Acoustic Echo and Noise Cancellation System for Hand-Free Telecommunication using Variable Step Size Algorithms

In this paper, acoustic echo cancellation with doubletalk detection system is implemented for a hand-free telecommunication system using Matlab. Here adaptive noise canceller with blind source separation (ANC-BSS) system is proposed to remove both background noise and far-end speaker echo signal in presence of double-talk. During the absence of double-talk, far-end speaker echo signal is cancelled by adaptive echo canceller. Both adaptive noise canceller and adaptive echo canceller are implemented using LMS, NLMS, VSLMS and VSNLMS algorithms. The normalized cross-correlation method is used for double-talk detection. VSNLMS has shown its superiority over all other algorithms both for double-talk and in absence of double-talk. During the absence of double-talk it shows its superiority in terms of increment in ERLE and decrement in misalignment. In presence of double-talk, it shows improvement in SNR of near-end speaker signal

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Subjective response to synthesized flight noise signatures of several types of V/STOL aircraft

Subjective response to far field noise characteristics of V/STOL aircraft sized to carry 60 passengers over 500 mile rang

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

Cerebral perfusion MR imaging using FAIR-HASTE in chronic carotid occlusive disease: comparison with dynamic susceptibility contrast-perfusion MR imaging.

To determine the efficacy of flow-sensitive alternating inversion recovery using half-Fourier single-shot turbo spin-echo (FAIR-HASTE) in detecting cerebral hypoperfusion in chronic carotid occlusive disease, we subjected 12 patients with various degrees of cervical internal carotid artery stenoses and/or occlusion (Stenosis group) and 24 volunteers (Normal group) to FAIR-HASTE. In addition, 10 out of 12 patients in the Stenosis group underwent dynamic susceptibility contrast-perfusion magnetic resonance imaging (DSC-pMRI) before and after revascularization in the dominantly affected side. The absolute asymmetry indexes (AIs) of both cerebral hemispheres in the Normal and Stenosis groups were compared in FAIR-HASTE. In addition, the AIs were compared with those in the Stenosis group before and after revascularization in both FAIR-HASTE and regional cerebral blood flow (rCBF), calculated with DSC-pMRI. A statistically significant difference was recognized between the AIs in the Normal and Stenosis groups (AI = 2.25 +- 1.92, 8.09 +- 4.60, respectively ; p < 0.0001). Furthermore, in the Stenosis group the AIs on both FAIR-HASTE (8.88 +- 4.93, 2.22 +- 1.79, respectively ; p = 0.0003) and rCBF (7.13 +- 3.57, 1.25 +- 1.33, respectively ; p = 0.0003) significantly decreased after revascularization. In the Stenosis group, before revascularization, signal intensity on both FAIR-HASTE and rCBF had a tendency to be lower in the dominantly affected side. FAIR-HASTE imaging was useful in the detection and evaluation of cerebral hypoperfusion in chronic occlusive carotid disease

The Influence of Training Method on Tone Colour Discrimination

This research addresses the question of whether one of two training methods, identification by continuous adjustment (ICA) or identification by successive approximation (ISA), is more effective in training students using a technical ear training program (TETP). No known empirical studies have examined the effectiveness of either training method within frequency spectrum-based student-targeted TETPs. Preliminary work involved the development of appropriate tests of students’ tone colour discrimination ability in isolation, on tasks sufficiently different from those encountered in TETPs. The tests were then deployed in a pilot study within a pre/post-training scenario using two groups of audio engineering students, one of which undertook an ICA and the other an ISA version of a TETP. These preliminary results indicated the suitability of a test that featured pairwise comparisons of synthetic percussive timbres to show differences in performance between the two training groups. This test was subsequently administered repeatedly in a full-scale study at regular intervals throughout a web-based TETP, in addition to before and after training. Results of the full-scale study showed the individual differences scaling (INDSCAL)-derived stimulus spaces for both groups were similar prior to undertaking the TETP. The ISA group’s post-training results were almost identical to their pre-training results, whereas the ICA groups’ post-training results showed minor, but insignificant differences. Although the full-scale study found insignificant differences in performance between training groups, the preliminary results suggest that the deployment of a pre/post-training test is an effective measure of the training method’s influence on students if the test features a task that is significantly different from those trained on in the TETP