A sound event detection based on hybrid convolution neural network and random forest

Afendi, M.; Yusoff, Marina

doi:10.11591/ijai.v11.i1.pp121-128

“…This feature of Convolutional Neural Networks makes them ideal for sound detection. In [ 103 ], the authors have used hybrid CNN and random forest. The feature extraction involves Mel-log energies.…”

Section: Acoustic Source Detection and Localization Methodsmentioning

confidence: 99%

A Survey of Sound Source Localization and Detection Methods and Their Applications

Jekateryńczuk,

Piotrowski

2023

Sensors

10

0

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This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

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“…In their proposed method, [54] used an SVM classifier with an RBF kernel for model fusion in sound event detection. The use of a hybrid approach combining CNN and RF for sound event detection (SED) in a natural forest environment in [62] achieved a remarkable performance, showing improvement with a 0.82 F1 score and a minimum false alarm rate of 10% in SED.…”

Section: Hyungui Et Al Used the Temporal Dependency Of The Extracted ...mentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Abubakar,

Othmani,

Siarry

et al. 2024

Preprint

0

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Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

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“…Spectrogram computation and features extraction methods (e.g., MFCCs or log-mel spectrograms) are commonly used in sound event detection [89]. In [4], Mel log energy (MLE) features are derived using the fast Fourier transform (FFT) to discern distinct frequencies in an audio signal. Recognizing these unique frequency bands is important in the detection of sound events, as each sound is characterized by its particular frequency spectrum.…”

Section: B Low-level Features Extractionmentioning

confidence: 99%

“…In their proposed method, [12] used an SVM classifier with an RBF kernel for model fusion in sound event detection. The use of a hybrid approach combining CNN and RF for sound event detection (SED) in a natural forest environment in [4] achieved a remarkable performance, showing improvement with a 0.82 F1 score and a minimum false alarm rate of 10% in SED.…”

Section: Classificationmentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

Abubakar,

Othmani,

Siarry

et al. 2024

IEEE Access

0

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Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

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A sound event detection based on hybrid convolution neural network and random forest

Cited by 7 publications

References 26 publications

A Survey of Sound Source Localization and Detection Methods and Their Applications

A Survey of Sound Source Localization and Detection Methods and Their Applications

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

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