Sounding Mechanism of a Flue Organ Pipe-A Multi-Sensor Measurement Approach.

This work presents an approach that integrates the results of measuring, analyzing, and modeling air flow phenomena driven by pressurized air in a flue organ pipe. The investigation concerns a Bourdon organ pipe. Measurements are performed in an anechoic chamber using the Cartesian robot equipped with a 3D acoustic vector sensor (AVS) that acquires both acoustic pressure and air particle velocity. Also, a high-speed camera is employed to observe the jet coming out from the windway. For that purpose, the steam resulting from dry ice and hot water is used. A numerical simulation of the sounding mechanism of a pipe of the same geometry is based on measuring the pressure signal and the intensity field around the mouth employing an intensity probe and visualizing and observing the motion of the air jet, which represents the excitation mechanism of the system. The ParaVIEW software serves for the simulation and visualization of the air jet. Then, the results obtained from measurements and simulations are compared and discussed. Also, some future directions discussing the application of a machine-learning approach to the area of pipe organ air flow investigation are contained in the Conclusions section.

Noise profiling for speech enhancement employing machine learning models.

This paper aims to propose a noise profiling method that can be performed in near real time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features derived from the Aurora noise dataset. This is to select the best-performing classifier in the context of noise profiling. Therefore, a comparison of all classifier outcomes is shown based on effectiveness metrics. Also, confusion matrices prepared for all tested models are presented. The second part of the experiment consists of selecting the algorithm that scored the best, i.e., Naive Bayes, resulting in an accuracy of 96.76%, and using it in a noise-type recognition model to demonstrate that it can perform in a stable way. Classification results are derived from the real-life recordings performed in momentary and averaging modes. The key contribution is discussed regarding speech intelligibility improvements in the presence of noise, where identifying the type of noise is crucial. Finally, conclusions deliver the overall findings and future work directions.

Musical Instrument Identification Using Deep Learning Approach.

The work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata description and a review of related works. This is followed by showing the dataset prepared for the experiment and its division into subsets: training, validation, and evaluation. Then, the analyzed architecture of the neural network model is presented. Based on the described model, training is performed, and several quality metrics are determined for the training and validation sets. The results of the evaluation of the trained network on a separate set are shown. Detailed values for precision, recall, and the number of true and false positive and negative detections are presented. The model efficiency is high, with the metric values ranging from 0.86 for the guitar to 0.99 for drums. Finally, a discussion and a summary of the results obtained follows.

Detecting Lombard Speech Using Deep Learning Approach.

Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks (CNNs) and various two-dimensional (2D) speech signal representations. To reduce the computational cost and not resign from the 2D representation-based approach, a strategy for threshold-based averaging of the Lombard effect detection results is introduced. The pseudocode of the averaging process is also included. A series of experiments are performed to determine the most effective network structure and the 2D speech signal representation. Investigations are carried out on German and Polish recordings containing Lombard speech. All 2D signal speech representations are tested with and without augmentation. Augmentation means using the alpha channel to store additional data: gender of the speaker, F0 frequency, and first two MFCCs. The experimental results show that Lombard and neutral speech recordings can clearly be discerned, which is done with high detection accuracy. It is also demonstrated that the proposed speech detection process is capable of working in near real-time. These are the key contributions of this work.

A Novel Method for Intelligibility Assessment of Nonlinearly Processed Speech in Spaces Characterized by Long Reverberation Times.

