Sports Highlights Generation using Decomposed Audio Information

Islam, Muhammad Rafiqul; Paul, Manoranjan; Antolovich, Michael; Kabir, Muhammad Ashad

doi:10.1109/icmew.2019.00105

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…For instance, Xu et al [55] create seven audio keywords for detecting potential events: long-whistling, double-whistling, multiwhistling, exciting commentator speech, plain commentator speech, exciting audience sound, and plain audience sound, with the justification that these have strong ties to certain types of events. Islam et al [79] propose an approach to extract audio features using Empirical Mode Decomposition (EMD) that can filter out noises. They conduct a set of experiments on four soccer videos and show that the proposed method can detect goal events with 96% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Rongved

Stige

Hicks

et al. 2021

MAKE

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Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.

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Section: Discussionmentioning

confidence: 99%

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Rongved

Stige

Hicks

et al. 2021

MAKE

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“…Empirical mode decomposition (EMD) is a popular method to decompose the timeseries signal into different intrinsic mode functions (IMFs) based on frequency differences. This technique is also helpful in filtering out some specific signal components from the original signal in the presence of noise [68]. In this study, we have applied EMD in the spectral signal data to filter out some specific spectral signals and extract new features from them to be used in machine learning techniques to improve classification performance.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band

et al. 2023

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Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.

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A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques

Vasudevan¹,

Gounder²

2023

EAI/Springer Innovations in Communication and Computing

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Sports Highlights Generation using Decomposed Audio Information

Cited by 6 publications

References 15 publications

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band

A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques

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