Sound Signals Research Articles

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.

On the acceptance limits for short-term level fluctuation of sound calibrators

To ensure noise measurement accuracy, sound calibrators are used to check the sound level meter accuracy before and after any such measurements, as required by most noise regulations. The sound calibrator itself needs to be calibrated routinely and traceable to national standards. The International Standard IEC 60942, Electroacoustics - Sound calibrators, specifies performance requirements for the sound pressure level, short-term level fluctuation, frequency, and distortion and associated test methods. However, the acceptance limits for short-term level fluctuation specified in IEC 60942 is problematic. The short-term level fluctuation acceptance limits specified in Table 2 of IEC 60942 for the level variations inherited by a stable sinusoid at low frequencies are either underestimated or overestimated. In this paper, a detailed theoretical analysis is presented addressing this particular issue. Closed-form solutions are obtained for typical sinusoidal sound signals. Two experimental setups and measurement results for the validation of the theoretical analysis are also presented. One uses a commercial sound level meter and the other uses a precision sound pressure measurement system developed recently at the National Research Council Canada. A revised table for the acceptance limits is then proposed for the future revision of Table 2 of IEC 60942.

Experimental study on sound propagation caused by disturbance of wake flow on water surface boat

The research on underwater sound propagation generally assumes that the water is static. However, when a boat passes on the water surface, its wake can cause disturbance to the stable sound field. The main reason is that the wake changes the temperature and salinity distributions of seawater in the just field, thereby affecting propagation loss. This article conducts a time-frequency analysis of the sound signal propagated by the boat wake. Results show that the wake not only affects the propagation loss but also causes obvious fluctuations in the envelope of the received signal. This fluctuation indicates the frequency modulation of sound by the boat wake. These changes in sound propagation are not only related to the boat speed but also to the signal frequency.

A framework for the acoustic simulation of passing vehicles using variable length delay lines

The sound produced by vehicles driving on roadways constitutes one of the dominant noise sources in urban areas. The impact of traffic noise on human activities and the related investigation on modeling, assessment, and abatement strategies fueled the research on the simulation of the sound produced by individual passing vehicles. Simulators enable in fact to promote a perceptual assessment of the nature of traffic noise and of the impact of single road agents on the overall soundscape. In this work, we present TrafficSoundSim, an open-source framework for the acoustic simulation of vehicles transiting on a road. We first discuss the generation of the sound signal produced by a vehicle, represented as a combination of road/tire interaction noise and engine noise. We then introduce a propagation model based on the use of variable length delay lines, allowing to simulate acoustic propagation and Doppler effect. The proposed simulator incorporates the effect of air absorption and ground reflection, modeled via complex-valued reflection coefficients dependent on the road surface impedance, as well as a model of the directivity of sound sources representing the passing vehicles. The source signal generation and the propagation stages are decoupled, and all effects are implemented using finite impulse response filters. Moreover, no recorded data is required to run the simulation, making the framework flexible and independent on data availability. Finally, to validate the framework capability to accurately simulate passing vehicles, a comparison between synthetic and recorded pass-by events is presented. The validation shows that sounds generated with the proposed method achieve a good match with recorded events in terms of power spectral density and psychoacoustics metrics as well as a perceptually plausible result.

Extreme, Outrageous, and (Un)reliable: Navigating Uncertainty in the Development of Sound-Based Fog Signals in Scotland, 1860-1900.

This case study demonstrates how an analysis grounded in sound studies, rather than visual studies, reveals a different technological outcome in the development of coastal navigation systems. In the late nineteenth century, engineers at Scotland's Northern Lighthouse Board developed and managed a growing network of sound-based fog signals, primarily using steam-powered sirens. Despite the prevailing perception that sound signaling was more unreliable and risky than lighthouses for coastal navigation, engineers focused on maximizing the loudness of these devices to address the uncertainty of sound transmission. By prioritizing the siren and narrowing the system's goals to warning sailors about imminent danger, the engineers created a system with fundamentally different aims, implementation methods, and design priorities than those of visually-based lighthouse technology. This sound-based analysis uncovers how sensory hierarchies shaped technological decisions, leading to a unique and distinct approach to coastal navigation.