Objective assessment of speech intelligibility is a complex task that requires taking into account a number of factors such as different perception of each speech sub-bands by the human hearing sense or different physical properties of each frequency band of a speech signal. Currently, the state-of-the-art method used for assessing the quality of speech transmission is the speech transmission index (STI). It is a standardized way of objectively measuring the quality of, e.g., an acoustical adaptation of conference rooms or public address systems. The wide use of this measure and implementation of this method on numerous measurement devices make STI a popular choice when the speech-related quality of rooms has to be estimated. However, the STI measure has a significant drawback which excludes it from some particular use cases. For instance, if one would like to enhance speech intelligibility by employing a nonlinear digital processing algorithm, the STI method is not suitable to measure the impact of such an algorithm, as it requires that the measurement signal should not be altered in a nonlinear way. Consequently, if a nonlinear speech enhancing algorithm has to be tested, the STI-a standard way of estimating speech transmission cannot be used. In this work, we would like to propose a method based on the STI method but modified in such a way that it makes it possible to employ it for the estimation of the performance of the nonlinear speech intelligibility enhancement method. The proposed approach is based upon a broadband comparison of cumulated energy of the transmitted envelope modulation and the received modulation, so we called it broadband STI (bSTI). Its credibility with regard to signals altered by the environment or nonlinear speech changed by a DSP algorithm is checked by performing a comparative analysis of ten selected impulse responses for which a baseline value of STI was known.

Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network.

The goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined with a convolutional neural network (CNN). In the first experiment, we compare the effectiveness of the similarity matrices applied to discerning acoustic differences between consonant phonemes of the Polish and Lithuanian languages. The similarity matrices built on both an extensive set of parameters and a reduced set after removing high-correlated parameters are used. The results show that higher accuracy is obtained by the similarity matrices without discarding high-correlated parameters. In the second experiment, the averaged accuracies of the similarity matrices obtained are compared with the results provided by spectrograms combined with CNN, as well as the results of the vectors containing acoustic parameters and two baseline classifiers, namely k-nearest neighbors and support vector machine. The performance of the similarity matrix approach demonstrates its superiority over the methods used for comparison.

Evaluation of aspiration problems in L2 English pronunciation employing machine learning.

The approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers' pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones were prepared by experts in English phonetics and phonology. The datasets created include recordings of words pronounced by nine native English speakers of standard southern British accent and 20 Polish L2 English users. Complete unedited words are treated as input data for feature extraction and classification algorithms such as k-nearest neighbors, naive Bayes method, long-short term memory, and convolutional neural network (CNN). Various signal representations, including low-level audio features, the so-called mid-term and feature trajectory, and spectrograms, are tested in the context of their usability for the detection of aspiration. The results obtained show high potential for an automated evaluation of pronunciation focused on a particular phonological feature (aspiration) when classifiers analyze whole words. Additionally, CNN returns satisfying results for the automated classification of words containing aspirated and unaspirated allophones produced by Polish L2 speakers.

Introduction to the special issue on machine learning in acoustics.

The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information about events observed in acoustic data. Acoustic data provide scientific and engineering insight ranging from biology and communications to ocean and Earth science. This special issue included 61 papers, illustrating the very diverse applications of ML in acoustics.

Acoustic Sensing Analytics Applied to Speech in Reverberation Conditions.

The paper aims to discuss a case study of sensing analytics and technology in acoustics when applied to reverberation conditions. Reverberation is one of the issues that makes speech in indoor spaces challenging to understand. This problem is particularly critical in large spaces with few absorbing or diffusing surfaces. One of the natural remedies to improve speech intelligibility in such conditions may be achieved through speaking slowly. It is possible to use algorithms that reduce the rate of speech (RoS) in real time. Therefore, the study aims to find recommended values of RoS in the context of STI (speech transmission index) in different acoustic environments. In the experiments, speech intelligibility for six impulse responses recorded in spaces with different STIs is investigated using a sentence test (for the Polish language). Fifteen subjects with normal hearing participated in these tests. The results of the analytical analysis enabled us to propose a curve specifying the maximum RoS values translating into understandable speech under given acoustic conditions. This curve can be used in speech processing control technology as well as compressive reverse acoustic sensing.

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch's method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

Knowledge representation of motor activity of patients with Parkinson's disease.

An approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity disorders in patients. Obtained results indicate that it is possible to interpret some selected patient's body movements with a sufficiently high effectiveness.

Exploring the technological dimension of Autonomous sensory meridian response-induced physiological responses.

