Sound Signals Research Articles

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

The Critical Thing about the Ear's Sensory Hair Cells.

The capabilities of the human ear are remarkable. We can normally detect acoustic stimuli down to a threshold sound-pressure level of 0 dB (decibels) at the entrance to the external ear, which elicits eardrum vibrations in the picometer range. From this threshold up to the onset of pain, 120 dB, our ears can encompass sounds that differ in power by a trillionfold. The comprehension of speech and enjoyment of music result from our ability to distinguish between tones that differ in frequency by only 0.2%. All these capabilities vanish upon damage to the ear's receptors, the mechanoreceptive sensory hair cells. Each cochlea, the auditory organ of the inner ear, contains some 16,000 such cells that are frequency-tuned between ∼20 Hz (cycles per second) and 20,000 Hz. Remarkably enough, hair cells do not simply capture sound energy: they can also exhibit an active process whereby sound signals are amplified, tuned, and scaled. This article describes the active process in detail and offers evidence that its striking features emerge from the operation of hair cells on the brink of an oscillatory instability-one example of the critical phenomena that are widespread in physics.

Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.

A Framework for Detecting Pulmonary Diseases from Lung Sound Signals Using a Hybrid Multi-Task Autoencoder-SVM Model

Research focuses on the efficacy of Multi-Task Autoencoder (MTAE) models in signal classification due to their ability to handle many tasks while improving feature extraction. However, researchers have not thoroughly investigated the study of lung sounds (LSs) for pulmonary disease detection. This paper introduces a new framework that utilizes an MTAE model to detect lung diseases based on LS signals. The model integrates an autoencoder and a supervised classifier, simultaneously optimizing both classification accuracy and signal reconstruction. Furthermore, we propose a hybrid approach that combines an MTAE and a Support Vector Machine (MTAE-SVM) to enhance performance. We evaluated our model using LS signals from a publicly available database from King Abdullah University Hospital. The model attained an accuracy of 89.47% for four classes (normal, pneumonia, asthma, and chronic obstructive pulmonary disease) and 90.22% for three classes (normal, pneumonia, and asthma cases). Using the MTAE-SVM, the accuracy was further improved to 91.49% for four classes and 93.08% for three classes, respectively. The results indicate that the MTAE and MTAE-SVM have a considerable potential for detecting pulmonary diseases from lung sound signals. This could aid in the creation of more user-friendly and effective diagnostic tools.

A novel monitoring method based on the fusion of sound and image signals for laser welding penetration status

Accurately identifying penetration status is crucial for ensuring high-quality welding. However, relying on a single sensor to monitor the welding status falls short in accuracy. Thus, a novel monitoring method that fuses sound and image is proposed for assessing the laser welding penetration status. First, a microphone and a high-speed camera are employed to collect sound and image signals during welding. Second, to overcome noise challenges in sound signal, a denoising method is proposed that utilizes Grey Wolf Optimization (GWO) to find the optimal parameters for Variational Mode Decomposition (VMD). The time and frequency domain features from the denoised sound and Local Binary Pattern (LBP) features from the image are extracted. These features constitute a high-dimensional dataset, which is reduced using Principal Component Analysis (PCA). Finally, a back propagation neural network (BPNN) is constructed for fusion monitoring. The results demonstrate that fusion monitoring achieves higher accuracy (97.9%) compared to using sound (79.9%) or image (92.3%) features independently. In comparison to other methods like K-Nearest Neighbor (KNN), Naive Bayesian (NB), Support Vector Machine (SVM), and Decision Tree, BPNN fusion monitoring achieves higher accuracies, with increases of 18.09%, 12.79%, 6.53%, and 25.84% respectively. The proposed method helps to promote the development of welding intelligence.

The Relationship Between the Burpee Movement Program and Strength and Endurance Performance Measures in Active Young Adults: A Cross-Sectional Analysis.

