This work presents a novel approach for speech enhancement in scenarios where clean data is unavailable but noise type is known, using a modified Conditional Generative Adversarial Network (cGAN) with a Residual Attention UNet (RAUNet) as the generator. The proposed Noise-to-Noisy-to-Noise (N2N2N) algorithm works on known noise types to iteratively refine speech signals, achieving high performance even in low signal-to-noise ratio (SNR) conditions such as -15 dB. We demonstrate that our method surpasses existing solutions in terms of robustness, phase preservation, and computational efficiency. Experimental results validate the effectiveness of our approach, showcasing significant improvements in PESQ, STOI, CBAK, COVL and SSNR metrics across various noise environments.

Amyotrophic Lateral Sclerosis (ALS) is one of the most common neuromuscular diseases which affects both lower and upper motor neurons. In this paper, a dilated one dimensional convolutional neural network, named ALSNet, is proposed for identifying ALS from raw EMG signal. No hand-crafted feature extraction is required, rather, ALSNet is able to take raw EMG signal as input and detect EMG signals of ALS subjects. This makes the method more feasible for practical implementation by reducing the computational cost required for extracting features. To our best knowledge, no research work for identification of ALS from raw EMG signal has been conducted yet. The performance of the ALSNet was evaluated using popular metrics such as overall accuracy, sensitivity, specificity and balanced accuracy and compared with other existing methods. The proposed method showed a better performance than the other existing methods with an overall accuracy of 97.74%.

Prosthetic leg is an artificial device that replaces a missing leg, which may be lost through accident, disease, or due to a condition at birth. The development of prosthetic legs has provided a solution for the rehabilitation of the amputees so that their mobility and ability to manage daily activities can be improved. But this is only possible if a smart intent recognition system is added with it. The existing electromyography-based systems are too expensive to be afforded by the mass. In this paper, a low-cost pattern recognition-based method is proposed for locomotion mode recognition (LMR) by using a combination of angular data from multiple gyroscope sensors combined with a force sensor. Three types of locomotion activities including walking, ascending and descending staircase are identified in this work. A dataset with sufficient amount of gait cycle values for each activity is developed to create the classifier model, and train and test it. Finally, the performance is assessed through both offline and online locomotion mode detection.

Non-biodegradable product usage and ignorance towards proper disposal are creating the problem of ever-growing trash stacks. An autonomous mobile trash collector which collects trash lying on ground in a trash-container attached to it can be a feasible solution to the problem. In this procedure trash detection is done via deep learning algorithm. An ultrasonic sonar sensor on the robot detects object along the path and a camera module sends pictures of the object to raspberry pi for classification into trash or not trash. The prototype robot is of low-cost and can detect a wide range of trash with high accuracy. Therefore has good environmental as well as economic impact.

In the society, most of the disabled people especially completely movement impaired ones live like burdens and suffer from severe depression as they have to depend on others for day-to-day activities and cannot contribute to the community. These people may not be able to move their arms or legs but they can certainly think. If their ability to think and make decisions can be utilized in decision making situations and also if they can further be included in industrial automation structure, they will no longer be burden rather they will become man-power to the society. One of the major challenges of involving these people in such processes is making a low cost device that is affordable by the mass, made with locally available components and also interprets the person's choice accurately. As these people are physically disable, comprehending their electroencephalogram (EEG) signal for blink detection can be an effective solution for translating their choices. EEG-based controlling devices are mobile and can serve as powerful aids for severely disabled people in their daily life. In this work, a low cost EEG sensor is made with readily available components and a framework is developed to detect voluntary eye blinks. A graphical user interface (GUI) is also developed which is suitable for the impaired person. The eye blinks of the person act as bridge between a set of choices pointed in the GUI and his/her selection interest and thus help the person control home appliances, wheel chair and even computer without depending on others.

This paper proposes a system for converting handwritten words and numbers into a text file. Our system uses A CNN based method to identify letter and digits. This Automatic system requires image preprocessing, classifying into letters and digits and saving the letter into a text file. The system consists of a touchscreen as a user interface, an Arduino board (microcontroller ATmega 2560) and MATLAB. Any type of handwriting is tested with the classifying process; we got 89% accuracy using our own dataset. Both letter and digits can be recognized and converted into text file using this process. The proposed system will lessen the labor of creating electronic documents and provide easy preservation of data.

