A review on speech enhancement techniques

Chaudhari, Amol; Dhonde, S. B.

doi:10.1109/pervasive.2015.7087096

Cited by 25 publications

(8 citation statements)

References 10 publications

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“…Researchers have been searching for biomarkers in SF that can be used to diagnose OA earlier and with greater reliability. However, the composition of SF is complex, and it is difficult to effectively study its single components, so it requires the development of signal enhancement techniques [ 115 ]. As an important component of SF, HA has attracted the attention of researchers.…”

Section: Raman Spectroscopymentioning

confidence: 99%

Vibrational Spectroscopy in Assessment of Early Osteoarthritis—A Narrative Review

Chen

Zhao

et al. 2021

IJMS

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Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.

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Section: Raman Spectroscopymentioning

confidence: 99%

Vibrational Spectroscopy in Assessment of Early Osteoarthritis—A Narrative Review

Chen

Zhao

et al. 2021

IJMS

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“…An essential part of speech signal processing is also the suppression of additive noise in the speech signal using single-channel or multichannel methods [ 61 ], for example, single-channel methods like speech enhancement using spectral subtraction-type algorithms [ 62 ], use of complex adaptive methods of signal processing [ 63 , 64 ], model-based speech enhancement [ 65 , 66 ], increasing additive noise removal in speech processing using spectral subtraction [ 67 ], and noise reduction of speech signal using wavelet transform with modified universal threshold [ 68 ] or denoising speech signals by wavelet transform [ 69 ]. …”

Section: Related Workmentioning

confidence: 99%

Voice Communication in Noisy Environments in a Smart House Using Hybrid LMS+ICA Algorithm

Martínek

Vaňuš

Nedoma

et al. 2020

Sensors

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This publication describes an innovative approach to voice control of operational and technical functions in a real Smart Home (SH) environment, where, for voice control within SH, it is necessary to provide robust technological systems for building automation and for technology visualization, software for recognition of individual voice commands, and a robust system for additive noise canceling. The KNX technology for building automation is used and described in the article. The LabVIEW SW tool is used for visualization, data connectivity to the speech recognizer, connection to the sound card, and the actual mathematical calculations within additive noise canceling. For the actual recognition of commands, the SW tool for recognition within the Microsoft Windows OS is used. In the article, the least mean squares algorithm (LMS) and independent component analysis (ICA) are used for additive noise canceling from the speech signal measured in a real SH environment. Within the proposed experiments, the success rate of voice command recognition for different types of additive interference (television, vacuum cleaner, washing machine, dishwasher, and fan) in the real SH environment was compared. The recognition success rate was greater than 95% for the selected experiments.

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“…Initial attempts at speech denoising involved applying linear filters to the mixture to reduce or eliminate the noise portion, most prominently Wiener filtering [25], by estimating the noise statistics. In this review, speech denoising methods are placed into two main categories [26][27][28][29]: conventional methods, including Wiener filtering, spectral subtraction, and Minimum Mean Square Error (MMSE) methods [30][31][32][33][34][35][36][37][38][39], and more recent deep learning-based methods [40][41][42][43][44][45][46][47][48][49][50][51]. Conventional methods attempt to estimate the statistical attributes in the mixture.…”

Section: Introductionmentioning

confidence: 99%