A novel approach using modified filtering algorithm (MFA) for effective completion of cloud tasks

In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities.

A Wearable, Extensible, Open-Source Platform for Hearing Healthcare Research

Hearing loss is one of the most common conditions affecting older adults worldwide. Frequent complaints from the users of modern hearing aids include poor speech intelligibility in noisy environments and high cost, among other issues. However, the signal processing and audiological research needed to address these problems has long been hampered by proprietary development systems, underpowered embedded processors, and the difficulty of performing tests in real-world acoustical environments. To facilitate existing research in hearing healthcare and enable new investigations beyond what is currently possible, we have developed a modern, open-source hearing research platform, Open Speech Platform (OSP). This paper presents the system design of the complete OSP wearable platform, from hardware through firmware and software to user applications. The platform provides a complete suite of basic and advanced hearing aid features which can be adapted by researchers. It serves web apps directly from a hotspot on the wearable hardware, enabling users and researchers to control the system in real time. In addition, it can simultaneously acquire high-quality electroencephalography (EEG) or other electrophysiological signals closely synchronized to the audio. All of these features are provided in a wearable form factor with enough battery life for hours of operation in the field.

A Realtime, Open-Source Speech-Processing Platform for Research in Hearing Loss Compensation

We are developing a realtime, wearable, open-source speech-processing platform (OSP) that can be configured at compile and run times by audiologists and hearing aid (HA) researchers to investigate advanced HA algorithms in lab and field studies. The goals of this contribution are to present the current system and propose areas for enhancements and extensions. We identify (i) basic and (ii) advanced features in commercial HAs and describe current signal processing libraries and reference designs to build a functional HA. We present performance of this system and compare with commercial HAs using "Specification of Hearing Aid Characteristics," the ANSI 3.22 standard. We then describe a wireless protocol stack for remote control of the HA parameters and uploading media and HA status for offline research. The proposed architecture enables advanced research to compensate for hearing loss by offloading processing from ear-level-assemblies, thereby eliminating the bottlenecks of CPU and communication between left and right HAs.

Speech quality and stable gain trade-offs in adaptive feedback cancellation for hearing aids

This paper addresses trade-offs in adaptive feedback cancellation (AFC) for hearing aids. Aggressive AFC for improved added stable gain (ASG) reduces speech quality. In this paper, the hearing-aid speech quality index (HASQI) is used to investigate AFC performance before the system becomes unstable. It is demonstrated that for a desired speech quality, multiple AFC algorithms can be evaluated for their ASG and computational efficiency. An example is presented with HASQI = 0.8, baseline AFC, and two advanced approaches. For the advanced AFCs, ASG gains of 4 and 7 dB were obtained at additional computational complexity of 8% and 11%, respectively.

Speech enhancement in discontinuous transmission systems using the constrained-stability least-mean-squares algorithm

In this paper a novel constrained-stability least-mean-squares (LMS) algorithm for filtering speech sounds is proposed in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the weight vector change under a stability constraint over the a posteriori estimation errors. To this purpose, the Lagrangian methodology has been used in order to propose a nonlinear adaptation in terms of the product of differential input and error. Convergence analysis is also studied in terms of the evolution of the natural modes to the optimal Wiener-Hopf solution so that the stability performance depends exclusively on the adaptation parameter mu and the eigenvalues of the difference matrix DeltaR(1). The algorithm shows superior performance over the referenced algorithms in the ANC problem of speech discontinuous transmission systems, which are characterized by rapid transitions of the desired signal. The experimental analysis carried out on the AURORA 3 speech databases provides an extensive performance evaluation together with an exhaustive comparison to the standard LMS algorithms, i.e., the normalized LMS (NLMS), and other recently reported LMS algorithms such as the modified NLMS, the error nonlinearity LMS, or the normalized data nonlinearity LMS adaptation.

