Fesal Toosy^{1, *}, Muhammad Sarwar Ehsan¹

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1253-1270, 2020, DOI:10.32604/cmc.2020.011667

Abstract Mobile phones and other handheld electronic devices are now ubiquitous and play an important role in our everyday lives. Over the last decade, we have seen a sharp rise in the sophistication of both hardware and software for these devices, thus significantly increasing their utility and use. Electronic devices are now commonly used for the streaming of audio and video and for the regular playback of music. Multichannel audio has now become a popular format and with recent updates in software, the latest audio codecs that support this format can effectively be played back on most electronic devices. As a… More >

Hossein ElahiShirvan¹, MohammadReza Ghotbi-Ravandi², Sajad Zare^3,*, Mostafa Ghazizadeh Ahsaee⁴

Sound & Vibration, Vol.54, No.2, pp. 99-112, 2020, DOI:10.32604/sv.2020.08839

Abstract Workers’ exposure to excessive noise is a big universal work-related challenges. One of the major consequences of exposure to noise is permanent or transient hearing loss. The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’ hearing loss based using the Support Vector Machine (SVM) algorithm. This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran. The participating workers (n = 150) were divided into three groups of 50 based on the sound pressure level to which they were exposed (two experimental groups and one control group). Audiometric tests… More >

T. Jayasree^1,*, R. Prem Ananth²

Sound & Vibration, Vol.54, No.1, pp. 57-74, 2020, DOI:10.32604/sv.2020.08573

Abstract Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets… More >

Lamia Bouafif¹, Noureddine Ellouze²

Sound & Vibration, Vol.52, No.6, pp. 2-10, 2018, DOI:10.32604/sv.2018.02410

Abstract Sound indexing and segmentation of digital documents especially in the internet and digital libraries are very useful to simplify and to accelerate the multimedia document retrieval. We can imagine that we can extract multimedia files not only by keywords but also by speech semantic contents. The main difficulty of this operation is the parameterization and modelling of the sound track and the discrimination of the speech, music and noise segments. In this paper, we will present a Speech/Music/Noise indexing interface designed for audio discrimination in multimedia documents. The program uses a statistical method based on ANN and HMM classifiers. After… More >