Background: In recent years, the scientific community has been captivated by the intriguing Autonomous sensory meridian response (ASMR), a unique phenomenon characterized by tingling sensations originating from the scalp and propagating down the spine. While anecdotal evidence suggests the therapeutic potential of ASMR, the field has witnessed a surge of scientific interest, particularly through the use of neuroimaging techniques including functional magnetic resonance imaging (fMRI) as well as electroencephalography (EEG) and physiological measures such as eye tracking (Pupil Diameter), heart rate (HR), heartbeat-evoked potential (HEP), blood pressure (BP), pulse rates (PR), finger photoplethysmography (PPG), and skin conductance (SC). This article is intended to provide a comprehensive overview of technology's contributions to the scientific elucidation of ASMR mechanisms. Methodology: A meticulous literature review was undertaken to identify studies that have examined ASMR using EEG and physiological measurements. The comprehensive search was conducted across databases such as PUBMED, SCOPUS, and IEEE, using a range of relevant keywords such as 'ASMR', 'Autonomous sensory meridian response', 'EEG', 'fMRI', 'electroencephalography', 'physiological measures', 'heart rate', 'skin conductance', and 'eye tracking'. This rigorous process yielded a substantial number of 63 PUBMED and 166 SCOPUS-related articles, ensuring the inclusion of a wide range of high-quality research in this review. Results: The review uncovered a body of research utilizing EEG and physiological measures to explore ASMR's effects. EEG studies have revealed distinct patterns of brain activity associated with ASMR experiences, particularly in regions implicated in emotional processing and sensory integration. In physiological measurements, a decrease in HR and an increase in SC and pupil diameter indicate relaxation and increased attention during ASMR-triggered stimuli. Conclusions: The findings of this review underscore the significance of EEG and physiological measures in unraveling the psychological and physiological effects of ASMR. ASMR experiences have been associated with unique neural signatures, while physiological measures provide valuable insights into the autonomic responses elicited by ASMR stimuli. This review not only highlights the interdisciplinary nature of ASMR research but also emphasizes the need for further investigation to elucidate the mechanisms underlying ASMR and explore its potential therapeutic applications, thereby paving the way for the development of novel therapeutic interventions.

Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt.

This paper describes a novel way to measure, process, analyze, and compare respiratory signals acquired by two types of devices: a wearable sensorized belt and a microwave radar-based sensor. Both devices provide breathing rate readouts. First, the background research is presented. Then, the underlying principles and working parameters of the microwave radar-based sensor, a contactless device for monitoring breathing, are described. The breathing rate measurement protocol is then presented, and the proposed algorithm for octave error elimination is introduced. Details are provided about the data processing phase; specifically, the management of signals acquired from two devices with different working principles and how they are resampled with a common processing sample rate. This is followed by an analysis of respiratory signals experimentally acquired by the belt and microwave radar-based sensors. The analysis outcomes were checked using Levene's test, the Kruskal-Wallis test, and Dunn's post hoc test. The findings show that the proposed assessment method is statistically stable. The source of variability lies in the person-triggered breathing patterns rather than the working principles of the devices used. Finally, conclusions are derived, and future work is outlined.

Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction.

The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set-based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

Evoked stapedius reflex and compound action potential thresholds versus most comfortable loudness level: assessment of their relation for charge-based fitting strategies in implant users.

AIM: The main goal of the present study was to assess the feasibility of using evoked stapedius reflex (eSR) and evoked compound action potential (eCAP) thresholds to create speech processor programs for children using Med-El Maestro software. The secondary goals were (1) to compare the eSR and eCAP thresholds recorded using charge units in experienced adults fitted with Med-El Pulsar CI100 cochlear implants with most comfortable loudness levels (MCLs) obtained for the apical, medial and basal electrodes, and (2) to compare eSR and eCAP thresholds for the apical, medial and basal electrodes between adults and children. METHODS: Fourteen children and 16 adults participated in the study. eSR and eCAP thresholds were measured in both groups using the auditory nerve response telemetry algorithm, with MCL being behaviourally measured only in the adult group. RESULTS: In the adult population, the correlation between eSR threshold and MCL was better for apical, medial and basal electrodes than that between eCAP threshold and MCL. There was no significant difference in the means obtained for eCAP and eSR thresholds in children and adults for any of the electrodes tested. This finding suggests that in children, the correlations between eCAP thresholds and MCL values, and those between eSR thresholds and MCL values are not lower than those generally found in adults. CONCLUSIONS: Although the eSR threshold is a better predictor of MCL values, both eSR and eCAP thresholds can be useful tools for assisting with map creation for children.

