Acoustic Dimensions Research Articles

Influence of personal and career burnout on female employers' subjective evaluation of workplace acoustic environment.

Burnout is an increasingly common problem in modern work settings that significantly affects people's health and well-being. Several studies have emphasized the impact of career burnout on employees' performance and efficiency. It is unknown whether career burnout mediated by personal burnout may affect employees' perception of their workplace physical environment attributes. This research aims to understand if personal and career burnout can affect employers' acoustic environment evaluation of their workplace. Considering commonly experienced or highly experienced personal and career burnout among working women, the study targeted female university faculty members. The research involved stratified sampling and employed data from 272 individuals across five public and private universities in Tehran. Collected data were analyzed using SmartPLS (latest release 4.1). The results revealed a significant link between personal and career burnout and the subjective evaluation of workplace acoustic environment. Career burnout mediated the relationship between personal burnout and negative evaluation of the workplace acoustic environment. This study provides compelling evidence that experiences of burnout, whether related to personal or career aspects, substantially impact the subjective assessment of the acoustic environment within the workplace. The results underscore the complex interplay between an individual's degree of burnout and their subjective perception of the acoustic dimensions of their work environment. The findings extend our understanding of how psychological factors might shape our interpretation of the physical workplace.

Multimodal exploration of the thank God expressive construction and its implications for translation

Abstract Multimodal research in communication and translation studies is increasingly recognized, yet it remains incompletely explored. Leveraging computational linguistics with both Praat for acoustic analysis and the OpenPose and Rapid Annotator tools for visual analysis, this study delves into the intricate dynamics of the expressive construction thank God, providing a comprehensive examination of both visual and acoustic dimensions. Our objective is to uncover nuanced patterns of multimodal communication embedded within this expression and their implications for Translation and Interpreting. Through an analysis of linguistic features and co-speech gestures present in thank God, we aim to deepen our comprehension of how meaning crisscrosses modalities. Our findings underscore the necessity of a multimodal approach in language studies, emphasizing the requisite to preserve emotional and contextual nuances. The analysis unveils the phonological relevance of the duration of the construction’s second vowel, a key factor for translation. Additionally, data reveals a correlation between the emotion of relief and gestures executed with both hands closer to the chest. Overall, these findings contribute to advancing both multimodal communication research and translation studies, shedding light on the role of multimodal analysis in understanding language and translation dynamics, particularly in the context of constructions like thank God.

Which acoustic dimensions could act as mediators between urban greenspace and human health?

The beneficial effects of urban greenspace on human health and well-being are widely recognized, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is often proposed as a detrimental factor and emerging evidence suggests that it may also represent a beneficial resource. If different dimensions of the AE were to represent a mediator between greenspace and human health, they would need to be associated with both. We present results investigating the first prerequisite: the associations between acoustic dimensions and urban greenspace. We use audio recordings from SALVE, randomly sampled at 730 locations four times per year (n=2,746) in Bochum, Germany. The AE is quantified by 122 eco- and psychoacoustic indices, sound pressure levels and complex network measures. Greenspace is defined as the percentage of green area in a 300 m-buffer around each recording location, grouped into quintiles. We apply dimensionality reduction and present descriptive statistics to analyze the associations between identified acoustic dimensions and urban green area. In addition to the "sound volume", we find that biophonic sounds, the Acoustic Richness, the Impulsiveness, the Roughness, and the "acoustic dominance" show a consistently decreasing/increasing trend across the five quintiles of urban green area.

Auditory time travel: Acoustic notes on the virtual reality reconstruction of the Queen's theatre (Adelaide, 1841)

This paper presents the results of an international interdisciplinary effort to reconstruct the Queen's Theatre in Adelaide as it stood on its inaugural debut in 1841. Our collaborative approach brings together real actors, set and costume designers, architects, virtual reality (VR) developers, motion capture specialists, and acoustic engineers in order to reproduce, as realistically as possible, the opening night monologue by Mr Gustavus Arabin. By combining the modelled ambisonics room impulse responses with a proprietary algorithm, we achieved an authentic representation of the historical environment both visually and acoustically by allowing the user to immerse themselves in the space at several locations within the theatre. Central to our VR reconstruction is the ability of our software to combine auralisations from multiple sound sources simultaneously as well as allowing the user to upload user-generated anechoic files. The paper focuses on the acoustic nuances throughout the VR reconstruction, providing a comprehensive overview of collaborative endeavours and technological advancements enabling users to grasp and appreciate the acoustic intricacies of Queen's Theatre. Emphasising the acoustic dimension of the space underscores the potential of our virtual model to serve as a dynamic platform for historical exploration, research, and educational pursuits.

