Hardware-Accelerated, Short-Term Processing Voice and Nonvoice Sound Recognitions for Electric Equipment Control

Tsai, Wen–Chung; Shih, You-Jyun; Huang, Nien-Ting

doi:10.3390/electronics8090924

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Thus, it is partially consistent with the second category presented in Reference [13], and in particular, with the HFB-based solution described there. It should be noted that HFBs are called hardware accelerators in some publications [18].…”

Section: Different Ways Of Algorithms Implementation On Fpgamentioning

confidence: 99%

Fixed-Point Arithmetic Unit with a Scaling Mechanism for FPGA-Based Embedded Systems

Przybył¹

2021

Electronics

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The work describes the new architecture of a fixed-point arithmetic unit. It is based on the use of integer arithmetic operations for which the information about the scale of the processed numbers is contained in the binary code of the arithmetic instruction being executed. Therefore, this approach is different from the typical way of implementing fixed-point operations on standard processors. The presented solution is also significantly different from the one used in floating-point arithmetic, as the decision to determine the appropriate scale is made at the stage of compiling the code and not during its execution. As a result, the real-time processing of real numbers is simplified and, therefore, faster. The described method provides a better ratio of the processing efficiency to the complexity of the digital system than other methods. In particular, the advantage of using the described method in FPGA-based embedded control systems should be indicated. Experimental tests on an industrial servo-drive confirm the correctness of the described solution.

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Section: Different Ways Of Algorithms Implementation On Fpgamentioning

confidence: 99%

Fixed-Point Arithmetic Unit with a Scaling Mechanism for FPGA-Based Embedded Systems

Przybył¹

2021

Electronics

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“…The process of extracting the MFCCs consists of several mathematical transformations of the signal from the time domain to the frequency domain, to the quefrency domain. This comprises the Fourier transform and the discrete cosine transforms to obtain the log-magnitude representation of the spectrum [ 22 , 23 ]. The generation of MFCC vectors consists of the following steps: Pre-emphasizing the input signal to remove unwanted or high frequencies.…”

Section: Introductionmentioning

confidence: 99%

Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Habib¹,

Faris²,

Qaddoura

et al. 2021

Sensors

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Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

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“…Examples of such filter architectures are digital filters. Digital filtering is employed in a vast cluster of applications such as voice encoding/decoding [4], image processing [5], control systems [6], data compression [7], and telecommunications [8], to name a few. While many filtering tasks use digital signal processing, continuous-time filters are still important.…”

Section: Introductionmentioning

confidence: 99%

CMOS Analog Filter Design for Very High Frequency Applications

et al. 2020

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A design strategy for the synthesis of high-selectivity/low-order analog filters in Complementary Metal-Oxide-Semiconductor (CMOS) technology for very high frequency (VHF) applications is presented. The methodology for the reconstitution of a given transfer function by means of Signal Flow Graphs (SFG) manipulation in canonical form is proposed leading to a fully differential g m -C biquad filter. As a practical example, the design of a notch filter intended to suppress interferers in the lower sideband (400 MHz) of the Medical Implant Communication Service (MICS), in single-poly, 6-metal layers; Mixed-Signal/RF 0.18 µm CMOS technology is realized. To compare the performance of the proposal with some other solution, the design of a 7th order elliptic notch filter based on Frequency Dependent Negative Resistors (FDNRs) was also accomplished. The attained simulation results prove that the proposal is competitive compared to the FDNR solution and some other state-of-the-art filters reported in the literature. The most salient features of the proposed notch biquad include: the selectivity, whose value is comparable to that of a 7th order elliptic approach and some other 3rd order filters; a high-frequency operation without resonators; linearity, with a +15 dBm I I P 3 ; a reduced form factor with a total occupied area of 0.004282 mm2 and mostly a low design complexity.

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Hardware-Accelerated, Short-Term Processing Voice and Nonvoice Sound Recognitions for Electric Equipment Control

Cited by 3 publications

References 18 publications

Fixed-Point Arithmetic Unit with a Scaling Mechanism for FPGA-Based Embedded Systems

Fixed-Point Arithmetic Unit with a Scaling Mechanism for FPGA-Based Embedded Systems

Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

CMOS Analog Filter Design for Very High Frequency Applications

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