Sound Characteristics Research Articles

BROWN NOISE, PENDEKATAN INSTRUMENTASI DAN POST PRODUKSI “MUSIK TERAPI UNTUK ADHD DEWASA”: SEBUAH TAWARAN

The issues of mental health have become increasingly prevalent in recent times. People are exploring therapeutic methods from various avenues, including musik. Unfortunately, the availability of musik therapy is limited. This research aims to provide insight into how brown noise is closely related to musik that is considered to have a positive impact on adult ADHD survivor. The brown noise indicator (slope) can be one of measures in instrument selection.
 This research aims to explain the spectral sound characteristics trend in some musik therapies designed for adult ADHD survivor. This study uses noise-based approach in analyzing and finding the conclusion.
 The research process begins with sample selection, the determination of the main sample, analyze and measure the sample using deep listening and visual monitoring methods. The tools used are DAW Studio one 6.5, Spectrum meter plug in, and Ayaic CoS 6 Pro.
 The result of this research consisting of patterns in ADHD musik therapy measured through a noise approach, including white noise, pink noise and brown noise. The findings of this study are expected
 to assist musik therapy creator in instrument selection.

The underwater soundscape of the North Sea

As awareness on the impact of anthropogenic underwater noise on marine life grows, underwater noise measurement programs are needed to determine the current status of marine areas and monitor long-term trends. The Joint Monitoring Programme for Ambient Noise in the North Sea (JOMOPANS) collaborative project was funded by the EU Interreg to collect a unique dataset of underwater noise levels at 19 sites across the North Sea, spanning many different countries and covering the period from 2019 to 2020. The ambient noise from this dataset has been characterised and compared - setting a benchmark for future measurements in the North Sea area. By identifying clusters with similar sound characteristics in three broadband frequency bands (25–160 Hz, 0.2–1.6 kHz, and 2–10 kHz), geographical areas that are similarly affected by sound have been identified. The measured underwater sound levels show a persistent and spatially uniform correlation with wind speed at high frequencies (above 1 kHz) and a correlation with the distance from ships at mid and high frequencies (between 40 Hz and 4 kHz). Correlation with ocean current velocity at low frequencies (up to 200 Hz), which are susceptible to nonacoustic contamination by flow noise, was also evaluated. These correlations were evaluated and simplified linear scaling laws for wind and current speeds were derived. The presented dataset provides a baseline for underwater noise measurements in the North Sea and shows that spatial variability of the dominant sound sources must be considered to predict the impact of noise reduction measures.

Assessing the Soundscape Characteristics of Historical Urban Environments: An Analysis of the Historical Erbil Citadel and Its Environments

This research addresses the neglect of sensory features, specifically the soundscape, in studies focused on preserving historic areas. The aim was to contribute to soundscape research by examining the effect of sound on the perception of urban historical places and the impact of the “renewal and transformation” process on audio-visual experiences. This study focused on the historical Erbil Citadel as a case study area, known for its extensive cultural history. Sound sources in the region were identified, recorded, and analyzed using software to calculate the Sound Pressure Level (SPL). The sound sources assessed as a result of the analysis were examined using Schafer’s sound characteristics. In this study, data was collected through a soundwalk study in the historical Erbil Citadel, and its surroundings were identified. The characteristics of the sounds were determined from the sound recordings, enabling the distinction of sounds contributing to the city’s identity. A route was established in its current state, and it was identified which regional precautions should be taken to preserve the sound identity of the city.

Multiclassification for heart sound signals under multiple networks and multi-view feature

Heart sound signals play a crucial role in assessing cardiovascular health. Building upon this, we have designed five networks to effectively classify these signals. Initially, it is essential to standardize them. Subsequently, we generate four types of two-dimensional (2D) heart sound feature images, namely Spectrogram, Mel-spectrogram, Bispectral analysis, and Phonocardiogram (PCG). To enhance performance, we refine the block in ConvNext and compare two one-dimensional (1D) ConvNext networks. After obtaining a superior network, we merge it with a pre-trained 2D ConvNext network on ImageNet. Then, we introduce the Encoder block from the Transformer network and combine it with the improved 1D ConvNext network. Among them, the Conv-Encoder1 network achieved classification accuracies of 98.7%, 91.9%, and 97.9% on the three datasets, respectively. Furthermore, by observing all the test results, it can be concluded that utilizing 1D heart sound signals for automatic classification offers significant advantages compared to using 2D feature images.

