Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients

Muhammad, Ghulam; Alotaibi, Yousef Ajami; Alsulaiman, Mansour; Huda, Mohammad Nurul

doi:10.1109/icdt.2010.10

Cited by 29 publications

(8 citation statements)

References 10 publications

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“…Since then they are used in many audio and speech processing applications including environment detection [28], voice pathology detection, and speaker identification. Some of the features are scaler, and the rest are vector.…”

Section: Mpeg-7 Low-level Audio Featuresmentioning

confidence: 99%

Healthcare Big Data Voice Pathology Assessment Framework

Hossain

Muhammad

2016

IEEE Access

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The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprises data from different structured, semi-structured and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modelling of this vast heterogeneous volume of data. The present paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern (IDP), are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine (SVM), an extreme learning machine (ELM), and a Gaussian mixture model (GMM) are used as the classifier.In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement. . His research interests include serious games, social media, IoT, cloud and multimedia for healthcare, smart health, and resource provisioning for big data processing on media clouds. He has authored and coauthored around 120 publications including refereed IEEE/ACM/Springer/Elsevier journals, conference papers, books, and book chapters. He has served as a member of the organizing and technical committees of several international conferences and workshops. He has served as co-chair, general chair, workshop chair, publication chair, and TPC for over 12 IEEE and ACM conferences and workshops. Currently, he serves as a co-chair of the 6th IEEE ICME workshop on Multimedia Services

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Section: Mpeg-7 Low-level Audio Featuresmentioning

confidence: 99%

Healthcare Big Data Voice Pathology Assessment Framework

Hossain

Muhammad

2016

IEEE Access

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“…The interest for ASC has been increasing in the last few years and is becoming an important challenge in the machine listening community [1]. ASC has a variety of real life applications such as robotic navigation [2] or forensics [3]. Whilst many context aware devices only use visual information to adapt to their current location, complementary information can be given by analysing the surrounding audio environment.…”

Section: Descriptionmentioning

confidence: 99%

Machine listening techniques as a complement to video image analysis in forensics

Serizel

Bisot

Essid

et al. 2016

2016 IEEE International Conference on Image Processing (ICIP)

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Video is now one of the major sources of information for forensics. However, video documents can be originating from various recording devices (CCTV, mobile devices.. .) with inconsistent quality and can sometimes be recorded in challenging light or motion conditions. Therefore, the amount of information that can be extracted relying solely on video image can vary to a great extent. Most of the videos however generally include audio recording as well. Machine listening can then become a valuable complement to video image analysis in challenging scenarios. In this paper, the authors present a brief overview of some machine listening techniques and their application to the analysis of video documents for forensics. The applicability of these techniques to forensics problems is then discussed in the light of machine listening system performances.

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“…They converted the classifier outputs from SVM and KNN into probabilistic scores and fused them to improve classification accuracy. Muhammad et al [18] combined several low-level MPEG-7 descriptors and MFCC and used Fisher's Discriminant Ratio (F-Ratio) to discard irrelevant features. Although MPEG-7 features perform better than MFCC, MFCC and MPEG-7 descriptors are shown to be complementary to each other and, when used together, the classification accuracy can be improved.…”

Section: Stationary Esr Techniquesmentioning

confidence: 99%

Environmental sound recognition: A survey

Chachada

Kuo

2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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Although research in audio recognition has traditionally focused on speech and music signals, the problem of environmental sound recognition (ESR) has received more attention in recent years. Research on ESR has significantly increased in the past decade. Recent work has focused on the appraisal of non-stationary aspects of environmental sounds, and several new features predicated on non-stationary characteristics have been proposed. These features strive to maximize their information content pertaining to signal's temporal and spectral characteristics. Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds. In this survey, we will offer a qualitative and elucidatory survey on recent developments. It includes three parts: i) basic environmental sound processing schemes, ii) stationary ESR techniques and iii) non-stationary ESR techniques. Finally, concluding remarks and future research and development trends in the ESR field will be given.

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Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients

Cited by 29 publications

References 10 publications

Healthcare Big Data Voice Pathology Assessment Framework

Healthcare Big Data Voice Pathology Assessment Framework

Machine listening techniques as a complement to video image analysis in forensics

Environmental sound recognition: A survey

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