The greatest challenge for hearing impaired people is the realization of speech in a noisy environment. For the compensation of hearing losses, hearing aid devices are used by hearing impaired people because of being programmable to regulate gain values with different frequencies, reduction of noise for speech enhancement and improved signal to noise ratio (SNR) which have made it better in performance than analog hearing aid. The speech signal is always deteriorated by background noise in a noisy environment which degrades the original speech. To develop the speech quality, perceptibility and degree of listener's exhaustion, speech enhancement is done using various methods. In this paper, we propose a configurable digital hearing aid system with reduction of noise for speech enhancement using spectral subtraction method using MATLAB. In this work, configurable digital hearing aid includes a combination of frequency dependent amplification of speech and noise reduction filter for background noise reduction which will provide more flexibility to hearing impaired people along with improvement of quality of the speech signal.

A real-time sign language translator is an important milestone in facilitating communication between the deaf community and the general public. We hereby present the development and implementation of an American Sign Language (ASL) fingerspelling translator based on skin segmentation and machine learning algorithms. We present an automatic human skin segmentation algorithm based on color information. The YCbCr color space is employed because it is typically used in video coding and provides an effective use of chrominance information for modeling the human skin color. We model the skin-color distribution as a bivariate normal distribution in the CbCr plane. The performance of the algorithm is illustrated by simulations carried out on images depicting people of different ethnicity. Then Convolutional Neural Network (CNN) is used to extract features from the images and Deep Learning Method is used to train a classifier to recognize Sign Language.

The paper presents the design methodology and implementation of a low-cost spirometer prototype that can detect pulmonary diseases. By sensing the pressure difference of the expiratory air passing through the 3D printed venturi tube, the system calculates forced vital capacity (FVC). Through free campaigns, the performance and efficiency of this venturi tube spirometer were tested in different locations and the accuracy rate was highly promising. With this great efficiency and accuracy, this model diagnoses chronic obstructive pulmonary disease (COPD). Apart from measuring FVC, the model also measures forced expiratory volume in one second (FEV1) and from their ratio, it identifies COPD. In underprivileged areas, each and every hospital cannot afford spirometers available in the market because of their high price. This low-cost spirometer model ensures proper detection of pulmonary diseases in those underprivileged areas and it is definitely going to add a new dimension to the diagnosis of COPD.

Biometric authentication is a process of identifying and differentiating individuals for security purpose that counts on the distinctive physiological and behavioral characteristics of a person for verification. Dorsal vein pattern is one of the prospective biometrics and we have made successful use of this biometric feature for authentication purpose. This paper describes a novel design and its implementation for identifying individuals on the basis of their vein pattern of dorsal hand. The prime goal has been to establish a method with better Correct Recognition Rate (CRR), low False Acceptance Rate (FAR) and low False Rejection Rate (FRR). For our paper we have used Near Infrared (NIR) image of dorsal hand as it provides better resolution of vein pattern in the image than visible light. This recognition system consists of several steps; they are denoising of the input image using Laplacian Scale Mixture Modeling (LSM), Region of Interest (ROI) extraction from denoised image using valley point detection method, contrast enhancement using pyramid based edge aware filtering method, contrast limited adaptive histogram equalization, binarization, several serial morphological operations and finally authentication using neural networks and Support Vector Machine (SVM) classifier. The experiment was initially performed on a database of 16 distinct subjects where there are 10 raw images of each subject taken in different conditions. The accuracy obtained from experimental results is 96.63%. The scale of the experiment can be extended for more users, which we believe will yield similar success and thus this method of biometric authentication using dorsal vein pattern can be used in security purpose.

Wheelchairs provide immense impact for the physically challenged as they offer mobility with relative ease. But in cases like severe disability or seeking relief from physical exertion, there is a necessity for automated electric wheelchairs which are very expensive. In this paper, an innovative design and implementation of a robust and user-friendly smart electric wheelchair are presented, where the product cost is minimized considering the affordability of a large group of people. A notable feature of the proposed scheme is `Destination Mapping' by which the smart wheelchair learns the destinations accessed by the user and autonomously reaches those destinations afterward using speech recognition. There are also various smart features such as rough surface detection, torque adjustments, slope and obstacle detection which promise a comfortable and safe ride. The proposed system takes input from a joystick, microphone, rotary encoders and sonar sensors, processes them through a microcontroller and eventually drives the motors through a motor driver. After much study on selecting suitable motors for implementation, windshield wiper motors were used as they produce quite enough torque at low rpm and are locally available as recycled parts. Experimentation has been carried out with 50 disabled persons of various age groups and subsequent improvements were undertaken based on their feedback over the timeframe of 1 year. Satisfactory results were obtained which implies that the proposed wheelchair can really serve as a low-cost smart alternative to aid the physically challenged, which has a tremendous potential impact on society.

