AC: An Audio Classifier to Classify Violent Extensive Audios

Pillai, Anuradha; Kaushik, Prachi

doi:10.1007/978-981-10-6626-9_1

Cited by 2 publications

(6 citation statements)

References 5 publications

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“…The result shows a precision of 93% at a false rejection rate of 5% when the SNR is 10 dB. Research work in [22,26] has also used GMM for the classification of a scream under noisy conditions.…”

Section: Scream Detection Architecturesmentioning

confidence: 98%

“…Research is ongoing on the effect of screams for text-independent speaker recognition systems [20]. Many research works have been conducted on scream detection for surveillance applications in both indoor and outdoor noisy environments [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Scream Detection Architecturesmentioning

confidence: 99%

“…Spectral features such as spectral entropy [35], spectral centroid [22,32,35], and spectral roll-off [22,29,32] have been found to be effective for scream detection. Research work in [27] describes a low-cost noise-robust scream detection system by using band-limited spectral entropy as a feature.…”

Section: Scream Detection Featuresmentioning

confidence: 99%

“…The temporal feature zero crossing rate (ZCR) is also widely used in scream detection for discriminant performance [21,22,29,32]. In [30], the authors used the continuity of log-energy combined with MFCCs to detect screams from non-scream audio.…”

Section: Scream Detection Featuresmentioning

confidence: 99%

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An Initial Machine Learning-Based Victim’s Scream Detection Analysis for Burning Sites

et al. 2021

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Fire incidents are responsible for severe damage and thousands of deaths every year all over the world. Extreme temperatures, low visibility, toxic gases, and unknown locations of victims create difficulties and delays in rescue operations, escalating the risk of injury or death. It is time-critical to detect the victims trapped inside the burning sites for facilitating the rescue operations. This research work presents an audio-based automated system for victim detection in fire emergencies, investigating two machine learning (ML) methods: support vector machines (SVM) and long short-term memory (LSTM). The performance of these two ML techniques has been evaluated based on a variety of performance metrics. Our analyses show that both ML methods provide superior scream detection performance, with SVM slightly overperforming LSTM. Because of its lower complexity, SVM is a better candidate for real-time implementation in our autonomous embedded system vehicle (AESV).

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Section: Scream Detection Architecturesmentioning

confidence: 98%

Section: Scream Detection Architecturesmentioning

confidence: 99%

Section: Scream Detection Featuresmentioning

confidence: 99%

Section: Scream Detection Featuresmentioning

confidence: 99%

See 2 more Smart Citations

An Initial Machine Learning-Based Victim’s Scream Detection Analysis for Burning Sites

et al. 2021

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“…In more complicated and complex signals such as speech or music where the signal changes its properties over time, it is evidently more meaningful to refer to the altering frequency content over a smaller time interval than an infinite time interval. Spectral Flux E. R. Siebert et al [29], L. Gerosa et al [2],, M. Z. Zaheer et al [14], R. A. Breguet et al [31] Spectral Tilt L. Gerosa et al [2], R. A. Breguet et al [31], C. Zhang et al [25] Spectral Entropy M. Mark et al [21], A. Pillai et al [8] , N. Hayasaka et al [4], W. Liao et al [23] Signal Bandwidth M. Mark et al [21], W. Liao et al [23] Sub-Band Energy Ratio J. H. L. Hansen et al [1], C. Chan et al [22], M. Z. Zaheer et al [14], C. Zhang et al [25] Linear Prediction P. C. Schön et al [27], N. E. O. Connor et al [30] Prosodic Pitch/Fundamental Frequency M. Mark et al [21], L. H. Arnal et al [7] , C. Chan et al [22], L. Gerosa et al [2], J. H. L. Hansen et al [13], M. Z. Zaheer et al [14], K. Kato [19], B. Uzkent et al [20], W. Liao et al [23] Loudness/Intensity L. Gerosa et al [2], K. Kato [19], C. Zhang et al [25] Rhythm/Duration C. Chan et al [22], K. Kato [19], C. Zhang et al [25] Log Energy N. Hayasaka et al [4], W. Huang et al [3] 0.0% 70 | P a g e www.ijacsa.thesai.org …”

Section: B Analysis Of Scream Sound Featuresmentioning

confidence: 99%

A Review on Scream Classification for Situation Understanding

Nazir¹,

Awais²,

Malik³

et al. 2018

ijacsa

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Abstract-In our living environment, a non-speech audio signal provides a significant evidence for situation awareness. It also compliments the information obtained from a video signal. In non-speech audio signals, screaming is one of the events in which the people like security guard, care taker and family members are particularly interested in terms of care and surveillance because screams are atomically considered as a sign of danger. Contrary to this concept, this review is particularly targeting automated acoustic systems using non-speech class of scream believing that the screams can further be classified into various classes like happiness, sadness, fear, danger, etc. Inspired by the prevalent scream audio detection and classification field, a taxonomy has been projected to highlight the target applications, significant sound features, classification techniques, and their impact on classification problems in last few decades. This review will assist the researchers for retrieving the most appropriate scream detection and classification technique and acoustic parameters for scream classification that can assist in understanding the vocalization condition of the speaker.

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AC: An Audio Classifier to Classify Violent Extensive Audios

Cited by 2 publications

References 5 publications

An Initial Machine Learning-Based Victim’s Scream Detection Analysis for Burning Sites

An Initial Machine Learning-Based Victim’s Scream Detection Analysis for Burning Sites

A Review on Scream Classification for Situation Understanding

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