Sound Characteristics Research Articles

Study on sound wave kinematic characteristics and temperature sensing mechanism during the warming process of loose coals

Coal spontaneous combustion is a major threat for safe production. In order to achieve the application of sound wave technology to the monitoring and early warning of coal spontaneous combustion, clarify the propagation characteristics of sound waves in loose coal heating process, and illustrate the temperature sensing mechanism of the sound wave detection and early warning of coal spontaneous combustion, the present study built a test system of sound wave kinematic parameter for loose coals to survey the attenuation characteristics of sound wave propagation during loose coal heating. The elastic wave CT algorithm was used to reconstruct the sound wave velocity field, clarify the propagation path, and identify the temperature anomaly area. The results show that with the increase of temperature, the time for sound wave propagation reduces rapidly and then slowly, the velocity increases gradually, and the internal structure and pores of coals become enlarged. In the sound velocity field reconstructed on the basis of SIRT algorithm, the area with high value corresponds to the high temperature area of the coal, which better reflects the changes of the temperature anomaly area during coal heating.

Music Mode: Transforming Robot Movement into Music Increases Likability and Perceived Intelligence

As robots enter everyday spaces like offices, the sounds they create affect how they are perceived. We present “Music Mode,” a novel mapping between a robot’s joint motions and sounds, programmed by artists and engineers to make the robot generate music as it moves. Two experiments were designed to characterize the effect of this musical augmentation on human users. In the first experiment, a robot performed three tasks while playing three different sound mappings. Results showed that participants observing the robot perceived it as more safe, animate, intelligent, anthropomorphic, and likable when playing the Music Mode Orchestra software. To test whether the results of the first experiment were due to the Music Mode algorithm, rather than music alone, we conducted a second experiment. Here the robot performed the same three tasks, while a participant observed via video, but the Orchestra music was either linked to its movement or random. Participants rated the robots as more intelligent when the music was linked to the movement. Robots using Music Mode logged approximately two hundred hours of operation while navigating, wiping tables, and sorting trash, and bystander comments made during this operating time served as an embedded case study. This paper has both designerly contributions and engineering contributions. The contributions are: (1) an interdisciplinary choreographic, musical, and coding design process to develop a real-world robot sound feature, (2) a technical implementation for movement-based sound generation, and (3) two experiments and an embedded case study of robots running this feature during daily work activities that resulted in increased likeability and perceived intelligence of the robot.

Sound absorption characteristics of 3‐dimensional printed biodegradable structure backed with luffa fiber

AbstractIn this work, an investigation has been carried out on the sound absorption properties of the microperforated composites prepared by filling natural luffa fiber and synthetic luffa fiber. These composite structures are created by 3‐dimensional printing of polylactic acid, which exhibits excellent biodegradability. After preparing the structure, the micro‐perforation is done at the top of the composite with the help of a heated cylindrical drill. During the printing process, the infill density (printing parameter) and type of luffa fiber (natural and synthetic) have been varied to find out its effect on acoustic properties. Finally, a comparative study is carried out among natural luffa fiber and without using natural luffa fiber. The results showed excellent sound absorption of luffa filled structure compared to an unfilled structure. Out of three different infilled densities, the density of 5 % revealed a maximum amount of absorption, suggesting the potential applications of these structures in the field of sound absorption.

Vibration and noise mechanism of a 110 kV transformer under DC bias based on finite element method

Global energy and environmental issues are becoming increasingly problematic, and the vibration and noise problem of 110 kV transformers, which are the most widely distributed, have attracted widespread attention from both inside and outside the industry. DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers. To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias, a multi-field coupling model of a 110 kV transformer was established using the finite element method. The electromagnetic, vibration, and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC, harmonic, and DC power sources. It was found that a DC bias can cause significant distortions in the magnetic flux density, force, and displacement distributions of the core and winding. The contributions of the DC bias effect to the core and winding are different at Kdc = 0.85. At this point, the core approached saturation, and the increase in the core force and displacement slowed. However, the saturation of the core increased the leakage flux, and the stress and displacement of the winding increased faster. The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics. When the DC bias coefficient was 1.25, the noise sound pressure level reached 73.6 dB.