Elderly people in our society may be visually challenged, physically weak and thus face troubles during day-to-day life movement. Due to poor vision, often they fail to detect obstacles around them and hurt themselves. Due to aging, abnormal involuntary movements and lack of a timely rescue system, the magnitude of injury and other adverse effects worsens. One possible way to reduce these old age sufferings could be to design an assistive device which can take corrective measures based on position, neighborhood information and the event of fall (if any). The aim of this research is to develop a cost effective, comfortable and easily producible smart-hat for safe and smooth movement of the elderly people. Since wearing a hat is a common phenomenon for elderly people, smart-hat will get ease of acceptance by them. Some of the key features to be offered by the smart-hat are — obstacle detection, providing aid in the movement in a straight path and fall detection. No such technology exists at present that incorporates all these features in a single device. The proposed device uses a very simple methodology. This system takes reading from an accelerometer and differentiates a fall from other normal activities using an adaptive threshold set in triaxial directions. This device aids an elderly person to walk straight using the angular displacement of head movement obtained from a gyroscope. Lastly, few sonar sensors are attached to the hat to sense obstacles around the user covering a 360-degree range. During an event of fall, the system keeps sending request for help to pre-assigned caregivers by using a GSM module, until any of them responds. Also, the device makes wireless arrangement for opening the lock of the front door for the ease of rescue operation when the victim is at home. The device showed an overall success rate of nearly 86.25%. The implications of this safe and smooth movement assistant would be to reduce the number of accidents, decrease the time of arrival of medical attention, increase supervision capability, thus reducing injuries as well as mortality rate; which in turn will ensure independent living of the target group.

In his paper a method of seizure detection has been proposed based on the Discrete Wavelet Transform (DWT) analysis of the dominant Intrinsic mode function(IMF) resulting from the Empirical Mode Decomposition(EMD) of the EEG signals. Considering the normalized energy, Fourier spectrum and cross-correlation coefficient analysis, only the 4th Level DWT coefficients of the dominant IMF is found reasonable for feature computation. In order to reduce the dimension of the feature vector, Higher order statistics of these coefficients are employed to form he feature vector. The reduced feature vector thus formed is found effective for distinguishing seizure and non-seizure EEG signals when fed to a k-nearest neighborhood (k-NN) classifier. Extensive simulations are carried out using a benchmark EEG dataset. It is shown that the proposed method is capable of producing greater sensitivity, specificity and accuracy in comparison to that obtained by a sate-of-the-art method using the same EEG dataset and classifier.

This paper presents a speech enhancement approach, where an adaptive threshold is statistically determined based on Student$t$Modeling of Teager energy (TE) operated perceptual wavelet packet (PWP) coefficients of noisy speech. In order to obtain an enhanced speech, the threshold thus derived is applied upon the PWP coefficients by employing a Student$t$pdf dependent custom thresholding function, which is designed based on a combination of modified hard and semisoft thresholding functions. Extensive simulations are carried out using the NOIZEUS database to evaluate the effectiveness of the proposed method for car and multi-talker babble noise corrupted speech signals. Several standard objective measures and subjective evaluations including formal listening tests show that the proposed method outperforms some of the state-of-the-art speech enhancement methods at high as well as low levels of SNRs.

This paper presents a speaker-independent speech emotion recognition method, where emotional features are derived from the Teager energy (TE) operated wavelet coefficients of speech signal. Due to TE operation, the enhanced detail as well as approximate Wavelet coefficients thus obtained is then used to compute entropy. Entropy values of TE operated detail and approximate wavelet coefficients not only reduces feature dimension but also form an effective feature vector for distinguishing different emotions when fed to a Euclidean distance based classifier. Extensive simulations are carried out using EMO-DB German speech emotion database containing four class emotions, such as angry, happy, sad and neutral. Simulation results show that the proposed method is capable of outperforming an existing speaker-independent emotion recognition method thus solving a four-class emotion recognition problem in terms of higher recognition accuracy with lower computation.

Amyotrophic lateral sclerosis (ALS) is a common fatal motor neuron disease that assails the nerve cells in the brain. As the nervous system controls the muscle activity, the electromyography (EMG) signals can be viewed and examined in order to detect the vital features of the ALS disease in individuals. In this paper, the discrete wavelet transform (DWT) based features, which are extracted from a frame of EMG data, are introduced to classify the normal person and the ALS patients. From each frame of EMG data, instead of using a large number of DWT coefficients, the DWT coefficients with higher values as well as their mean and maxima are proposed to be used, which drastically reduces the feature dimension. It is shown that the proposed feature vector offers a high within class compactness and between class separations. For the purpose of classification, the K-nearest neighborhood classifier is employed. In order to demonstrate the classification performance, an EMG database consisted of 5 normal subjects and 5 ALS patients is considered and it is found that the proposed method is capable of distinctly separating the ALS patients from the normal persons.