Multi-Order Asymmetric Acoustic Metamaterials with Broad Bandgaps at Subwavelength Scales.

Noise manipulation at the subwavelength scale remains a challenging problem. To obtain better broadband sound isolation within the subwavelength range, a class of asymmetric acoustic metamaterials (AAMs) based on rotation is proposed, and this class of AAMs can further improve subwavelength sound isolation performance by introducing multi-orders. The influences of changing the alternate propagation length of the coiled channel and the square cavity in the unit cell on the band frequency distribution and the omnidirectional band structure were investigated. The effective parameters are calculated with the S-parameter retrieval method, and the generation and change mechanisms of the bandgaps were elucidated. The calculation of sound transmission characteristics showed that, in the asymmetric mode, the overall sound isolation performance of the structure was greatly improved, and the relative bandwidth expanded as the alternate propagation length of the coiled channel and square cavity increased. The omnidirectional bandgaps from the first-order to the third-order AAMs occupied 63.6%, 75.96%, and 76.84% of the subwavelength range, respectively. In particular, the first bandgap moves to the low frequency and becomes wider. Both the experimental results and numerical analyses consistently showed that disrupting structural symmetry enhances acoustic metamaterials for superior broadband sound isolation, inspiring broader applications for asymmetry in this field.

Sound absorption and transmission loss of 3D printed wood fibre reinforced poly lactic acid with functionally graded perforations

ABSTRACT The effect of wood fibre reinforcement on acoustic properties of 3D printed poly lactic acid samples having varying cross-section perforation and functionally graded spherical perforations is presented. Acoustic characteristics like sound absorption as well as transmission loss are obtained using an impedance tube setup. Results revealed the benefits due to the addition of wood fibres in PLA improved samples’ sound transmission loss characteristics significantly. The sound absorption curve peaks shifted to low-frequency regions which are favourable to living beings. The material can be used in acoustic insulation for structural and transportation applications, especially where eco-friendliness and aesthetics are of major concern.

A multi-stream spatio-temporal network based behavioural multiparametric pain assessment system

Existing approaches for pain assessment are mostly based on facial expressions which cannot fully describe the expressiveness of pain. Although few researchers have also implemented multimodal approaches, they have not considered body movement for pain assessment. Besides, assessing pain in an unconstrained setting is challenging due to occlusions and poor illumination. Hence, to overcome these challenges we present a multi-stream framework for behavioural multiparametric pain assessment. To detect pain from face images a domain adaptation technique with a stacked BiLSTM network is implemented for joint spatio-temporal modelling. Further, a FCN-based-BiLSTM model is implemented to learn pain-related body dynamics. To learn pain-related features from pain sound VGG-based features are extracted and then a linear model is used for pain classification. Finally, a decision-level fusion approach is implemented to learn jointly from all pain classifiers. The proposed pain assessment system has achieved 92 % of accuracy after evaluating on self-created pain dataset.

A Developmental Switch in Cholinergic Mechanisms of Modulation in the Medial Nucleus of the Trapezoid Body.

