Acoustic Surveillance Intrusion Detection with Linear Predictive Coding and Random Forest

Yusoff, Marina; Afendi, Amirul Sadikin Md.

doi:10.1007/978-981-13-3441-2_6

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…The Sound event detection (SED) research field has been an active recently [1][2][3][4]. Autonomous audio surveillance has become more efficient as that artificial intelligence has stepped up the game [5].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has become very powerful to allow new approaches to be facilitated in countless domain [6]. Acoustic surveillance specifically in security application is still new to the world and requires research to allow better performances [4,7,8]. The detection on anomalous audio events effectively while avoiding false positives is crucial in security [3].…”

Section: Introductionmentioning

confidence: 99%

“…The rare term relates to the possibility that any target sound event to be identified may happen at most once within half a minute [4,12]. Detecting rare sound events are one of the key features IJ-AI ISSN: 2252-8938  Review of anomalous sound event detection approaches (Amirul Sadikin Md Afendi)…”

Section: Introductionmentioning

confidence: 99%

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Review of anomalous sound event detection approaches

Affendi

Yusoff

2019

IJ-AI

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<p>This paper presents a review of anomalous sound event detection(SED) approaches. SED is becoming more applicable for real-world appliactaions such as security, fire determination or olther emergency alarms. Despite many research outcome previously, further research is required to reduce false positives and improve accurracy. SED approaches are comprehensively organized by methods covering system pipeline components of acoustic descriptors, classification engine, and decision finalization method. The review compares multiple approaches that is applied on a specific dataset. Security relies on anomalous events in order to prevent it one must find these anomalous events. Audio surveillance has become more efficient as that artificial intelligence has stepped up the game. Autonomous SED could be used for early detection and prevention. It is found that the state of the art method viable used in SED using features of log-mel energies in convolutional recurrent neural network(CRNN) with long short term memory(LSTM) with a verification step of thresholding has obtained 93.1% F1 score and 0.1307 ER. It is found that feature extraction of log mel energies are highly reliable method showing promising results on multiple experiments.</p>

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review of anomalous sound event detection approaches

Affendi

Yusoff

2019

IJ-AI

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“…CRNN uses the CNN local feature extraction capability, and the RNN temporal summarization would lead to an efficient and effective model than standard CNN or RNN on its own [16]. However, CRNN has its limitation and problem which these are due to its architecture; the hidden states in CRNN need to be calculated one by one [17].…”

Section: Research Backgroundmentioning

confidence: 99%

Forest Sound Event Detection with Convolutional Recurrent Neural Network-Long Short-Term Memory

Muhammad Naqiuddin Zaini,

Marina Yusoff,

Muhammad Amirul Sadikin

2023

ARASET

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Sound event detection tackles an audio environment's complex sound analysis and recognition problem. The process involves localizing and classifying sounds mainly to estimate the start point and end points of the separate sounds and describe each sound. Sound event detection capability relies on the type of sound. Although detecting sequences of distinct temporal sounds is straightforward, the situation becomes complex when the sound is multiple overlapping of much single audio. This situation usually occurs in the forest environment. Therefore, this aim of the paper is to propose a Convolution Recurrent Neural Network-Long Short-Term Memory algorithm to detect an audio signature of intruders in the forest environment. The audio is extracted in the Mel-frequency cepstrum coefficient and fed into the algorithm as an input. Six sound categories are chainsaw, machete, car, hatchet, ambiance, and bike. They were tested using several epochs, batch size, and filter of the layer in the model. The proposed model can achieve an accuracy of 98.52 percent in detecting the audio signature with a suitable parameter selection. In the future, additional types of audio signatures of intruders and further aspects of evaluation can be added to make the algorithm better at detecting intruders in the forest environment.

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“…Ecoacoustic monitoring is growing with multiple applications in different domains, including the monitoring of protected and invasive species in the wildlife (e.g., bats, birds, bees, toads, etc.) [ 32 , 33 , 34 , 35 ], soundscape analysis [ 36 , 37 ], biodiversity conversation [ 38 ], environmental surveillance [ 39 , 40 ] and ocean monitoring [ 41 , 42 ]. In addition, the optical monitoring feature enhances the potential of identifying insects [ 12 ] since it is immune to ambient noise, unlike acoustic approaches.…”

Section: Typical Applicationsmentioning

confidence: 99%

A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities

Vasconcelos¹,

Nunes²

2022

Sensors

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The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.

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Acoustic Surveillance Intrusion Detection with Linear Predictive Coding and Random Forest

Cited by 6 publications

References 26 publications

Review of anomalous sound event detection approaches

Review of anomalous sound event detection approaches

Forest Sound Event Detection with Convolutional Recurrent Neural Network-Long Short-Term Memory

A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities

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