A broadband active sound absorber with adjustable absorption coefficient and bandwidth.

Broadband adjustable sound absorbers are desired for controlling the acoustic conditions within enclosed spaces. Existing studies on acoustic absorbers, either passive or active, aim to maximize the sound absorption coefficients over an extended frequency band. By contrast, this paper introduces a tunable acoustic absorber, whose working frequency band and sound absorption characteristics can be defined by users for different applications. The approach leverages an error signal that can be synthesized using a standing wave separation technique. The error signal encodes different target reflection coefficients, leading to arbitrary absorption coefficients between 0 and 1. Experimental validation is conducted in a one-dimensional standing wave tube, demonstrating that the proposed active absorber achieves near-perfect absorption within the 150-1600 Hz frequency range, boasting an average absorption coefficient of 0.98. Adjustable absorption is demonstrated across three octave bands, aligning closely with theoretical predictions. Furthermore, when coupled with a shaping filter, the absorber exhibits spectrally tunable broadband absorption capabilities, selectively reflecting specific frequency bands while effectively absorbing others. These outcomes underscore the versatile tunability of the proposed active acoustic absorber, which is expected to pave the way for personalized regulating of the indoor acoustic environment.

Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals

One of the most common cardiovascular diseases is coronary artery disease (CAD). Thus, it is crucial for early CAD diagnosis to control disease progression. Computer-aided CAD detection often converts heart sounds into graphics for analysis. However, this method relies heavily on the subjective experience of experts. Therefore, in this study, we proposed a method for CAD detection using raw heart sound signals by constructing a fusion framework with two CAD detection models: a multidomain feature model and a medical multidomain feature fusion model. We collected heart sound signal datasets from 400 participants, extracting 206 multidomain features and 126 medical multidomain features. The designed framework fused the same one-dimensional deep learning features with different multidomain features for CAD detection. The experimental results showed that the multidomain feature model and the medical multidomain feature fusion model achieved areas under the curve (AUC) of 94.7 % and 92.7 %, respectively, demonstrating the effectiveness of the fusion framework in integrating one-dimensional and cross-domain heart sound features through deep learning algorithms, providing an effective solution for noninvasive CAD detection.

Sound feature analysis and gender recognition based on deep learning: A review

The application of deep learning in identifying sound features has become increasingly prevalent, greatly enhancing the performance of voice applications across various professional domains. This study focuses on deep learning techniques applied to sound feature analysis and gender recognition. It reviews methodologies, datasets, and case studies, emphasizing deep learning's crucial role in boosting efficiency and accuracy. Recent advancements highlight CNN-based architectures and novel models, demonstrating deep learning for voice systems for enhanced interaction and analysis. Challenges such as computational demands and limited data availability persist, but ongoing optimizations and multi-modal approaches promise future advancements in voice technology, enabling more intelligent and responsive interactions.

Distinctive Features and Digital Filtration System (DFS)

This study aimed to evaluate the potential of DFS to help identify distinctive sound features easily and quickly. Through 20 participants in a between-group format, ten of whom were placed into each group, the researcher wanted to answer the main question of the study: How effective is using DFS to identify distinctive sound features? The participants in the study were tasked with identifying sounds through their distinctive features using either the paper-assisted matrix or the Excel filter matrix. The results show how the Excel filtering cohort outperformed the paper-based matrix one in accuracy and speediness. Group one using the Excel filtration performed perfectly with 100% accuracy in comparison to group two, which used a matrix on a piece of paper and gave accuracy that fluctuated between 40% and 80%. The group employing Excel filtration had better response time, with speed scores ranging between 0.4 min and 0.15 min compared with the paper-based matrix that demonstrated speed scores ranging from 1 to 3 min. The results showed that significant differences existed among the medians of accuracy (p < 0.05) and speed (p < 0.05) between the two studied groups. It thus proved to be a better approach because of its increased precision and faster reactive speed compared to the paper-based matrix, which was used for this test.

Eco-conscious upcycling of sugarcane bagasse into flexible polyurethane foam for mechanical & acoustic relevance.