A unified model of lenition as modulation reduction: gauging consonant strength in Ibibio

Abstract We review and elaborate an account of consonantal strength that is founded on the model of speech as a modulated carrier signal. The stronger the consonant, the greater the modulation. Unlike approaches based on sonority or articulatory aperture, the account offers a uniform definition of the phonetic effect lenition has on consonants: All types of lenition (such as debuccalisation, spirantisation and vocalisation) reduce the extent to which a consonant modulates the carrier. To demonstrate the quantifiability of this account, we present an analysis of Ibibio, in which we investigate the effects of lenition on the amplitude, periodicity and temporal properties of consonants. We propose a method for integrating these different acoustic dimensions within an overall measure of modulation size. Not only does the modulated-carrier account cover all the classically recognised lenition types, but it also encompasses loss of plosive release in final stops – which, although not traditionally classed as lenition, is clearly related to processes that are.

Characterizing acoustic dimensions of health-related urban greenspace

The association of urban greenspace and human health and well-being is widely recognised, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is frequently proposed as a mediator between greenspace and human health. While it is commonly viewed as a negative health factor (e.g. noise pollution), there is growing evidence that it also has positive effects on human health. However, a general problem is the lack of information on the AE for greenspaces in high spatial resolution. To provide evidence-based support for research on this issue, we identify and assess acoustic properties of health-related urban greenspace by estimating the association between urban green area and selected acoustic indices.The analysis is based on 5-minute audio recordings collected systematically from 730 locations, four times a year (n = 2,746) in Bochum, Germany. To quantify the acoustic properties, we use an indicator-set of 30 sound pressure levels, 11 eco-, 78 psychoacoustic indices and 3 complex network measures. Greenspace is defined, in alignment with various health studies, as the percentage of green area within three buffer sizes (50, 100, 300 m) around each recording location.Through cluster analysis, we identify eleven acoustic dimensions in our indicator-set, for which representative indices are derived using principal component analysis. Descriptive statistics and multi-level regression models are used to analyse the associations between representative indices and greenspace. We find the highest effect sizes for Link Density (representing “acoustic dominance”), Acoustic Richness (representing “persistent sound volume”) and the Articulation Index (representing “sound volume”). Thus, in addition to “sound volume”, the AE of urban greenspace is characterised to a similar extent by at least two additional acoustic dimensions. If properties of the AE act as mediators between urban greenspace and human health, these three acoustic dimensions hold the potential to reflect them. Studies on pathways between greenspace and human health could incorporate measures of these dimensions to extend the understanding of the AE’s relevance for Public Health.

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds.

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep-related breathing disorder that significantly impacts the daily lives of patients. Currently, the diagnosis of OSAHS relies on various physiological signal monitoring devices, requiring a comprehensive Polysomnography (PSG). However, this invasive diagnostic method faces challenges such as data fluctuation and high costs. To address these challenges, we propose a novel data-driven Audio-Semantic Multi-Modal model for OSAHS severity classification (i.e., ASMM-OSA) based on patient snoring sound characteristics. In light of the correlation between the acoustic attributes of a patient's snoring patterns and their episodes of breathing disorders, we utilize the patient's sleep audio recordings as an initial screening modality. We analyze the audio features of snoring sounds during the night for subjects suspected of having OSAHS. Audio features were augmented via PubMedBERT to enrich their diversity and detail and subsequently classified for OSAHS severity using XGBoost based on the number of sleep apnea events. Experimental results using the OSAHS dataset from a collaborative university hospital demonstrate that our ASMM-OSA audio-semantic multimodal model achieves a diagnostic level in automatically identifying sleep apnea events and classifying the four-class severity (normal, mild, moderate, and severe) of OSAHS. Our proposed model promises new perspectives for non-invasive OSAHS diagnosis, potentially reducing costs and enhancing patient quality of life.