This paper aimed to assess the motor performance in the Burpee Movement Program through the acceleration recorded by the Phyphox mobile app and define its relationship to strength and endurance parameters. Altogether, 15 students in physical education teaching completed the 3 × 3 min Burpee Movement Program, consisting of the repeated execution of a single burpee with maximum effort at regular intervals triggered by a sound signal. During the load phase, the intensity of the burpee and the fatigue index expressed in percentages were evaluated by means of the acceleration recorded through a mobile phone. In the second part of testing, we evaluated the performance parameters during a bench press and squat where the intensity was measured using a linear displacement transducer (Tendo Power Analyzer) and aerobic endurance was assessed with a 20 m shuttle run test (20 mSR). The average intensity of the burpee ranged from 3.12 to 11.12 ms-2. The fatigue index ranged from -21.95% (which represented an increase in performance) to 33.63% (which represented a decrease in performance). The performances in the bench presses ranged from 58 to 480 W and from 175 to 696 W during the squats. The distance in the 20 m shuttle run test (20 mSR) ranged from 540 to 2000 m. The intensity of the burpee showed a significant correlation to the performances achieved in the bench presses and squats r = 0.82 and 0.79. The fatigue index showed a significant correlation to the 20 m shuttle run test (20 mSR) r = -0.67. These findings indicate that in, our case, the results from the Burpee Movement Program are significantly associated with the participants' strength and endurance abilities. We recommend using BMP for the development of strength-endurance abilities, but further exploration is needed regarding the potential use of BMP as a diagnostic test.

Deep Learning-Based Gender Recognition in Cherry Valley Ducks Through Sound Analysis.

Gender recognition is an important part of the duck industry. Currently, the gender identification of ducks mainly relies on manual labor, which is highly labor-intensive. This study aims to propose a novel method for distinguishing between males and females based on the characteristic sound parameters for day-old ducks. The effective data from the sounds of day-old ducks were recorded and extracted using the endpoint detection method. The 12-dimensional Mel-frequency cepstral coefficients (MFCCs) with first-order and second-order difference coefficients in the effective sound signals of the ducks were calculated, and a total of 36-dimensional feature vectors were obtained. These data were used as input information to train three classification models, include a backpropagation neural network (BPNN), a deep neural network (DNN), and a convolutional neural network (CNN). The training results show that the accuracies of the BPNN, DNN, and CNN were 83.87%, 83.94%, and 84.15%, respectively, and that the three classification models could identify the sounds of male and female ducks. The prediction results showed that the prediction accuracies of the BPNN, DNN, and CNN were 93.33%, 91.67%, and 95.0%, respectively, which shows that the scheme for distinguishing between male and female ducks via sound had high accuracy. Moreover, the CNN demonstrated the best recognition effect. The method proposed in this study can provide some support for developing an efficient technique for gender identification in duck production.

Molecular specializations underlying phenotypic differences in inner ear hair cells of zebrafish and mice.

Hair cells (HCs) are the sensory receptors of the auditory and vestibular systems in the inner ears of vertebrates that selectively transduce mechanical stimuli into electrical activity. Although all HCs have the hallmark stereocilia bundle for mechanotransduction, HCs in non-mammals and mammals differ in their molecular specialization in the apical, basolateral, and synaptic membranes. HCs of non-mammals, such as zebrafish (zHCs), are electrically tuned to specific frequencies and possess an active process in the stereocilia bundle to amplify sound signals. Mammalian HCs, in contrast, are not electrically tuned and achieve amplification by somatic motility of outer HCs (OHCs). To understand the genetic mechanisms underlying differences between adult zebrafish and mammalian HCs, we compared their RNA-seq-characterized transcriptomes, focusing on protein-coding orthologous genes related to HC specialization. There was considerable shared expression of gene orthologs among the HCs, including those genes associated with mechanotransduction, ion transport/channels, and synaptic signaling. However, there were some notable differences in expression among zHCs, OHCs, and inner HCs (IHCs), which likely underlie the distinctive physiological properties of each cell type. For example, OHCs highly express Slc26a5 which encodes the motor protein prestin that contributes to OHC electromotility. However, zHCs have only weak expression of slc26a5, and subsequently showed no voltage-dependent electromotility when measured. Notably, the zHCs expressed more paralogous genes including those associated with HC-specific functions and transcriptional activity, though it is unknown whether they have functions similar to their mammalian counterparts. There was overlap in the expressed genes associated with a known hearing phenotype. Our analyses unveil substantial differences in gene expression patterns that may explain phenotypic specialization of zebrafish and mouse HCs. This dataset also includes several protein-coding genes to further the functional characterization of HCs and study of HC evolution from non-mammals to mammals.