„Na o–bi–ad!”: słuchanie warszawskiej trąbizupki, czyli kuchni ruchomej (1901–1904), jako sygnału oraz praktyki akustemologicznej ludowej społeczności Warszawy

Trąbizupka – Warsaw’s mobile field kitchen – was a charitable initiative of the early years of the 20th century; a cart going around Warsaw sold nutritious and cheap soup, and its arrival was accompanied by the sound of a trumpet. This peculiar sound phenomenon existed in the city’s phonosphere for a short time – the residents had to learn to interpret it, but after a few years, trąbizupka’s activity was suspended. The functioning of trąbizupka (manifested through its sonicity – literally, in the sound of the trumpet, and figuratively, in the name itself) was coupled with a class discourse on charity – from admiration for the initiative to the feeling of shame of Warsaw’s folk classes, as reported “first-hand” by the positivist “Przegląd Tygodniowy” [Weekly Review], linked to eating a pauper’s meal “on the street.” At the same time, trąbizupka became an element of urban folklore, preserved in popular polkas for piano and as a sound characteristic of Warsaw. Trąbizupka, as a case study analyzed from the position of historical sound studies, allows us to ask questions about the sound signal as a vehicle of communication and about the acoustemology of the city’s residents, especially of the lower social classes, at the turn of the 20th century. Key-words: acoustemology, audiosphere, sound history, sound studies, Warsaw

Evaluating heart rate variability by a novel stethoscopic approach to minimise stress in cats.

Stress associated with manipulation during electrocardiography (ECG) recording in cats potentially limits the assessment of autonomic function through heart rate variability (HRV) in the feline population. This study proposed an alternative, cat friendly, stethoscopic approach to evaluate HRV with an easily acquired vasovagal tonus index (VVTI). The aim of this prospective study was to evaluate whether VVTI derived from heart sound signals could distinguish between relaxed and stimulated states. A total of 29 cats with 56 recordings of heart sound and ECG on 31 occasions were included. In 25 cats in their home environment, a stethoscope connected to a digital recording device was used to record 2 mins of heart sounds twice - with the cats in a relaxed state and immediately after stimulation. The VVTI was calculated from 20, 60 and 120 consecutive beat-to-beat intervals on the heart sound spectrogram (stethoscopic-VVTI 20, 60 and 120), using the natural logarithm of the variance of the intervals based on previous literature. A 2-min ECG recording was obtained at home with the intention of avoiding strict restraint. To demonstrate the feasibility of the stethoscopic approach in a hospital setting, six cats (two of which were also recorded at home) underwent heart sound and ECG recordings during planned veterinary visits. Stethoscopic-VVTI 20 (5.43 to 4.79, P = 0.001), 60 (6.20 to 5.18, P <0.001) and 120 (6.24 to 5.60, P = 0.02) all significantly decreased after stimulation, indicating a reduced vasovagal tone as expected. Calculations of stethoscopic-VVTI from different sections of the recording yielded statistically similar results. Stethoscopic-VVTI showed a negative correlation with the corresponding heart rate. Bland-Altman analysis revealed a mean bias for the differences between stethoscopic-VVTI and ECG-VVTI of 0.50 and 1.07 at home and in the hospital, respectively. VVTI can be successfully detected through a stethoscopic approach, serving as a less stressful tool for HRV evaluation in cats during routine auscultation.

Surface quality prediction in-situ monitoring system: A deep transfer learning-based regression approach with audible signal

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

Paramps: Convolutional neural networks based on tensor decomposition for heart sound signal analysis and cardiovascular disease diagnosis

Currently, convolutional neural networks have demonstrated outstanding efficiency in heart sound detection and automatic diagnosis of cardiovascular diseases. However, due to the non-stationary nature and complex data patterns caused by environmental noise and stethoscope differences, traditional neural networks are limited in extracting discriminative features. This article proposes a convolutional neural network based on tensor decomposition to address this issue. This model uses a convolutional neural network with four parallel structures to extract audio features of heart sound signals and introduces a tensor network to use tensor decomposition to perform low-rank approximation on the convolutional kernel, compress model parameters, reduce redundancy, and improve performance. When processing feature data, the model divides large areas of features into locally unordered small areas to achieve feature compression and reorganization, ensuring that crucial information is preserved while compressing parameters. The model can accurately capture spatial structural information and critical features by refining the matrix product state layer. Experiments were conducted on the 2016 PhysioNet/CinC Challenge and the Yaseen heart sound public dataset, the experimental results show that the proposed method has an accuracy of 96.4% and 99.2% on two datasets, specificity of 99.1% and 99.8%, demonstrating its excellent generalization ability and diagnostic accuracy.

Toward Wearables for Bruxism Detection: Voluntary Oral Behaviors Sound Recorded Across the Head Depend on Transducer Placement.