This study explores the use of sugarcane bagasse (SCB), a byproduct of sugarcane processing, as a bio-filler in the production of flexible polyurethane foam (FPU), focusing on its benefits for both the environment and the economy. By varying the inclusion of SCB waste from 1 to 6 wt%, the research aims to enhance the FPU's mechanical and acoustic characteristics. Techniques such as Fourier transform infrared (FTIR) spectroscopy and field emission scanning electron microscopy (FESEM) were utilized to analyze the chemical structure and surface characteristics of both SCB and the FPU/SCB composites. Additionally, tests on gel fraction, density, and mechanical properties were conducted. The results indicate that adding 4 wt% SCB to FPU considerably improved the foam's properties. This modification resulted in a 148.63% increase in apparent density, a 228.47% rise in compressive strength, and a 116.24% boost in tensile strength. Furthermore, sound absorption across various frequency ranges was enhanced compared to the control foam. Additionally, the findings show that SCB effectively shifts sound absorption characteristics to lower frequencies. Specifically, at a low frequency of 500 Hz, the sound absorption coefficient increased to 0.4 with a foam thickness of 20 mm. This demonstrates that SCB can significantly improve FPU's performance, making it an attractive option for applications requiring noise mitigation, such as in the automotive and construction industries, thereby offering a sustainable solution to waste management and materials innovation.

Dentists who can auscultate: Microphone-based toothbrushing quality monitoring system for electronic toothbrush

Tooth brushing is a widely recognized and effective method for daily oral care. However, the lack of real-time feedback for monitoring the brushing process can hinder individuals in maintaining their oral hygiene. Currently, only high-end electric toothbrushes offer evaluation functions based on built-in sensors, making them unaffordable for the majority of people. In this paper, we propose a novel and cost-effective wearable acoustic-based system (both hardware and software) for monitoring the quality of daily tooth brushing. Our system combines a throat-microphone and a Bluetooth earphone to capture brushing noises, which are unique acoustical signals generated from the air and the human body during tooth brushing, respectively. By leveraging the distinctive characteristics of brushing sounds in these two different conductive media, we introduce an intelligent detection algorithm based on Single-Input Multiple-Output (SIMO) modeling and frequency response functions (FRFs) to detect the corresponding brushing area. Through experimentation, we have found that the divergence of FRFs provides more stable features compared to general statistical features used in previous studies. After comparing various machine learning algorithms, we have selected XGBoost as the final detection model. Additionally, we utilize the Symmetric Nearest Neighbor (SNN) filter in the post-processing stage to further reduce the jitter noise in continuous detection. The final system achieves an average precision of 98.46% when tested on tooth brushing data collected from different volunteers. To enhance user experience, we have developed an Android App with a user-friendly interface that incorporates the detection system. With a total wearable hardware cost of less than 10 dollars, our system shows great potential for integration into future smart wearable devices.

SyDRA: An approach to understand game engine architecture

Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.

Implementation of Data Mining for Speech Recognition Classification of Sundanese Dialect Using KNN Method with MFCC Feature Extraction

The importance of preservation and development of speech recognition technology for regional languages such as Sundanese, which have unique phonetic characteristics. Regional language speech recognition can assist in the development of local, educational, and cultural preservation applications to implement and evaluate the effectiveness of the combination of MFCC and KNN methods in classifying Sundanese dialect speech recognition. Methods used include trait extraction with MFCC, which converts voice data into numerical representations based on frequency characteristics, and classification with KNN, which groups data based on similarity to train data. The Dataset used consisted of speech recordings of Western and Southern Sundanese dialects. The results showed that the k-Nearest Neighbors (KNN) method can classify Sundanese dialect speech recognition with an accuracy of 80.00%, showing good ability in distinguishing "Western" and "southern" dialects. Mel-Frequency Cepstral Coefficients (MFCC) proved to be very effective in extracting sound features, helping KNN achieve low error rates. The combination of MFCC and KNN proved effective for speech recognition classification of Sundanese dialects, providing satisfactory results with high accuracy.