Morten Qvenild – The HyPer(sonal) Piano Project

Towards a (per)sonal topography of grand piano and electronics How can I develop a grand piano with live electronics through iterated development loops in the cognitive technological environment of instrument, music, performance and my poetics? The instrument I am developing, a grand piano with electronic augmentations, is adapted to cater my poetics. This adaptation of the instrument will change the way I compose. The change of composition will change the music. The change of music will change my performances. The change in performative needs will change the instrument, because it needs to do different things. This change in the instrument will show me other poetics and change my ideas. The change of ideas demands another music and another instrument, because the instrument should cater to my poetics. And so it goes… These are the development loops I am talking about. I have made an augmented grand piano using various music technologies. I call the instrument the HyPer(sonal) Piano, a name derived from the suspected interagency between the extended instrument (HyPer), the personal (my poetics) and the sonal result (music and sound). I use old analogue guitar pedals and my own computer programming side by side, processing the original piano sound. I also take out control signals from the piano keys to drive different sound processes. The sound output of the instrument is deciding colors, patterns and density on a 1x3 meter LED light carpet attached to the grand piano. I sing, yet the sound of my voice is heavily processed, a processing decided by what I am playing on the keys. All sound sources and control signal sources are interconnected, allowing for complex and sometimes incomprehensible situations in the instrument´s mechanisms. Credits: First supervisor: Henrik Hellstenius Second Supervisors: Øyvind Brandtsegg and Eivind Buene Cover photo by Jørn Stenersen, www.anamorphiclofi.com All other photo, audio and video recording/editing by Morten Qvenild, unless stated.

Sound and vibration control crocodile hatching

Abstract The birth of a baby or the hatching of an egg are moments when the risk of predation is particularly high. In oviparous species where clutches contain many eggs, one strategy for limiting predation is based on mechanisms that synchronise hatching, which reduces the individual risk of being targeted and may allow hatchlings to emerge under parental protection. These mechanisms involve communication signals between eggs and between eggs and parents. While previous studies in various species have shown the importance of sound and vibration signals in these exchanges, how embryos integrate this information remains poorly understood. Here we show that crocodile embryos respond differently to calls emitted by siblings (hatching calls) and to vibrations mimicking parental intervention (rubbing and scratching the shell) or the presence of a predator, suggesting that these two stimuli carry different information. Through playback experiments, we confirm that hatching calls elicit vocal responses and synchronous hatching in mature embryos, while embryos hatch earlier when they perceive vibrations. Our study underlines that the control of hatching by external factors can rely on differential apprehension of multiple sensory inputs by embryos.

Computer vision based early fire-detection and firefighting mobile robots oriented for onsite construction

Fires are one of the most dangerous hazards and the leading cause of death in construction sites. This paper proposes a video-based firefighting mobile robot (FFMR), which is designed to patrol the desired territory and will constantly observe for fire-related events to make sure the camera without any occlusions. Once a fire is detected, the early warning system will send sound and light signals instantly and the FFMR moves to the right place to fight the fire source using the extinguisher. To improve the accuracy and speed of fire detection, an improved YOLOv3-Tiny (namely as YOLOv3-Tiny-S) model is proposed by optimizing its network structure, introducing a Spatial Pyramid Pooling (SPP) module, and refining the multi-scale anchor mechanism. The experiments show the proposed YOLOv3-Tiny-S model based FFMR can detect a small fire target with relatively higher accuracy and faster speed under the occlusions by outdoor environment. The proposed FFMR can be helpful to disaster management systems, avoiding huge ecological and economic losses, as well as saving a lot of human lives.

Simulation and experimental research on the propagation mechanisms of acoustic energy inside the pipeline to detect the leakage

Leakage from pipelines has negative impacts on property and environment. The diameters of internal acoustic detectors in pipelines are small and not easily blocked, making it suitable for pipelines under various working conditions. Studying the characteristics of pipeline leakage sound signal and the propagation mechanisms within pipelines is crucial for timely detection of small leaks. In this study, a 3D simulation model of pipeline leakage was established, and a CFD/CAA hybrid calculation method was used to obtain effective leakage flow field information via LES. The equivalent sound sources in the flow field were extracted based on the Lighthill acoustic analogy theory. Determine the characteristic frequency range of the leakage sound signal was 1–2 kHz. This study founded that an increase in the leakage pressure and aperture size both lead to an enhancement in the leakage sound signal, and the propagation of leakage sound signal in pipelines is affected by background flow. An experimental pipeline leakage system was designed, and the accuracy of the simulation results was verified experimentally.

