A SILENCE REMOVAL AND ENDPOINT DETECTION APPROACH FOR SPEECH PROCESSING

In this paper a brief overview of silence removal and voice activity detection is discussed and a new method for silence removal is suggested. The objective of suggested method is to delete the silence and unvoiced segments from the speech signal which are very useful to increase the performance and accuracy of the system. Endpoint detection is used to remove the DC offset value from the signal after silence removal process. Silence removal and Endpoint detection are main part of many applications such as speaker and speech recognition. The proposed method uses Root Mean Square (RMS) to delete the unvoiced segments from the speech signal. This work showed better results for silence removal and endpoint detection than existing methods. The performance of this research work is evaluated using MATLAB tool and accuracy of 97.2% is achieved

A NOVEL APPROACH FOR ERROR DETECTION AND CORRECTION USING GATED CORRECTION METHOD

Data communication is the process of exchanging information between sender and receiver. The basic objective of a communication system is to transmit data which is free of error. Advancement in technology has made various revolutions in data communication, with which come greater chances that the data to be sent becomes corrupted. The data is transferred from various transmission impairments and during this period various factors affect the signal, the data received at the receiver is different from the data transmitted. As digital signals exist in two states either high or low, the error occurred will change its state. In today's advance world different techniques have been made to detect and remove error in the data. The paper delivers a simple error correction and detection method which can detect and correct single, multiple and burst error simply by using XNOR and COMPLEMENT. In the proposed method key is calculated and is send as a redundant bits at the receiver different operations are made to get the data that was originally sent. This error correction is a step ahead of hamming code. This paper also discusses the shortcomings of hamming code

A NOVEL APPROACH FOR ERROR DETECTION AND CORRECTION USING GATED CORRECTION METHOD

Data communication is the process of exchanging information between sender and receiver. The basic objective of a communication system is to transmit data which is free of error. Advancement in technology has made various revolutions in data communication, with which come greater chances that the data to be sent becomes corrupted. The data is transferred from various transmission impairments and during this period various factors affect the signal, the data received at the receiver is different from the data transmitted. As digital signals exist in two states either high or low, the error occurred will change its state. In today's advance world different techniques have been made to detect and remove error in the data. The paper delivers a simple error correction and detection method which can detect and correct single, multiple and burst error simply by using XNOR and COMPLEMENT. In the proposed method key is calculated and is send as a redundant bits at the receiver different operations are made to get the data that was originally sent. This error correction is a step ahead of hamming code. This paper also discusses the shortcomings of hamming code

A SILENCE REMOVAL AND ENDPOINT DETECTION APPROACH FOR SPEECH PROCESSING

In this paper a brief overview of silence removal and voice activity detection is discussed and a new method for silence removal is suggested. The objective of suggested method is to delete the silence and unvoiced segments from the speech signal which are very useful to increase the performance and accuracy of the system. Endpoint detection is used to remove the DC offset value from the signal after silence removal process. Silence removal and Endpoint detection are main part of many applications such as speaker and speech recognition. The proposed method uses Root Mean Square (RMS) to delete the unvoiced segments from the speech signal. This work showed better results for silence removal and endpoint detection than existing methods. The performance of this research work is evaluated using MATLAB tool and accuracy of 97.2% is achieved

An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization

Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal. The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution. The selection of an appropriate window size is difficult when no background information about the input signal is known. In this paper, a novel empirical model is proposed that adaptively adjusts the window size for a narrow band-signal using spectrum sensing technique. For wide-band signals, where a fixed time-frequency resolution is undesirable, the approach adapts the constant Q transform (CQT). Unlike the STFT, the CQT provides a varying time-frequency resolution. This results in a high spectral resolution at low frequencies and high temporal resolution at high frequencies. In this paper, a simple but effective switching framework is provided between both STFT and CQT. The proposed method also allows for the dynamic construction of a filter bank according to user-defined parameters. This helps in reducing redundant entries in the filter bank. Results obtained from the proposed method not only improve the spectrogram visualization but also reduce the computation cost and achieves 87.71% of the appropriate window length selection

Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing

Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by an audiologist to assess a patient’s auditory system. However, the procedure requires an appointment, which can result in delays and practitioner fees. In addition, there are often challenges associated with the unavailability of equipment and qualified practitioners, particularly in remote areas. This paper presents a novel idea that automatically identifies any hearing impairment based on a cognitively inspired feature extraction and speech recognition approach. The proposed system uses an adaptive filter bank with weighted Mel-frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated hearing test method to achieve comparable results to the clinical ground truth, established by the expert audiologist’s tests. The overall absolute error of the proposed model when compared with the expert audiologist test is less than 4.9 dB and 4.4 dB for the pure tone and speech audiometry tests, respectively. The overall accuracy achieved is 96.67% with a hidden Markov model (HMM). The proposed method potentially offers a second opinion to audiologists, and serves as a cost-effective pre-screening test to predict hearing loss at an early stage. In future work, authors intend to explore the application of advanced deep learning and optimization approaches to further enhance the performance of the automated testing prototype considering imperfect datasets with real-world background noise

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA

An Online-Offline Certificateless Signature Scheme for Internet of Health Things

The Internet of Health Things (IoHT) is an extended breed of the Internet of Things (IoT), which plays an important role in the remote sharing of data from various physical processes such as patient monitoring, treatment progress, observation, and consultation. The key benefit of the IoHT platform is the ease of time-independent interaction from geographically distant locations by offering preventive or proactive healthcare services at a lower cost. The communication, integration, computation, and interoperability in IoHT are provided by various low-power biomedical sensors equipped with limited computational capabilities. Therefore, conventional cryptographic solutions are not feasible for the majority of IoHT applications. In addition, executing computing-intensive tasks will lead to a slow response time that can deteriorate the performance of IoHT. We strive to resolve such a deficiency, and thus a new scheme has been proposed in this article, called an online-offline signature scheme in certificateless settings. The scheme divides the signing part into two phases, i.e., online and offline. In the absence of a message, the offline phase performs computationally intensive tasks, while lighter computations are executed in the online phase when there is a message. Security analyses and comparisons with the respective existing schemes are carried out to show the feasibility of the proposed scheme. The results obtained authenticate that the proposed scheme offers enhanced security with lower computational and communication costs

AI-Enabled Wearable Medical Internet of Things in Healthcare System: A Survey

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems