Bathroom Activity Monitoring Based on Sound

Chen, Jianfeng; Kam, A.H.; Zhang, Jianmin; Liu, Ning; Li, Shue

doi:10.1007/11428572_4

Cited by 156 publications

(76 citation statements)

References 8 publications

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“…Examples include gunshots [16], vehicles [17], machines [18], and birds [19]. In addition to this, usually a low number of sound categories are involved in the studies, specifically chosen to minimize overlapping between different categories, and evaluations are carried out with one or very small set of audio contexts (kitchen [20], bathroom [21], meeting room [22], office and canteen [23]). Many of these previously presented methods are not applicable as such for the automatic sound event detection for continuous audio in real-world situations.…”

Section: Previous Workmentioning

confidence: 99%

Context-dependent sound event detection

Heittola

Mesaros

Eronen

et al. 2013

J AUDIO SPEECH MUSIC PROC.

189

133

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The work presented in this article studies how the context information can be used in the automatic sound event detection process, and how the detection system can benefit from such information. Humans are using context information to make more accurate predictions about the sound events and ruling out unlikely events given the context. We propose a similar utilization of context information in the automatic sound event detection process. The proposed approach is composed of two stages: automatic context recognition stage and sound event detection stage. Contexts are modeled using Gaussian mixture models and sound events are modeled using three-state left-to-right hidden Markov models. In the first stage, audio context of the tested signal is recognized. Based on the recognized context, a context-specific set of sound event classes is selected for the sound event detection stage. The event detection stage also uses context-dependent acoustic models and count-based event priors. Two alternative event detection approaches are studied. In the first one, a monophonic event sequence is outputted by detecting the most prominent sound event at each time instance using Viterbi decoding. The second approach introduces a new method for producing polyphonic event sequence by detecting multiple overlapping sound events using multiple restricted Viterbi passes. A new metric is introduced to evaluate the sound event detection performance with various level of polyphony. This combines the detection accuracy and coarse time-resolution error into one metric, making the comparison of the performance of detection algorithms simpler. The two-step approach was found to improve the results substantially compared to the context-independent baseline system. In the block-level, the detection accuracy can be almost doubled by using the proposed context-dependent event detection.

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Section: Previous Workmentioning

confidence: 99%

Context-dependent sound event detection

Heittola

Mesaros

Eronen

et al. 2013

J AUDIO SPEECH MUSIC PROC.

189

133

View full text Add to dashboard Cite

show abstract

“…(Popescu et al, 2008) used two microphones for the same purpose, using Kohonen Neural Networks. Out of a context of distress situation detection, (Chen et al, 2005) used HMM with the Mel-Frequency Cepstral Coefficients (MFCC) to determine the different uses of the bathroom (in order to recognize sequences of daily living). (Cowling, 2004) applied the recognition of non-speech sounds associated with their direction, with the purpose of using these techniques in an autonomous mobile surveillance robot.…”

Section: Sound Recognitionmentioning

confidence: 99%

Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living

Vacher¹,

Fleury²,

Portet³

et al. 2010

New Developments in Biomedical Engineering

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“…ESR can be used for home monitoring. It can be used to assist elderly people living in their home alone [6], [7]. In [8], it is used for home automation.…”

Section: Introductionmentioning

confidence: 99%