Problems of railway noise-a case study.

Under Directive 2002/49/EC relating to the assessment and management of environmental noise, all European countries are obliged to model their environmental noise levels in heavily populated areas. Some countries have their own national method, to predict noise but most have not created one yet. The recommendation for countries that do not have their own model is to use an interim method. The Dutch SRM II scheme is suggested for railways. In addition to the Dutch model, this paper describes and discusses 3 other national methods. Moreover, discrepancies between the HARMONOISE and IMAGINE projects are analysed. The results of rail traffic noise measurements are compared with national methods.

A new method for measuring the psychoacoustical properties of tinnitus.

BACKGROUND: This study investigates the usefulness and effectiveness of a new way of tinnitus screening and diagnosing. The authors believe that in order to arrive at relevant diagnostic information, select the tinnitus treatment and quantitatively substantiate its effects, the measurement of the Tinnitus psychoacoustic parameters should be made an inherent part of the Tinnitus therapy. METHODS: For this purpose the multimedia-based sound synthesizer has been proposed for testing tinnitus and the results obtained this way are compared with the outcome of the audiometer-based Wilcoxon test. The method has been verified with 14 patients suffering from tinnitus. RESULTS: The experiments reveal capabilities, limitations, advantages and disadvantages of both methods. The synthesizer enables the patient to estimate his/her tinnitus more than twice as fast as the audiometer and makes the information on the tinnitus character perception more accurate. The analysis of the Wilcoxon test results shows that there are statistically important differences between the two tests. CONCLUSIONS: Patients using the synthesizer operate the software application themselves and thus get more involved in testing. Moreover, they do not concentrate on describing verbally their tinnitus, which could be difficult for some of them. As a result, the test outcome is closer to the perceived tinnitus. However, the more complex the description of the perceived tinnitus, the harder it is to determine the sound parameters of the patient's perception. It also takes more time regardless of the method. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1954066324109436.

Automatic assessment of the motor state of the Parkinson's disease patient--a case study.

This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of the Parkinson's Disease can be automatically assessed. For this purpose, past and current UPDRS data from 47 subjects were examined. The results show that, among other classifiers, the rough set-based decision algorithm turned out to be most suitable for such automatic assessment. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1563339375633634.

Waveguide model of the hearing aid earmold system.

BACKGROUND: The earmold system of the Behind-The-Ear hearing aid is an acoustic system that modifies the spectrum of the propagated sound waves. Improper selection of the earmold system may result in deterioration of sound quality and speech intelligibility. Computer modeling methods may be useful in the process of hearing aid fitting, allowing physician to examine various earmold system configurations and choose the optimum one for the hearing aid user. METHODS: In this paper, a computer model adequate for this task is proposed. This model is based on the waveguide modeling method. The waveguide model simulates the propagation of sound waves in the system of cylindrical tubes. Frequency response of the hearing aid receiver is simulated in the model and the influence of the ear canal and the eardrum on the earmold system is taken into account. The model parameters are easily calculated from parameters of a physical hearing aid system. Transfer function of the model is calculated and frequency response plots are obtained using the Matlab system. RESULTS: The frequency response plots of the earmold model were compared to the measurement plots of the corresponding physical earmold systems. The same changes in frequency responses caused by modification of length or diameter of a selected waveguide section, are observed in both measurement data of a real earmold system and in computed model responses. CONCLUSION: Comparison of model responses obtained for various sets of parameters with measurement data proved that the proposed model accurately simulates the real earmold system and the developed model may be used to construct a computer system assisting the physician who performs earmold system fitting.