Coal-gangue sound recognition using hybrid multi-branch CNN based on attention mechanism fusion in noisy environments

The coal-gangue recognition technology plays an important role in the intelligent realization of fully mechanized caving face and the improvement of coal quality. Although great progress has been made for the coal-gangue recognition in recent years, most of them have not taken into account the impact of the complex environment of top coal caving on recognition performance. Herein, a hybrid multi-branch convolutional neural network (HMBCNN) is proposed for coal-gangue recognition, which based on improved Mel Frequency Cepstral Coefficient (MFCC) as well as Mel spectrogram, and attention mechanism. Firstly, the MFCC and its smooth feature matrix are input into each branch of one-dimensional multi-branch convolutional neural network, and the spliced features are extracted adaptively through multi-head attention mechanism. Secondly, the Mel spectrogram and its first-order derivative are input into each branch of the two-dimensional multi-branch convolutional neural network respectively, and the effective time-frequency information is paid attention to through the soft attention mechanism. Finally, at the decision-making level, the two networks are fused to establish a model for feature fusion and classification, obtaining optimal fusion strategies for different features and networks. A database of sound pressure signals under different signal-to-noise ratios and equipment operations is constructed based on a large amount of data collected in the laboratory and on-site. Comparative experiments and discussions are conducted on this database with advanced algorithms and different neural network structures. The results show that the proposed method achieves higher recognition accuracy and better robustness in noisy environments.

Study of the acoustic features and exposures of some typical construction noise sources in India

Occupational noise research and occupational noise standards to date are limited to continuous, non-fluctuating broadband noise and uniform exposure. However, in many occupational environments, such as the construction sites, the workers may be exposed to continuous or intermittent noise of varied spectral and temporal patterns, and their exposure may not be continuous or uniform. These differences in sound signals and sound exposures may have different effect on humans. This paper studies the sound signals of various sources in construction sites of the institute campus through in-situ measurements. The key acoustic features and the spectral content of the sound signals are analyzed. The findings show that construction workers in India are exposed to construction noise with widely varying spectral content. A modified permissible upper limit of construction noise is developed for the presented case study and it is found that five out of eight studied construction sources violate the permissible limit and pose a noise hazard to the construction workers. The study suggests the need for updating occupational noise guidelines based on the noise types and exposure types; and imposing stricter measures for protecting the workers against the construction noise.

Experimental Study on Pressure Oscillation Characteristics of Di-N-Butyl Ether in a Constant Volume Vessel

ABSTRACT This paper conducted experimental research on spontaneous pressure oscillation characteristics of di-n-butyl ether (DBE). Based on a constant volume vessel (CVV) with 210 mm in length and 180 mm in diameter, the initial pressure, temperature, and equivalence ratio ranges of 0.05–0.3 MPa, 373–453 K, and 0.7–1.6. The experimental data of combustion pressure, CVV vibration and sound pressure were analyzed. The results indicate that DBE exhibits the strongest pressure oscillation at high pressures, moderate temperatures and equivalence ratios. The highest oscillation intensity is achieved at 0.3 MPa, 433 K, equivalence ratio of 1.4. The vibration and sound pressure signals exhibit the same patterns as combustion pressure signals. Spectrum analysis is conducted by Fast Fourier Transform of pressure signals, it shows that the amplitude of the characteristic frequency increases with the increase of oscillation intensity. In addition, to further reveal the influence of combustion instability on oscillation intensity, the dimensionless parameter Peclet number and critical radius are compared, it shows that the combustion instability has strong effect on pressure oscillation, and diffusive-thermal instability is the key factor that influences pressure oscillation. This study is believed to be beneficial for organizing better combustion for internal combustion engines.

