The accurate classification of infrasound events is significant in natural disaster warning, verification of nuclear test bans and geophysical research. Current deep learning-based classification methods mostly focus on denoised and filtered signals. To simplify the process, avoid information loss, and address the issues of incomplete feature extraction by single-scale convolution kernels and the potential loss of physical information by single models, this paper directly utilizes raw infrasound signals and proposes two fusion classification models based on multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Experiments were conducted on a typical infrasound signal dataset (comprising four signal types: mountain-associated waves, auroral infrasound waves, volcanic eruptions, and microbaroms). The performances of the two models were compared in terms of accuracy, convergence speed, and stability. The results indicate that both models achieve classification accuracies exceeding 99% with optimal parameter combinations. The dual-branch multi-scale CNN-LSTM model generally outperforms the multi-scale CNN-LSTM model in classification accuracy, while also demonstrating faster convergence speed and better stability. Addressing the class imbalance in the dataset, evaluations using precision, recall, and F1-score further validated the effectiveness of the proposed models. This study demonstrates that the proposed methods can effectively achieve end-to-end classification of raw infrasound signals and are competitive with existing techniques.

This study investigates the influence of microgravity on the fundamental frequency (F0) of astronauts’ speech. A speech corpus was compiled, including recordings in microgravity and on Earth, matched by speaker and content. The signal processing methodology included filtering with consideration of human auditory perception, segmentation of speech fragments, F0 estimation using digital signal processing techniques, and visualization through fundamental frequency dynamics plots. Results revealed a consistent increase in F0 for most astronauts under microgravity, with maximum values of 450 Hz for female speakers and 245 Hz for male speakers. Elevated F0 levels were observed for approximately 86% of the total duration of speech fragments recorded in microgravity, compared with 14% on Earth. These findings confirm that microgravity affects the speech apparatus and acoustic characteristics of voice. Practical implications include adapting voice-controlled systems and automatic speech recognition for space environments, monitoring crew condition, and studying speech physiology under extreme conditions.

Speaker change detection (SCD) in long, multi-party meetings is essential for diarization, Automatic speech recognition (ASR), and summarization, and is now often performed in the space of pre-trained speech embeddings. However, unsupervised approaches remain dominant when timely labeled audio is scarce, and their behavior under a unified modeling setup is still not well understood. In this paper, we systematically compare two representative unsupervised approaches on the multi-talker audio meeting corpus: (i) a clustering-based pipeline that segments and clusters embeddings/features and scores boundaries via cluster changes and jump magnitude, and (ii) a multi-scale jump-based detector that measures embedding discontinuities at several window lengths and fuses them via temporal clustering and voting. Using a shared front-end and protocol, we vary the underlying features (ECAPA, WavLM, wav2vec 2.0, MFCC, and log-Mel) and test the model’s robustness under additive noise. The results show that embedding choice is crucial and that the two methods offer complementary trade-offs: the pipeline yields low false alarm rates but higher misses, while the multi-scale detector achieves relatively high recall at the cost of many false alarms.

Elastic closed-cell porous material is widely applied as a class of light sound insulation product. However, it is difficult to accurately predict its soundproof property due to the occurrence of the closed cells. Therefore, a combined theoretical model of Biot’s theory and acoustic field equations has been developed to predict the sound transmission loss (STL) in the mass control region. Five NBR-PVC closed-cell composites with different parameters were selected to verify the prediction model. Their STL measurement values were compared with the data calculated separately by the theoretical model and the Mass Law, whether under normal incidence or under random incidence. The results show that the Mass Law overestimates the sound insulation values of closed-cell porous material. STL prediction values from the theoretical model have more acceptable agreements to the measurement data than those from the Mass Law. The average deviation rates of the theoretical model are less than 4% under the normal incidence condition and are about 2.9% under the random incidence condition.

Korea has a high population density, considering the size of its territory. Therefore, the importance of convenient and comfortable apartment buildings and high-rise residential–commercial complex buildings has been rising. In addition, because of the improvement in the standard of living along with continuous national economic growth, the interest in well-being and the expectation of a quiet life with a comfortable and pleasant residential environment have also been increasing. However, Koreans have a lifestyle involving sitting on the floor, so floor impact noise has been occurring more and more frequently. Because of this, neighborly disputes have been a serious social problem. And lately, damage and disputes from noise between floors have been increasing much more. The present work, therefore, used waste tire chips as a resilient material for reducing floor impact noise in order to recycle waste tires effectively. Also, a compounded resilient material, which combines EPS (expanded polystyrene), a flat resilient material on the upper part, with waste tire chips for the lower part, was developed. After constructing waste tire chips at a standardized test building, experiments with both light-weight and heavy-weight floor impact noise were performed. The tests confirmed that waste tire chips, when used as a resilient material, can effectively reduce both light-weight and heavy-weight floor impact noise.

