingerprint was closer to a previous visit's fingerprint than to any fingerprints from the other 32 rooms. An implementation of ABS-localization called Batphone is publicly available for Apple iPhones. We used Batphone to show the benefit of using ABS-localization together with a commercial WiFi-based localization method. In this second experiment, adding ABS improved room-level localization accuracy from 30% (Wi-Fi only) to 69% (Wi-Fi and ABS). While Wi-Fibased localization has difficulty distinguishing nearby rooms, Batphone performs just as well with nearby rooms; it can distinguish pairs of adjacent rooms with 92% accuracy.

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“…Scott [36] designed a 6-element microphonearray consisting of low-cost, off-the-shelf microphones and sound cards. The array was installed in a 1.8m x 1.8m x 1.2m room and was used for localizing implosive sounds such finger clicks or hand claps.…”

Section: Microphone Array Designmentioning

confidence: 99%