The medial nucleus of the trapezoid body (MNTB) has been intensively investigated as a primary source of inhibition in brainstem auditory circuitry. MNTB-derived inhibition plays a critical role in the computation of sound location, as temporal features of sounds are precisely conveyed through the calyx of Held/MNTB synapse. In adult gerbils, cholinergic signaling influences sound-evoked responses of MNTB neurons via nicotinic acetylcholine receptors (nAChRs; Zhang et al., 2021) establishing a modulatory role for cholinergic input to this nucleus. However, the cellular mechanisms through which acetylcholine (ACh) mediates this modulation in the MNTB remain obscure. To investigate these mechanisms, we used whole-cell current and voltage-clamp recordings to examine cholinergic physiology in MNTB neurons from Mongolian gerbils (Meriones unguiculatus) of both sexes. Membrane excitability was assessed in brain slices, in pre-hearing (postnatal days 9-13) and post-hearing onset (P18-20) MNTB neurons during bath application of agonists and antagonists of nicotinic (nAChRs) and muscarinic receptors (mAChRs). Muscarinic activation induced a potent increase in excitability most prominently prior to hearing onset with nAChR modulation emerging at later time points. Pharmacological manipulations further demonstrated that the voltage-gated K+ channel KCNQ (Kv7) is the downstream effector of mAChR activation that impacts excitability early in development. Cholinergic modulation of Kv7 reduces outward K+ conductance and depolarizes resting membrane potential. Immunolabeling revealed expression of Kv7 channels as well as mAChRs containing M1 and M3 subunits. Together, our results suggest that mAChR modulation is prominent but transient in the developing MNTB and that cholinergic modulation functions to shape auditory circuit development.

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inception CNN model that is optimized based on GoogleNet for CMT weld penetration state recognition. The experimental results show that the accuracy of the method proposed in this paper for identifying the fusion state of CMT welds in aluminum alloy plates is 97.7%, which is higher than the identification accuracy of a single spectrogram as the input. The recognition accuracy of the customized Inception CNN is improved by 0.93% over the recognition accuracy of GoogleNet. The customized Inception CNN also has high recognition results compared to AlexNet and ResNet.

Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and logarithmic). The sound frame is converted to Mel frequency and processed with several triangular filters to get the cepstrum coefficient. Meanwhile, at the speech pattern recognition stage, the speaker uses an artificial neural network (ANN) Madaline model (many Adaline/ which is the plural form of Adaline) to compare the test sound characteristics. The training voice's features have been inputted as training data. The Madaline Neural Network training is BFGS Quasi-Newton Backpropagation with a goal parameter of 0,0001. The results obtained from the study prove that the Madaline model of artificial neural networks is not recommended for identification research. The results showed that the database's speech recognition rate reached 61% for ten tests. The test outside the database was rejected by only 14%, and 84% refused testing outside the database with different words from the training data. The results of this model can be used as a reference for creating an Android-based real-time system.

Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and logarithmic). The sound frame is converted to Mel frequency and processed with several triangular filters to get the cepstrum coefficient. Meanwhile, at the speech pattern recognition stage, the speaker uses an artificial neural network (ANN) Madaline model (many Adaline/ which is the plural form of Adaline) to compare the test sound characteristics. The training voice's features have been inputted as training data. The Madaline Neural Network training is BFGS Quasi-Newton Backpropagation with a goal parameter of 0,0001. The results obtained from the study prove that the Madaline model of artificial neural networks is not recommended for identification research. The results showed that the database's speech recognition rate reached 61% for ten tests. The test outside the database was rejected by only 14%, and 84% refused testing outside the database with different words from the training data. The results of this model can be used as a reference for creating an Android-based real-time system.

The influence of sound waves and musical experiences on movement coordination with beats

Synchronizing movement with external stimuli is important in musicians and athletes. This study investigated the effects of sound characteristics, including sound with harmonics (square wave) and without harmonics (sine wave) and levels of expertise in sports and music on rhythmic ability. Thirty-two university students participated in the study. The participants were divided into sixteen music education (ME) and sixteen physical education (PE) majors. They were asked to perform finger tapping tasks with 1,2 and 3 Hz beat rates, tapping in time with the sine and square wave beat produced by a metronome. The relative phase angle of finger tapping and the onset time of metronome sound were calculated using circular statistics. The results showed that type of wave and music experience affected the rhythmic ability of participants. Our study highlights the importance of types of waves on rhythmic ability, especially for participants with no background in music. The square wave is recommended for athletes to learn to synchronize their movement with beats.