Bruxism is a parafunctional orofacial behavior. For diagnosis, wearable devices that use sounds as biomarkers can be applied to provide the necessary information. Human beings emit various verbal and nonverbal sounds, making it challenging to identify bruxism-induced sounds. We wanted to investigate whether the acoustic emissions of different oral behaviors have distinctive characteristics and if the placement of the transducer has an impact on recording the sound signals. Sounds from five oral behaviors were investigated: jaw clenching, teeth grinding, reading, eating, and drinking. Eight transducers were used; six were attached to the temporal, frontal, and zygomatic bones with the aid of medical tape, and two were integrated into two commercial earphones. The data from 15 participants were analyzed using time-domain energy, spectral flux, and zero crossing rate (ZCR). In summary, all oral behaviors showed distinct characteristic features except jaw clenching, though there was a peak in the recording, possibly due to tooth tapping, before its expected onset. For teeth grinding, the transducer placement did not have a significant impact (p > 0.05) based on energy, spectral flux, and ZCR. For jaw clenching, the transducer placement had an impact with regard to spectral flux (p < 0.01). For reading and eating, the transducer placement had a significant impact with regard to energy (p < 0.05 for reading, p < 0.01 for eating), spectral flux (p < 0.001 for reading, p < 0.01 for eating), and ZCR (p < 0.001 for both reading and eating). For drinking, the transducer placement only had a significant impact with regard to ZCR (p < 0.01). We were able to record the sounds of various oral behaviors from different locations on the head. However, the ears were an advantageous location to place the transducer, since they could compensate for various head movements and ear devices are socially tolerable.

Automated analysis of heart sound signals in screening for structural heart disease in children

Our aim was to investigate the ability of an artificial intelligence (AI)-based algorithm to differentiate innocent murmurs from pathologic ones. An AI-based algorithm was developed using heart sound recordings collected from 1413 patients at the five university hospitals in Finland. The corresponding heart condition was verified using echocardiography. In the second phase of the study, patients referred to Helsinki New Children’s Hospital due to a heart murmur were prospectively assessed with the algorithm, and then the results were compared with echocardiography findings. Ninety-eight children were included in this prospective study. The algorithm classified 72 (73%) of the heart sounds as normal and 26 (27%) as abnormal. Echocardiography was normal in 63 (64%) children and abnormal in 35 (36%). The algorithm recognized abnormal heart sounds in 24 of 35 children with abnormal echocardiography and normal heart sounds with normal echocardiography in 61 of 63 children. When the murmur was audible, the sensitivity and specificity of the algorithm were 83% (24/29) (confidence interval (CI) 64–94%) and 97% (59/61) (CI 89–100%), respectively.Conclusion: The algorithm was able to distinguish murmurs associated with structural cardiac anomalies from innocent murmurs with good sensitivity and specificity. The algorithm was unable to identify heart defects that did not cause a murmur. Further research is needed on the use of the algorithm in screening for heart murmurs in primary health care.What is Known:• Innocent murmurs are common in children, while the incidence of moderate or severe congenital heart defects is low. Auscultation plays a significant role in assessing the need for further examinations of the murmur. The ability to differentiate innocent murmurs from those related to congenital heart defects requires clinical experience on the part of general practitioners. No AI-based auscultation algorithms have been systematically implemented in primary health care.What is New:• We developed an AI-based algorithm using a large dataset of sound samples validated by echocardiography. The algorithm performed well in recognizing pathological and innocent murmurs in children from different age groups.

Intelligent Fault Diagnosis Method for Constant Pressure Variable Pump Based on Mel-MobileViT Lightweight Network

The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis.

A Nanoparticle-Based Artificial Ear for Personalized Classification of Emotions in the Human Voice Using Deep Learning.

Artificial intelligence and human-computer interaction advances demand bioinspired sensing modalities capable of comprehending human affective states and speech. However, endowing skin-like interfaces with such intricate perception abilities remains challenging. Here, we have developed a flexible piezoresistive artificial ear (AE) sensor based on gold nanoparticles, which can convert sound signals into electrical signals through changes in resistance. By testing the sensor's performance at both frequency and sound pressure level (SPL), the AE has a frequency response range of 20 Hz to 12 kHz and can sense sound signals from up to 5 m away at a frequency of 1 kHz and an SPL of 126 dB. Furthermore, through deep learning, the device achieves up to 96.9% and 95.0% accuracy in classification and recognition applications for seven emotional and eight urban environmental noises, respectively. Hence, on one hand, our device can monitor the patient's emotional state by their speech, such as sudden yelling and screaming, which can help healthcare workers understand patients' condition in time. On the other hand, the device could also be used for real-time monitoring of noise levels in aircraft, ships, factories, and other high-decibel equipment and environments.