Deep learning-based snore sound analysis for the detection of night-time breathing disorders

Snoring, a prevalent symptom of obstructive sleep apnea, is believed to impact 57% of men and 40% of women in the United States. Night-time breathing disorders present significant challenges to both diagnosis and treatment, impacting millions of individuals worldwide. Traditional methods like CPAP machines and lifestyle changes face barriers such as discomfort, low adherence, and high costs, prompting the need for innovative solutions. This paper presents a novel approach using artificial intelligence, specifically deep learning, to create a snore sound analysis-based alerting system. This system aims to detect sleep disorders by analyzing snore patterns, providing a non-intrusive, cost-effective, and user-friendly alternative to traditional methods. By training models on snore sound characteristics, we've achieved promising results in identifying sleep apnea, showcasing the potential of this system in transforming the detection and management of night-time breathing disorders.

Theperception of emotion in music by people with hearing loss and people withcochlear implants.

Music is an important part of life for many people. It can evoke a wide range of emotions, including sadness, happiness, anger, tension, relief and excitement. People with hearing loss and people with cochlear implants have reduced abilities to discriminate some of the features of musical sounds that may be involved in evoking emotions. This paper reviews these changes in perceptual abilities and describes how they affect the perception of emotion in music. For people with acquired partial hearing loss, it appears that the perception of emotion in music is almost normal, whereas congenital partial hearing loss is associated with impaired perception of music emotion. For people with cochlear implants, the ability to discriminate changes in fundamental frequency (associated with perceived pitch) is much worse than normal and musical harmony is hardly perceived. As a result, people with cochlear implants appear to judge emotion in music primarily using tempo and rhythm cues, and this limits the range of emotions that can be judged. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Enhancing the absorption performance of minimal surface-based bulk absorbers via symmetry-breaking

The examination of Triply Periodic Minimal Surfaces (TPMSs) in the context of their acoustic dampening qualities has established them as viable alternatives to conventional porous absorbers. By adjusting the structure's porosity, one can significantly alter the TPMSs' acoustic absorption characteristics. Traditionally, enhancing sound absorption has involved decreasing porosity, which unfortunately increases the material's weight. In this study, we introduce a different approach to alter their absorption performance without altering their overall relative density. Our approach relies on leveraging the mathematical formulation of such structures to break their underlying geometrical symmetry. This symmetry-breaking results in altering their sound absorption characteristics while introducing an anisotropic acoustic response. In this paper, we present the mathematical design and fabricate the samples using the Fused Filament Fabrication (FFF) additive manufacturing method. The sound absorption characteristics of the printed samples are tested using a two-microphone normal incidence impedance tube setup. The presented results demonstrate that symmetry-breaking provides a viable route towards improving the low-frequency response of TPMS sound absorbers.

The two-cavity method for characterizing acoustical materials-an interlaboratory study

The study focuses on understanding the sound absorption and transmission characteristics of porous acoustical materials, which are determined by two key parameters independent of material thickness: characteristic impedance and propagation constant. These parameters can be characterized by testing a porous sample concept in a normal incidence impedance tube using either the two-thickness or the two-cavity method. In the two-thickness method, two samples of varying thicknesses are required. In contrast, the two-cavity method requires testing one sample at two air cavity depths. This method is particularly advantageous for materials that are costly or challenging to fabricate. This interlaboratory study evaluates the variability of characteristic properties determined using the two-cavity method. Porous acoustical materials were additively manufactured and tested in the Liner Technology Facility at NASA Langley Research Center and the Mechanics, Acoustics and Dynamics Lab (MADLab) at Michigan Technological University. The characteristic properties, derived from various cavity depth combinations, were used to predict the sound absorption curves of the sample with a rigid backing. These predictions were then compared with actual experimental absorption curves. The findings indicate that both the combinations of cavity depths and the sample's static airflow resistivity significantly influence the accuracy of the deduced properties.

