SoundSense

Lu, Hong; Pan, Wei; Lane, Nicholas D.; Choudhury, Tanzeem; Campbell, Andrew T.

doi:10.1145/1555816.1555834

Cited by 471 publications

(33 citation statements)

References 33 publications

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“…cars; You et al, 2013) can be identified from computer vision techniques using phone cameras; (3) higher level activities and behaviours (e.g. vacuuming, driving, sleeping) can be inferred from sensor data using automated classifiers Lu et al, 2009); (4) location patterns (e.g. visit frequency and duration) for both indoor and outdoor spaces can be estimated from GPS and WiFi scans; and (5) time can be estimated from each type of smartphone data using its associated timestamp.…”

Section: Assessment Of Cues Characteristics and Classesmentioning

confidence: 99%

Capturing Situational Information with Smartphones and Mobile Sensing Methods

et al. 2015

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Smartphones are pervasive companions to many people as they go about their daily lives. In addition, they are equipped with a wide array of sensors making it possible to measure objective information about people and situations, many times, with great fidelity, over long periods of time, in a way that is both unobtrusive and ecologically valid. Therefore, we argue that smartphones and other forms of mobile sensing are ideally suited to measuring situations. In particular, we describe how sensing methods can be used to assess situational cues, characteristics and classes.

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Section: Assessment Of Cues Characteristics and Classesmentioning

confidence: 99%

Capturing Situational Information with Smartphones and Mobile Sensing Methods

et al. 2015

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“…Another application worth mentioning is SoundSense [10], which explores continuously sensing and classifying audio events to recognize general sound types heard by users (e.g., voice or music) and specific activities (e.g., walking, driving cars). These classifications enable a number of different applications including an audio daily diary and music detection service, which were both prototyped by the authors.…”

Section: Mobile Identity Managementmentioning

confidence: 99%

“…Previous work on speech recognition [2], [7], [8] and speaker recognition [9], [10] has been applied on mobile devices, performing either implicit, or explicit user authentication. However, to the best of our knowledge, no prior work has been jointly performed on a mobile speaker and speech recognition task as well as being implemented as an identity awareness app access and function control framework.…”

Section: Introductionmentioning

confidence: 99%

USR: Enabling Identity Awareness and Usable App Access Control During Hand-free Mobile Interactions

Feng¹,

Gao²,

Boumber³

et al. 2014

Proceedings of the 6th International Conference on Mobile Computing, Applications and Services

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We propose a new approach for improving user experience and privacy protection during human-mobile speech interaction by considering factors, such as user identity, application privacy level, usable app access control, and application function class. To integrate these factors into speech recognition on the mobile device, we design and implement a unified speechspeaker recognizer (USR) framework, which will recognize both speech content and speaker identification, and respond accordingly. This USR framework consists of an application interface, a speaker recognition module, a speech recognition module, and an identity management module. A comparison study was conducted contrasting the benefits and limitations of USR framework to the established original speech recognition and app access control framework on mobile device, such as Google Voice, and AppLock. Our results show that, while USR framework intuitively improved mobile privacy by serving only the phone owner for specific customized applications, USR framework was also able to provide better user experience across a number of tasks.

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“…The classification performance based on MFCC was high, but the specific sound result was poor. Lu et al [12] also implemented an environmental sound classification system for mobile devices. They performed coarse classification for input signals such as speech, music, and "Ambient Sound."…”

Section: Related Workmentioning

confidence: 99%

Noise-Robust environmental sound classification method based on combination of ICA and MP features

Mogi

Kasai

2012

AIR

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This paper presents an environmental sound classification method that is noise-robust against sounds recorded by mobile devices, and presents evaluation of its performance. This method is specifically designed to recognize higher semantics of context from environmental sound. Conventionally, sound classifications have used acoustic features in the frequency domain extracted from sound data using signal processing techniques. Although the most popular feature is Mel-frequency Cepstral Coefficients (MFCC), MFCC is inappropriate for mixture sound with noise. Independent Component Analysis (ICA) can extract sound characteristics even when the source is corrupted by noise because components within the source are assumed to be independent. In recent years, Matching Pursuit (MP) has been addressed to extract time-domain features. It has been applied to various applications. The feature is effective for recognizing and classifying environmental sounds that include time-variant sound such as birdsongs, alarms, and vehicle sounds. In this way, some innovative techniques have been proposed to recognize and classify environmental sounds recorded on mobile devices. However, wehave not yet obtained a decisive method to attain a higher recognition and classification rate against environmental sounds with various noises such as unintended sounds and white noise. To address this problem, we propose a noise-robust classification method using a combination of Independent Component Analysis (ICA) and MP. It is possible to reduce noise effects for feature extraction. From performance evaluations, we confirmed that the proposed method can provide about 8% better classification than that of MFCC feature extraction.

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SoundSense

Cited by 471 publications

References 33 publications

Capturing Situational Information with Smartphones and Mobile Sensing Methods

Capturing Situational Information with Smartphones and Mobile Sensing Methods

USR: Enabling Identity Awareness and Usable App Access Control During Hand-free Mobile Interactions

Noise-Robust environmental sound classification method based on combination of ICA and MP features

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