An Overview of Audio Event Detection Methods from Feature Extraction to Classification

Babaee, Elham; Anuar, Nor Badrul; Wahab, Ainuddin Wahid Abdul; Shamshirband, Shahaboddin; Chronopoulos, Anthony T.

doi:10.1080/08839514.2018.1430469

Cited by 39 publications

(12 citation statements)

References 132 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e detection purpose is achieved by comparing the similarity of the shape of the object to be detected with the shape of the instances in the tree structure. In the literature [8], Deformable Part Models (DPM) are proposed to handle the partial deformation of an object. In this paper, a latent SVM is used to learn the parameters in an alternating optimization manner and to perform data mining and further learning for error-prone subsamples [9].…”

Section: Related Studiesmentioning

confidence: 99%

[Retracted] Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis

Geng

2021

Complexity

View full text Add to dashboard Cite

In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis (PCA) subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit (CPU). This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm.

show abstract

Section: Related Studiesmentioning

confidence: 99%

[Retracted] Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis

Geng

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…The most significant feature of this method is that the logarithmic gray spectrum of the audio signal is the input. The method has two different steps [ 8 ]. The first is to detect the existence of abnormal audio events on the system time axis, and the sliding convolution kernel is one detection scheme that filters the candidate areas of audio events through the system area recommendation network technology, also known as boundary detection [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Voice Detection and Music Smart Classroom Teaching Application Based on Mobile Edge Computing

Huang

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Audio monitoring information technology plays an important role in the application of monitoring systems, and it is an indispensable and important link. Whether intelligent audio monitoring management can be successfully realized, the key is to successfully detect abnormal sounds from a variety of external environment background sounds. The core technology of abnormal sound detection is a pattern classification task. The dimension of features is fixed in the traditional abnormal sound detection model. Such an ordinary solution will lead to a long time-consuming detection process and increase the boundary error. Traditional speech detection is not good enough for sound discrimination in a noisy environment, so this paper proposes an abnormal speech detection technology based on moving edge computing. Aiming at the noisy environment of the music classroom, the determination of objective function should be further optimized. Through the related technology, a certain sound can be quickly identified and analyzed in the music classroom to promote the development of the music wisdom classroom, and music wisdom classrooms can be used as a computer-aided system to help music teachers better grasp the learning situation of students, put forward relevant guidance strategies, improve students’ learning enthusiasm, and enhance teachers’ teaching efficiency so as to promote the progress of music teaching.

show abstract

“…Typical applications of Acoustic/Audio Event Detection (AED) includes multimedia indexing and retrieval [168], surveillance [65] and robotics [37]. AED frameworks are typically composed of feature extraction and inference/classication phases, and aim to recognize a distinct sound pattern/event in a continuous acoustic signal [18]. Ballan et al [20] proposed a probabilistic neural network for audio event detection in soccer recordings.…”

Section: Event Recognition In Audio Recordingsmentioning

confidence: 99%

How Deep Features Have Improved Event Recognition in Multimedia

Ahmad

Conci

2019

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

Event recognition is one of the areas in multimedia that is attracting great attention of researchers. Being applicable in a wide range of applications, from personal to collective events, a number of interesting solutions for event recognition using multimedia information sources have been proposed. On the other hand, following their immense success in classification, object recognition, and detection, deep learning has been shown to perform well in event recognition tasks also. Thus, a large portion of the literature on event analysis relies nowadays on deep learning architectures. In this article, we provide an extensive overview of the existing literature in this field, analyzing how deep features and deep learning architectures have changed the performance of event recognition frameworks. The literature on event-based analysis of multimedia contents can be categorized into four groups, namely (i) event recognition in single images; (ii) event recognition in personal photo collections; (iii) event recognition in videos; and (iv) event recognition in audio recordings. In this article, we extensively review different deep-learning-based frameworks for event recognition in these four domains. Furthermore, we also review some benchmark datasets made available to the scientific community to validate novel event recognition pipelines. In the final part of the manuscript, we also provide a detailed discussion on basic insights gathered from the literature review, and identify future trends and challenges.

show abstract

An Overview of Audio Event Detection Methods from Feature Extraction to Classification

Cited by 39 publications

References 132 publications

[Retracted] Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis

[Retracted] Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis

Voice Detection and Music Smart Classroom Teaching Application Based on Mobile Edge Computing

How Deep Features Have Improved Event Recognition in Multimedia

Contact Info

Product

Resources

About