An Overview on Sound Features in Time and Frequency Domain

Abstract Sound is the result of mechanical vibrations that set air molecules in motion, causing variations in air pressure that propagate as pressure waves. Represented as waveforms, these visual snapshots of sound reveal some of its characteristics. While waveform analysis offers limited insights, audio features provide a quantitative and structured way to describe sound, enabling data-driven analysis and interpretation. Different audio features capture various aspects of sound, facilitating a comprehensive understanding of the audio data. By leveraging audio features, machine learning models can be trained to recognize patterns, classify sounds, or make predictions, enabling the development of intelligent audio systems. Time-domain features, e.g., amplitude envelope, capture events from raw audio waveforms. Frequency domain features, like band energy ratio and spectral centroid, focus on frequency components, providing distinct information. In this paper, we will describe three time-domain and three frequency-domain features that we consider crucial and widely used. We will illustrate the suitability of each feature for specific tasks and draw general conclusions regarding the significance of sound features in the context of machine learning.

A note on the audio sound power generated by a parametric array loudspeaker.

Understanding the sound power characteristics of parametric array loudspeakers (PALs) poses a unique challenge due to the nonlinear process involved. This work addresses this issue by employing the spherical convolution directivity model to obtain the far field intensity, which is then used to calculate the sound power radiated by a PAL. The results reveal that the audio sound power is approximately proportional to the square of the audio frequency and the aperture size, while inversely proportional to the ultrasound frequency. Additionally, the conversion efficiency from ultrasound to audio sound is found to be generally below 0.15% for typical configurations.

Optimization design and analysis of the broadband acoustic absorber of the microperforated panel with an interpolated straight tube

The microperforated panel (MPP) structure is widely used in the field of noise reduction. The traditional MPP structure has a fixed absorption peak, and the optimized MPP structure has an improved absorption band, but the frequency range of the absorption is not ideal. For the design of broadband absorption in MPP structures, this paper presents a MPP acoustic absorber structure (N-MH) with an interpolated straight tube (IST) designed for superior broadband acoustic performance. The N-MH structure combines the advantages of the MPP and the embedded necked Helmholtz resonator, as well as having a sub-wavelength thickness of only 60 mm, which is only 1/14th of the wavelength at the first resonant frequency. The N-MH structure is optimized based on the particle swarm optimization (PSO) algorithm. In comparison to traditional microperforated panel structures, the N-MH structure exhibits a significantly broader sound absorption frequency band. Additionally, we thoroughly investigate the factors influencing the mid-low-frequency sound absorption performance of the N-MH structure. Through theoretical prediction, simulation analysis, and experimental testing, it is verified that the N-MH structure has excellent broadband sound absorption characteristics in the frequency interval of 0–2000 Hz, and the percentage of sound absorption bands with absorption coefficients exceeding 0.9 can reach 78.2%.

The relationship between body size and stridulatory sound production in loricariid catfishesa).

Sound production capabilities and characteristics in Loricariidae, the largest catfish family, have not been well examined. Sounds produced by three loricariid catfish species, Otocinclus affinis, Pterygoplichthys gibbiceps, and Pterygoplichthys pardalis, were recorded. Each of these species produces pulses via pectoral-fin spine stridulation by rubbing the ridged condyle of the dorsal process of the pectoral-fin spine base against a matching groove-like socket in the pectoral girdle. Light and scanning electron microscopy were used to examine the dorsal process of the pectoral-fin spines of these species. Mean distances between dorsal process ridges of O. affinis, P. gibbiceps, and P. pardalis were 53, 161, and 329 μm, respectively. Stridulation sounds occurred during either abduction (type A) or adduction (type B). O. affinis produced sounds through adduction only and P. pardalis through abduction only, whereas P. gibbiceps often produced pulse trains alternating between abduction and adduction. In these species, dominant frequency was an inverse function of sound duration, fish total length, and inter-ridge distance on the dorsal process of the pectoral-fin spine and sound duration increased with fish total length. While stridulation sounds are used in many behavioral contexts in catfishes, the functional significance of sound production in Loricariidae is currently unknown.