Beamformer performance with acoustic vector sensors in air

For some time, compact acoustic vector sensors (AVSs) capable of sensing particle velocity in three orthogonal directions have been used in underwater acoustic sensing applications. Potential advantages of using AVSs in air include substantial noise reduction with a very small aperture and few channels. For this study, a four-microphone array approximating a small (1 cm3) AVS in air was constructed using three gradient microphones and one omnidirectional microphone. This study evaluates the signal extraction performance of one nonadaptive and four adaptive beamforming algorithms. Test signals, consisting of two to five speech sources, were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio (SNR) of the output. For a three-microphone array, robust and nonrobust versions of a frequency-domain minimum-variance (FMV) distortionless-response beamformer produced SNR improvements of 11 to 14 dB, and a generalized sidelobe canceller (GSC) produced improvements of 5.5 to 8.5 dB. In comparison, a two-microphone omnidirectional array with a spacing of 15 cm yielded slightly lower SNR improvements for similar multi-interferer speech signals.

Performance of time- and frequency-domain binaural beamformers based on recorded signals from real rooms

Extraction of a target sound source amidst multiple interfering sound sources is difficult when there are fewer sensors than sources, as is the case for human listeners in the classic cocktail-party situation. This study compares the signal extraction performance of five algorithms using recordings of speech sources made with three different two-microphone arrays in three rooms of varying reverberation time. Test signals, consisting of two to five speech sources, were constructed for each room and array. The signals were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio of the output. A frequency-domain minimum-variance distortionless-response beamformer outperformed the time-domain based Frost beamformer and generalized sidelobe canceler for all tests with two or more interfering sound sources, and performed comparably or better than the time-domain algorithms for tests with one interfering sound source. The frequency-domain minimum-variance algorithm offered performance comparable to that of the Peissig-Kollmeier binaural frequency-domain algorithm, but with much less distortion of the target signal. Comparisons were also made to a simple beamformer. In addition, computer simulations illustrate that, when processing speech signals, the chosen implementation of the frequency-domain minimum-variance technique adapts more quickly and accurately than time-domain techniques.

Evaluation of array-processing algorithms for a headband hearing aid

Several array-processing algorithms were implemented and evaluated with experienced hearing-aid users. The array consisted of four directional microphones mounted broadside on a headband worn on the top of the listener's head. The algorithms included two adaptive array-processing algorithms, one fixed array-processing algorithm, and a reference condition consisting of binaural directional microphones. The algorithms were evaluated under conditions with both one and three independent noise sources. Performance metrics included quantitative speech reception thresholds and qualitative subject preference ratings for ease-of-listening measured using a paired-comparison procedure. On average, the fixed algorithm improved speech reception thresholds by 2 dB, while the adaptive algorithms provided 7-9-dB improvement over the reference condition. Subjects judging ease-of-listening generally preferred all array-processing algorithms over the reference condition. The results suggest that these adaptive algorithms should be evaluated further in more realistic acoustic environments.

Evaluation of feedback-reduction algorithms for hearing aids

Three adaptive feedback-reduction algorithms were implemented in a laboratory-based digital hearing aid system and evaluated with dynamic feedback paths and hearing-impaired subjects. The evaluation included measurements of maximum stable gain and subjective quality ratings. The continuously adapting CNN algorithm (Closed-loop processing with No probe Noise) provided the best performance: 8.5 dB of added stable gain (ASG) relative to a reference algorithm averaged over all subjects, ears, and vent conditions. Two intermittently adapting algorithms, ONO (Open-loop with Noise when Oscillation detected) and ONQ (Open-loop with Noise when Quiet detected), provided an average of 5 dB of ASG. Subjects with more severe hearing losses received greater benefits: 13 dB average ASG for the CNN algorithm and 7-8 dB average ASG for the ONO and ONQ algorithms. These values are conservative estimates of ASG because the fitting procedure produced a frequency-gain characteristic that already included precautions against feedback. Speech quality ratings showed no substantial algorithm effect on pleasantness or intelligibility, although subjects informally expressed strong objections to the probe noise used by the ONO and ONQ algorithms. This objection was not reflected in the speech quality ratings because of limitations of the experimental procedure. The results clearly indicate that the CNN algorithm is the most promising choice for adaptive feedback reduction in hearing aids.