Underwater sound from rotary oscillator and pile driving construction activities - I-205 Bridge over the Willamette River - I-205 Abernethy Bridge, Oregon

The Oregon Department of Transportation (ODOT) is conducting construction work in the Willamette River that involved the installation of piles through the water. There is a potential for impacts to wildlife, primarily fish, due to high noise levels. The project added piers to the existing bridge that consist of 12-foot diameter pile casings and drilled shafts. These casings are installed through rotary-oscillation drilled shafts that extended well below the water. These piles extend 200 feet below the river bottom. A large rotary oscillator was required to install the casings, which was supported on temporary platforms constructed in water. The platforms were supported by hollow 36-inch diameter steel piles. Underwater sounds from rotary oscillators have not been measured extensively in the past. This paper describes the underwater sound levels and characteristics of drilling large diameter casings deep through the substrate using the rotary oscillator. Measurements were also conducted for impact pile driving sounds from installation of the 36-inch diameter piles. The sound measurements evaluated the effectiveness of air bubble curtains meant to reduce sound from impact pile driving and meet permit requirements to minimize exposure of fish to harmful sound levels.

Metalite, a new class of composite laminates with unique properties

Metalite is a new class of antisymmetric composite laminates composed of angle-plies, 0-degree, and 90-degree plies, presenting unique properties. These include extremely thin laminates suitable for minimum gauge applications, remarkable weight savings compared to conventional quads, adjustable zero and negative coefficients of thermal expansion (CTE), ease of manufacturing, excellent ability to adjust mode frequency, change sound radiation characteristics, and high tunability. In this study, Metalite laminates ranging from 3 to 8 plies are described using their feasible spaces and compared with quads, detailing the weight savings achieved for hard, soft, and neutral laminates. Through an experimental study, the CTE value of a hybrid Metalite is correlated with theory, demonstrating how to tune zero and negative CTE values. The proposed work offers significant benefits through practical solutions for designing and manufacturing lightweight composite laminates with unique properties.

The contribution of short-term memory for sound features to speech-in-noise perception and cognition

Speech-in-noise (SIN) perception is a fundamental ability that declines with aging, as does general cognition. We assess whether auditory cognitive ability, in particular short-term memory for sound features, contributes to both. We examined how auditory memory for fundamental sound features, the carrier frequency and amplitude modulation rate of modulated white noise, contributes to SIN perception. We assessed SIN in 153 healthy participants with varying degrees of hearing loss using measures that require single-digit perception (the Digits-in-Noise, DIN) and sentence perception (Speech-in-Babble, SIB). Independent variables were auditory memory and a range of other factors including the Pure Tone Audiogram (PTA), a measure of dichotic pitch-in-noise perception (Huggins pitch), and demographic variables including age and sex. Multiple linear regression models were compared using Bayesian Model Comparison. The best predictor model for DIN included PTA and Huggins pitch (r2 = 0.32, p < 0.001), whereas the model for SIB included the addition of auditory memory for sound features (r2 = 0.24, p < 0.001). Further analysis demonstrated that auditory memory also explained a significant portion of the variance (28 %) in scores for a screening cognitive test for dementia. Auditory memory for non-speech sounds may therefore provide an important predictor of both SIN and cognitive ability.

Voice actors show enhanced neural tracking of pitch, prosody perception, and music perception

Experiences with sound that make strong demands on the precision of perception, such as musical training and experience speaking a tone language, can enhance auditory neural encoding. Are high demands on the precision of perception necessary for training to drive auditory neural plasticity? Voice actors are an ideal subject population for answering this question. Voice acting requires exaggerating prosodic cues to convey emotion, character, and linguistic structure, drawing upon attention to sound, memory for sound features, and accurate sound production, but not fine perceptual precision. Here we assessed neural encoding of pitch using the frequency-following response (FFR), as well as prosody, music, and sound perception, in voice actors and a matched group of non-actors. We find that the consistency of neural sound encoding, prosody perception, and musical phrase perception are all enhanced in voice actors, suggesting that a range of neural and behavioural auditory processing enhancements can result from training which lacks fine perceptual precision. However, fine discrimination was not enhanced in voice actors but was linked to degree of musical experience, suggesting that low-level auditory processing can only be enhanced by demanding perceptual training. These findings suggest that training which taxes attention, memory, and production but is not perceptually taxing may be a way to boost neural encoding of sound and auditory pattern detection in individuals with poor auditory skills.