Sound quality evaluation and quantification for power seat operating noise

In recent years, vehicle interior condition at running has been quiet due to progress of noise reduction technology. On the other hand, quietness is not required only at running condition but also at parking where slight operating noise is generated by various devices. In this study, we then focused on an operating noise during power seat operation as one of the devices radiated noise in cabin. The impression to power seat operating noise were investigated through subjective evaluation test. For the test, power seat radiated noise at various operating modes and seat types were recorded binaurally and presented in the subjective evaluation test. Participants evaluated the presented sound quality in several evaluation terms such as "loudness," "pitch," and "discomfort." As the results, representative power seat operating mode, in which sound impression varies widely among presented sounds, was found out and suitable evaluation terms for describing the sound quality characteristics well were also extracted. In addition, the representative sound quality tendency was attempted to be evaluated quantitatively using psychoacoustic metrics from recorded sound pressure signal of the power seat.

A time-domain approach to predict sound radiation from track vibrations

With the intended shift of transport from road to railway, noise exposure due to railway noise will increase. Auralisation of the noise is a key tool for assessing noise perception beyond equivalent sound pressure levels. Researching the connection between, for example, track design parameters and perceived sound quality holds potential for perception-centered track design. A computationally efficient model is required to render the sound field generated by the track vibration. In this paper, using simulated time-domain surface velocities of a rail and sleepers as input, such a model is presented. The sound pressure signal in a position on the side of the track is efficiently predicted by convolving these velocities with precalculated acoustic transfer functions. For a typical ballasted track, the necessary spatial discretisation of the evaluated velocities is determined in a convergence study. Further, the required length of the finite section of the track to be included in the prediction is specified. Finally, pass-by signals generated by track vibration are demonstrated.

Annoyance caused by simultaneous exposure to noise and seat vibrations inside of a mini excavator: multimodal interaction and the effects of changes in low-frequency content of the sound signal

Drivers of commercial vehicles are exposed to noise and vibrations during their occupation. The signal characteristics of these exposures can lead to different degrees of annoyance perception, even below the legal daily exposure limits. In addition, simultaneous exposure to sound and vibration may lead to potential interaction effects between the two stimuli, influencing the total annoyance perception. Therefore, a perception experiment was designed to ask the participants about the perceived total annoyance caused by simultaneous noise and seat vibrations in commercial vehicles, presenting modified sound and seat vibrations, recorded inside of a mini-excavator. Multimodal interaction effects of noise and vibrations were investigated by changing the intensity of both stimuli. Furthermore, the influence of low-frequency signal components of the sound signal with and without simultaneous seat vibrations was investigated by specific changes in the sound spectrum.

Modelling the effects of a random medium on direct and reflected sound signals from a moving source

Randomnesses in acoustic propagation conditions affect the signal at a receiver placed at a distance from the source. Without any reflecting surface the random medium causes fluctuations in amplitude and phase of the received signal. In the presence of a wave reflecting surface, a version of the signal with other fluctuations is added at the receiver, with some amount of correlation between the direct signal and the reflected signal. A method is presented that in a single approach includes the random fluctuations of the received signals and the decorrelation effect for signal pairs. Numerical examples are calculated and validated in terms of mutual coherence functions by comparison with analytical solutions for different conditions of atmospheric turbulence. Exemplifying sounds with different source signals are produced for a moving source.

Experimental study of shooting sound around semi-open shooting ranges

Measurements of shooting sound around semi-open shooting ranges are analyzed. Recorded sound signals show bullet sound and muzzle sound contributions. Time differences between bullet and muzzle sound pulses are explained by calculation of acoustical travel times. In general bullet sound contributions are stronger in forward directions (angles 0 to 90 degrees with respect to the shooting direction) than in backward directions. A representative frequency is defined to characterize the relative importance of bullet sound in the emitted sound spectrum. This is further analyzed by means of silencers on the firearms, as silencers suppress the muzzle sound but not the bullet sound. The effect of acoustical absorption material on the shooting range is also investigated. Differences of the order of 10 dB between different shooting ranges are attributed to acoustical absorption material of the overhead screens (baffles) on the range. For practical applications it would be useful to represent a shooting range by a single point source with a direction-dependent sound emission spectrum. The accuracy of this approximation is investigated.