Performance improvement and noise reduction analysis of multi-blade centrifugal fan imitating long-eared owl wing surface

This research was inspired by the long-eared owl's ability to fly silently. For the first time in this study, a wind turbine blade is designed to mimic the wing surface and the leading edge of the long-eared owl. The commonly used two-dimensional blade profile in previous studies is replaced by a more effective three-dimensional profile. This change leads to improved aerodynamic performance of the multi-blade centrifugal fan and reduced noise levels. The airfoil and leading edge profile parameters of the long-eared owl were extracted and utilized. These parameters were used to develop a fitting formula based on their correlation. This formula facilitated the design and optimization of a bionic blade (B-Blade). The results indicate a 4.1% enhancement in the maximum flow rate compared to the original blade fan, alongside a noise reduction of 1.3 dB(A) under identical static pressure conditions. An examination of the internal flow, noise, and sound source characteristics of both fan types was conducted, elucidating the aerodynamic noise mechanism. Fan noise propagation showed pronounced dipole sound source traits. The sound source area at the B-Blade fan's inlet and the volute tongue was more compact, leading to a decrease in mid-low frequency discrete noise. The sound source intensity was also diminished. The B-Blade fan also ameliorated the flow distribution at the impeller outlet, reducing the unstable interaction between the impeller and volute tongue, thereby effectively diminishing noise.

The Role of Deep Learning Voice Recognition in Assisting Students with Hearing Impairments in Education

With the development of modern technology, deep learning sound recognition technology has gradually become an important tool in supporting the education of students with hearing impairments. This paper aims to explore the role of deep learning sound recognition in assisting the education of students with hearing impairments by comprehensively analyzing relevant literature and research findings. Firstly, the background and challenges of supporting the education of students with hearing impairments are introduced, emphasizing the potential of deep learning sound recognition technology in addressing these challenges. Subsequently, the principles and applications of deep learning sound recognition technology are elaborated, including sound feature extraction, model training, and sound classification. Furthermore, the actual applications of deep learning sound recognition technology in supporting the education of students with hearing impairments are discussed, such as speech recognition, speech synthesis, and assistive hearing devices. Finally, the advantages and limitations of deep learning sound recognition technology in supporting the education of students with hearing impairments are summarized, and future research directions and trends are proposed.

A Review on Scream Detection and Classification

Abstract—In contemporary living environments, the examination of non-speech audio signals is vital for enhancing situational awareness, complementing visual information from video signals. Screams within non-speech audio signals are particularly important for individuals like security personnel, caregivers, and family members due to their association with potential danger. Contrary to traditional beliefs, this review concentrates on automated acoustic systems specialized in non-speech scream classification. The objective is to show that screams can be categorized into emotional classes such as happiness, sadness, fear, and danger. Drawing inspiration from advancements in scream audio detection and classification, the review provides a taxonomy detailing target applications, prominent sound features, classification techniques, and their evolution over recent decades. This comprehensive review aims to assist researchers in choosing the most suitable scream detection and classification techniques, along with relevant acoustic parameters, contributing to a deeper understanding of speaker vocalization conditions. Keywords— Scream detection, scream classification, classification techniques, non-speech audio signals.

Experimental study on the widening of the sound isolation characteristics of light weight sound isolation structure using inflated membrane with air

Light sound insulation structure by using membrane and air pressure (hereinafter refer to as MSI), which consists of meshes and pressurized membranes, exhibits varying sound insulation characteristics and can be effective for the isolation of low-frequency sound even at a lightweight. However, MSI is ineffective for broadband noise owing to the limited valid frequency range of its sound insulation characteristics. Additionally, the stiffness of the frame of MSI, which constrains the meshes and membrane, must be enhanced as the sound insulation performance in the stiffness-law region depends on the stiffness. To resolve these issues, we focused on the specification of the meshes and the constraint condition of MSI. In this study, first, we evaluated the sound reduction index of a MSI combined with meshes of different specification on the surface and rear of the pressurized membrane. The results revealed that the MSI exhibited sound insulation characteristics with less reduction in sound insulation performance compared to the MSI consisting of mesh with the same specification. Second, we evaluated the sound reduction index of MSI divided by glazing bars within the frame that reinforce the stiffness of MSI. The results revealed that the sound insulation performance in the stiffness-law region can be improved by narrowing the space between bars.