Event Detection and Recognition Using HMM with Whistle Sounds

Itoh, Hiroki; Takiguchi, Tetsuya; Ariki, Yasuo

doi:10.1109/sitis.2013.14

“…In this work, inhaler sounds are the environmental sound of interest. Most ESA technology has been applied for security purposes such as the detection of gun sounds [29]; in animal call detection of birds [30], and whales [31]; and also in applications to smart office [32] and smart homes for the assistance of the elderly [33] .…”

Section: Signal Processing Techniques For Environmental Sound Analysismentioning

confidence: 99%

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Garingo¹

2023

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The objective of this study is to provide a framework to aid physicians in identifying early respiratory ailments as well as provide a means of monitoring medication compliancy for both the patient and physicians. To aid physicians identify abnormal sounds during auscultations such as crackle, this work proposes a multimedia approach in the form of audio display (AD) to enhance crackle sounds produced in respiration. This work utilize a two step AD approach in which the crackle sound is first separated from the rest of the vesicular sound and then either sonified or audified. To aid in monitoring use of medication this work proposes an environmental sound analysis (ESA) framework to autonomously quantify adherence to medication. This work employed traditional audio features to extract meaningful discriminatory information to identify the inhaler sounds from the environment with the aid of maximum relevance and minimum redundancy algorithm and the hidden markov model.

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“…In this work, we are interested in the extraction of game happenings such as goals, cards, and substitutions in real-time (from live video streams), and in this context, the detection of events in soccer videos is not a new concept. Earlier approaches for the automated detection of selected events have employed probabilistic models such as Bayesian Networks [48,49], and hidden Markov model (HMM) [50][51][52][53][54]. With the rise of machine learning, there has been growing interest in using support vector machine (SVM) [55][56][57][58][59][60] and deep learning [45,[61][62][63][64][65] due to their relatively higher performance (detection accuracy) and the availability of more advanced computing infrastructures.…”

Section: Event Detection In Soccer Videosmentioning

confidence: 99%

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Rongved

¹

,

Stige

²

,

Hicks

³

et al. 2021

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Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.

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Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Garingo¹

2023

Preprint

0

View full text Add to dashboard Cite

The objective of this study is to provide a framework to aid physicians in identifying early respiratory ailments as well as provide a means of monitoring medication compliancy for both the patient and physicians. To aid physicians identify abnormal sounds during auscultations such as crackle, this work proposes a multimedia approach in the form of audio display (AD) to enhance crackle sounds produced in respiration. This work utilize a two step AD approach in which the crackle sound is first separated from the rest of the vesicular sound and then either sonified or audified. To aid in monitoring use of medication this work proposes an environmental sound analysis (ESA) framework to autonomously quantify adherence to medication. This work employed traditional audio features to extract meaningful discriminatory information to identify the inhaler sounds from the environment with the aid of maximum relevance and minimum redundancy algorithm and the hidden markov model.

show abstract

Event Detection and Recognition Using HMM with Whistle Sounds

Cited by 3 publications

References 10 publications

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

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