Speech intelligibility (SI) is critical in effective communication across various settings, although it is often compromised by adverse acoustic conditions. In noisy environments, visual cues such as lip movements and facial expressions, when congruent with auditory information, can significantly enhance speech perception and reduce cognitive effort. In an ever-growing diffusion of virtual environments, communicating through virtual avatars is becoming increasingly prevalent, thus requiring a comprehensive understanding of these dynamics to ensure effective interactions. The present study used Unreal Engine’s MetaHuman technology to compare four methodologies used to create facial animation: MetaHuman Animator (MHA), MetaHuman LiveLink (MHLL), Audio-Driven MetaHuman (ADMH), and Synthetized Audio-Driven MetaHuman (SADMH). Thirty-six word pairs from the Diagnostic Rhyme Test (DRT) were used as input stimuli to create the animations and to compare them in terms of intelligibility. Moreover, to simulate a challenging background noise, the animations were mixed with a babble noise at a signal-to-noise ratio of −13 dB (A). Participants assessed a total of 144 facial animations. Results showed the ADMH condition to be the most intelligible among the methodologies used, probably due to enhanced clarity and consistency in the generated facial animations, while eliminating distractions like micro-expressions and natural variations in human articulation.

This paper presents a comprehensive evaluation using a Z-test to assess the effectiveness of an adaptive Least Mean Squares (LMS) filter driven by the Steepest Descent Method (SDM). The study utilizes a male voice recording, captured in a controlled studio environment, to which persistent Gaussian noise was intentionally introduced, simulating real-world interference. All signal processing methods were implemented accordingly in MATLAB.version: 9.13.0 (R2022b), Natick, MA, USA: The MathWorks Inc.; 2022. The adaptive filter demonstrated a significant improvement of 20 dB in Signal-to-Noise Ratio (SNR) following the initial optimization of the filter parameter μ. To further assess the LMS filter’s performance, an empirical experiment was conducted with 30 young adults, aged between 20 and 30 years, who were tasked with qualitatively distinguishing between the clean and noise-corrupted signals (blind test). The quantitative analysis and statistical evaluation of the participants’ responses revealed that a significant majority, specifically 80%, were able to reliably identify the noise-affected and filtered signals. This outcome highlights the LMS filter’s potential—despite the slow convergence of the SDM—for enhancing signal clarity in noise-contaminated environments, thus validating its practical application in speech processing and noise reduction.

Kunqu Opera, hailed as the “ancestor of all Chinese operas”, plays a crucial role in preserving cultural identity and collective memory in the Jiangnan region. This research examines the acoustical traditions of Kunqu. Through expert interviews and thematic analysis, the study explores key acoustic elements, including vocal techniques and musical structures, and their symbolic and emotional impact. Five central themes emerged, including Kunqu’s role in collective memory, its function as a cultural symbol, the evolution of its transmission, the emotional resonance of its acoustic elements, and the modern challenges and opportunities for preservation. The findings highlight the importance of maintaining Kunqu’s cultural identity within acoustical heritage, while also recognizing the need for innovation in its transmission. This research contributes to the broader discussion on intangible cultural heritage and provides insights into how traditional art forms like Kunqu can be preserved amidst modernization.

Near-seafloor acoustic field characteristics are essential prior knowledge for near-seafloor underwater acoustic engineering. Scholte waves are a crucial component of the near-seafloor acoustic fields. These fields, when considering Scholte waves, are susceptible to seafloor relief. However, open-source bathymetric datasets generally lack sufficient precision. Therefore, acoustic field simulations using open-source data can contain significant errors or even introduce erroneous propagation characteristics. The spectral element method (SEM) is an example of exploring the influence of an inadequate spatial-sampling rate and sea-depth precision on acoustic field simulations. In this article, appropriate methods for topographic processing are presented. The results indicate that the terrain can be corrected using cubic spline interpolation in cases of an inadequate spatial-sampling rate. Where there is insufficient sea-depth precision, this study proposes a terrain processing method. The first step involves sequentially determining the interpolation points for the rising and falling edge, depressions, bulges, and horizontal segments. Then, it adopts cubic spline interpolation. The SEM examples effectively verify this effect. Given the limited research on terrain correction in acoustic field simulations, this study introduces a low-complexity method that can effectively support exploring acoustic fields affected by seafloor terrain.

This paper aims to conduct an acoustic analysis through noise measurements of a hybrid propulsion system intended for implementation on a drone, from which the main noise sources can be identified for further research on noise reduction techniques. Additionally, the noise was characterized by performing spectral analysis and identifying the tonal components that contribute to the overall noise. The propelling force system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The propulsion system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The electric current produced by the generator powers an electric ducted fan (EDF). The engineturbo-engine was tested in free-field conditions for noise generation at different speeds, and for this, an array of microphones was installed, positioned polarly around the system and near the intake and exhaust. Consequently, based on the test results, the acoustic directivity was plotted, revealing that the highest noise levels are at the front and rear of the engine. The noise level at a distance of 1.5 m from the turboengine exceeds 90 dBA at all tested speeds. Spectral analyses of both the far-field acoustic signals (measured with a polar microphone array) and the near-field signals (microphones positioned near the intake and exhaust) revealed that the primary contributors to the overall noise are the micromotor’s compressor, specifically the gas dynamic phenomena in the fan (BPF and 2× BPF). Thus, it was determined that at the intake level, the main noise contribution comes from the high-frequency components of the compressor, while at the exhaust level, the noise mainly originates from the combustion chamber, characterized by low-frequency components (up to 2 kHz). The findings from this study have practical applications in the design and development of quieter drone propulsion systems. By identifying and targeting the primary noise sources, engineers can implement effective noise reduction strategies, leading to drones that are less disruptive in urban environments and other noise-sensitive areas. This can enhance the acceptance and deployment of drone technology in various sectors, including logistics, surveillance, and environmental monitoring.