In this paper, a phase spectrum based acoustic parameter is presented for the detection of nasalized vowels from the mixture of oral and nasalized vowels of normal speakers. Acoustic analysis shows that during the event of nasalization, although additional formants (resonances) at various frequency locations are introduced, the introduction of a new formant in low frequency region around 250 Hz is found to remain consistent irrespective of female or male speakers in the modified group delay derived from the phase spectrum. By exploiting and verifying this fact on the band-limited modified group delay spectrum capable of resolving two closely spaced formants, an acoustic parameter RMGD is derived. Utilizing RMGD, the problem of detecting nasalized vowels is solved based on a threshold based scheme or a Euclidean distance based classifier. Simulation Results on TIMIT database show that the proposed method even with a simple classifier is superior in performance in comparison to that of the methods using Mel-frequency cepstral coefficients as a feature and Hidden Markov Modeling or Support Vector Machine as a classifier.

This paper presents an approach to detect voice disorders based on wavelet and prosody-related voice properties. First, several statistical measures of the normalized energy contents of the Discrete Wavelet Transform (DWT) coefficients over all voice frames are determined. Then, similar statistical measures of some prosody-related voice properties, such as mean pitch, jitter and shimmer are also computed over all the frames. In order to form a feature vector to be used in both training and testing phases, a set of statistical measure of the normalized energy contents of the DWT coefficients is combined with a set of statistical measure of the extracted prosody-related voice properties. Here, the voice samples under consideration are assumed to be of two categories, namely healthy and disordered thus formulating the problem in the proposed method as a two-class problem to be solved. Finally, the feature vector as obtained above is fed to an Euclidean Distance based classifier to detect the disordered voice. By performing extensive simulations, it is shown that the statistical analysis based on wavelet and prosody-related properties are able to provide effective detection of a variety of voice disorders from the mixture of healthy and disordered voices.

In this paper, a method for the estimation of pitch from noise-corrupted speech observations based on extracting a pitch harmonic and the corresponding harmonic number is proposed. Starting from the harmonic representation of clean speech, a simple yet accurate harmonic sinusoidal autocorrelation (HSAC) model is first derived. By employing this HSAC model expressed in terms of the pitch harmonics of the clean speech, a new autocorrelation-domain least-squares fitting optimization technique is developed to extract a pitch harmonic from the noisy speech. Then, the harmonic number associated with the pitch harmonic is determined by maximizing an objective function formulated as an impulse-train weighted symmetric average magnitude sum function (SAMSF) of the noisy speech. The period of the impulse-train is governed by the estimated pitch harmonic and the maximization of the objective function is carried out through a time-domain matching of periodicity of the impulse-train with that of the SAMSF. An SAMSF-based pitch tracking scheme using dynamic programming is devised to obtain a smoothed pitch contour. In order to demonstrate the efficacy of the proposed method, simulations are conducted by considering naturally spoken speech signals in the presence of white or multi-talker babble noise at different signal-to-noise ratio (SNR) levels. A comprehensive evaluation of the pitch estimation results shows the superiority of the proposed method over some of the state-of-the-art methods under low levels of SNR.

An effective method for pitch estimation from severely noise-corrupted speech is presented in this paper. We argue that an accurate estimate of a pitch-harmonic (PH) can be obtained from the Discrete Cosine Transform (DCT) power spectrum of an enhanced frame. By exploiting the PH thus extracted, a spectral matching method is developed in the DCT domain to determine the true harmonic number associated to the PH that leads to a pitch estimate. In a heavy noisy scenario, the superior efficacy of the proposed pitch estimation method relative to some of the existing ones is confirmed through simulation results using theKeeledatabase.

In this paper, a new algorithm for pitch extraction from noisy speech signals based on both temporal and spectral representations is presented. We derive a harmonic sinusoidal correlation (HSC) model of clean speech as a temporal representation. Given only a noisy speech frame, a noise-robust least-squares minimization technique is proposed to acquire the parameters of the HSC model which are directly employed for the accurate estimation of a pitch-harmonic (PH). Exploiting the extracted PH and based on a spectral representation which is an enhanced spectrum in the discrete cosine transform domain, a two-fold criterion is developed in order to achieve the true consecutive number corresponding to PH that is finally adopted for pitch detection in the presence of noise. Simulation results using the Keele pitch extraction reference database manifest that combining the multi cues obtained from the temporal as well as spectral representations, the proposed algorithm is able to achieve a superior efficacy in comparison to some of the existing methods from high to very low signal-to-noise ratio (SNR) levels.

