Global statistical features-based approach for Acoustic Event Detection

Jayalakshmi, S.; Chandrakala, S.; Nedunchelian, R.

doi:10.1016/j.apacoust.2018.04.026

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…The detection on anomalous audio events effectively while avoiding false positives is crucial in security [3]. Two categories of sound recognition, non-speech the determination of the sound event source and speech recognition of verbal language [9][10]. Many works embrace the method of multi-label classification to detect polyphonic acoustic events with a worldwide limit to detect active acoustic events [2,7,10].…”

Section: Introductionmentioning

confidence: 99%

“…Two categories of sound recognition, non-speech the determination of the sound event source and speech recognition of verbal language [9][10]. Many works embrace the method of multi-label classification to detect polyphonic acoustic events with a worldwide limit to detect active acoustic events [2,7,10]. The lack of accuracy and high false positives on current SED approaches is holding autonomous audio surveillance to be applied for real-world applications [11].…”

Section: Introductionmentioning

confidence: 99%

“…In the race towards making audio surveillance a reality many have aid by providing data for training and testing new models in solving the problem. Datasets on SED can be found available in many varieties as the internet of things are becoming more popular [1,10,12].…”

Section: Introductionmentioning

confidence: 99%

“…for security [10]. The approaches used in previous studies, particularly in detection of rare events can be simplified by the combinations of certain parts of the whole detection system [8].…”

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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%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Review of anomalous sound event detection approaches

Affendi

Yusoff

2019

IJ-AI

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“…Audio recognition and classification consist of extracting different features from a sample audio file and feeding these features into a machine-learning algorithm, to detect classes of the present sounds. This topic has been studied for ambient sound classification [18,19,20], noise signal classification [21,22], speech/music classification [23], music genre classification [24], human accent or language classification, speaker recognition [25,26], and indoor localization [27]. Hinton et al [28] and McLoughlin et al [29] used Deep Neural Network (DNN) to develop an automated speech recognition system and a robust sound event classification, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment

Sherafat

Rashidi

Lee

2019

Sensors

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Automatically recognizing and tracking construction equipment activities is the first step towards performance monitoring of a job site. Recognizing equipment activities helps construction managers to detect the equipment downtime/idle time in a real-time framework, estimate the productivity rate of each equipment based on its progress, and efficiently evaluate the cycle time of each activity. Thus, it leads to project cost reduction and time schedule improvement. Previous studies on this topic have been based on single sources of data (e.g., kinematic, audio, video signals) for automated activity-detection purposes. However, relying on only one source of data is not appropriate, as the selected data source may not be applicable under certain conditions and fails to provide accurate results. To tackle this issue, the authors propose a hybrid system for recognizing multiple activities of construction equipment. The system integrates two major sources of data—audio and kinematic—through implementing a robust data fusion procedure. The presented system includes recording audio and kinematic signals, preprocessing data, extracting several features, as well as dimension reduction, feature fusion, equipment activity classification using Support Vector Machines (SVM), and smoothing labels. The proposed system was implemented in several case studies (i.e., ten different types and equipment models operating at various construction job sites) and the results indicate that a hybrid system is capable of providing up to 20% more accurate results, compared to cases using individual sources of data.

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