A novel algorithm for pitch frequency (PF) estimation from non-stationary noise-corrupted speech observations is presented in this paper based on both spectral pre-processing and temporal representation. A modified power spectral subtraction based de-noising scheme that allows tracking the time-variation of the underlying non-stationary noise is put forward to enhance speech prior to PF estimation. The de-noised speech is then utilized to propose a squared difference function of the Linear Prediction (LP) residual which is expected to reveal more prominent dips at integral multiples of the pitch period compared to that revealed by the LP residual. The dips at different pitch-harmonic locations are added and weighted by a periodicity dependent weighting factor for every possible pitch period thus yielding a weighted and harmonically summed temporal function which is globally minimized to extract the desired PF. Simulation results using the Keele database show the superior efficacy of the proposed method in the presence of a multi-talker babble noise relative to some of the existing methods.

In this paper, we present a robust pitch estimation algorithm for noise-degraded speech. We propose a new circular average magnitude sum function (CAMSF) and a pseudo normalized correlation function (PNCF) both of which exhibit the periodicity at the pitch period of voiced speech. Exploiting the fact that CAMSF produces a peak while PNCF shows a notch, an integrated time-domain function (ITDF) is developed to enhance the pitch-harmonic-notches in presence of noise. Moreover, a frequency-frame relative smoothed noisy spectrum that acts as a harmonic spectral structure enhancer is utilized to accurately acquire a pitch-harmonic (PH) from noisy speech. We argued that employing the PH, pitch information can be effectively extracted through a variable-period impulse-train in conjunction with the proposed ITDF. It has been ascertained that the overall algorithm simulated using the Keele reference database is able to outperform some of the existing methods and well suited for a wide range of signal-to-noise ratios (SNRs) upto-10 dB.

A two-step algorithm for the voiced/unvoiced (V/UV) decision of colored noise-corrupted speech is presented in this paper. An effective noise-whitening process is first applied to the noisy speech to combat the adverse effect of colored noise. Then, a harmonicity measure is proposed which is derived from the LP residual of the pre-whitened speech. Integrating root-mean-square energy and zero-crossing rate, another composite measure is introduced. In the first step, signal-dependent initial-thresholds (SDITs) for both the measures which are capable of highlighting distinctive attributes of voiced and unvoiced frames, are determined analyzing their statistical properties. In the second step, based on the SDITs, a bi-feature logical target function is formulated to attain a preliminary score of V/UV decision. Additional voicing criteria are developed to conquer the artifacts that may exist due to the overlapping between decision regions. Simulation results demonstrate that the proposed algorithm yields superior performance in comparison with some of the existing V/UV decision schemes in the same interfering colored noise scenario.

This paper presents a new algorithm for the voiced/unvoiced (V/UV) decision of noise-corrupted speech. A speech periodicity-harmonic function (SPHF) is proposed to manifest distinctive characteristics between voiced and unvoiced regions. A composite feature vector is developed by combining a periodicity measure obtained from the SPHF with some energy measures such as zero-crossing rate-weighted RMS energy, Kaiser-Teager frame energy and the normalized low-frequency energy ratio. Unlike the conventional hard threshold, a signal-dependent initial-threshold (SDIT) for each feature is determined based on its statistical properties. The SDIT is exploited to develop a logical expression that returns an objective score regarding V/UV region. Additional voicing criteria are introduced to remove the artifacts that may exist due to the overlapping between decision regions. Simulation results of the proposed multifeature classification scheme, using the Keele reference database, show superior efficacy at a low SNR relative to some of the existing V/UV decision algorithms

In this paper, we present a joint time/frequency domain approach for pitch estimation of speech at a very low SNR. The kernel of this approach lies in introducing a new function for detecting the time-domain cue by modifying the circular average magnitude difference function (CAMDF). By using the new function in conjunction with the half-wave rectified version of the autocorrelation function, the pitch-peak can be emphasized and the non-pitch peaks suppressed. To guarantee a robust pitch detection in noisy speech, a priori frequency-domain estimate of the dominant pitch-harmonic is extracted as an additional cue and is utilized to optimally match the pitch-peak in time-domain. The proposed approach is simulated using the Keele reference database. It is shown that the proposed method using joint time and frequency domain cues is able to give a superior accuracy relative to some of the existing methods even at a very low SNR of -10 dB.