A comprehensive review on: Mechanical and acoustical characterization of natural fiber-reinforced composite

In our modern era of rapid development, the issue of noise pollution has become increasingly pressing. This relentless surge in noise pollution has brought about significant adverse effects on human health, extending beyond just auditory concerns. To combat this escalating problem, it becomes imperative to explore innovative solutions, such as the utilization of natural fiber-reinforced composites in acoustical applications. These composites offer environmental friendliness, cost-effectiveness, and enhanced mechanical and sound-absorbing qualities. In the context of this review, we examine various methods for evaluating both the mechanical and acoustical attributes of these composites. We examine significant acoustical metrics, including the sound absorption coefficient, sound transmission losses, and noise reflection coefficient, as well as mechanical aspects encompassing tensile, flexural, impact strengths, and hardness. Moreover, this comprehensive review delves into the realm of influencing parameters, which exert a notable impact on the mechanical and acoustical characteristics of natural fiber-reinforced composites. A range of variables is encompassed by these influencing parameters, including fiber properties, binder-filler amount, specimen thickness, etc. It is worth noting that manipulating these parameters can yield significant enhancements in mechanical strength and sound absorption. However, it is crucial to recognize and respect certain limitations to avoid adverse effects. In conclusion, this review emphasizes the importance of carefully considering these performance-influencing parameters to optimize the mechanical and acoustical attributes of natural fiber-reinforced composites. By doing so, we can fully harness the potential of these composites to address the challenges posed by escalating noise pollution effectively.

Perceptual (but not acoustic) features predict singing voice preferences.

Why do we prefer some singers to others? We investigated how much singing voice preferences can be traced back to objective features of the stimuli. To do so, we asked participants to rate short excerpts of singing performances in terms of how much they liked them as well as in terms of 10 perceptual attributes (e.g.: pitch accuracy, tempo, breathiness). We modeled liking ratings based on these perceptual ratings, as well as based on acoustic features and low-level features derived from Music Information Retrieval (MIR). Mean liking ratings for each stimulus were highly correlated between Experiments 1 (online, US-based participants) and 2 (in the lab, German participants), suggesting a role for attributes of the stimuli in grounding average preferences. We show that acoustic and MIR features barely explain any variance in liking ratings; in contrast, perceptual features of the voices achieved around 43% of prediction. Inter-rater agreement in liking and perceptual ratings was low, indicating substantial (and unsurprising) individual differences in participants' preferences and perception of the stimuli. Our results indicate that singing voice preferences are not grounded in acoustic attributes of the voices per se, but in how these features are perceptually interpreted by listeners.

Comparative first principles investigation on the structural, optoelectronic and vibrational properties of strain-engineered graphene-like AlC3, BC3 and C3N monolayers

Three cardinal two-dimensional semiconductors viz., AlC3, BC3 and C3N, closely resembling the graphene structure, are intriguing contenders for emerging optoelectronic and thermomechanical applications. Starting from a critical stability analysis, this density functional theory study delves into a quantitative assessment of structural, mechanical, electronic, optical, vibrational and thermodynamical properties of these monolayers as a function of biaxial strain (ε) in a sublinear regime (−2%⩽ε⩽4%) of elastic deformation. The structures with cohesive energies slightly smaller than graphene, manifest exceptional mechanical stiffness, flexibility and breaking stress. The mechanical parameters have been deployed to further cultivate acoustic attributes and thermal conductivity. The hexagonal structures with mixed ionic-covalent molecular bonds have indirect electronic band-gap and work-function acutely sensitive to ε . Dispersions of optical dielectric function, energy loss, refractive index, extinction coefficient, reflectivity, absorption coefficient and conductivity are deciphered in the UV–Vis–NIR regime against strain, where particular frequency bands featuring high polarization, dissipation, absorbance or reflectance are identified. Phonon band-structure and density of states testify dynamic stability in the ground state for all systems except the compressed ones. A comprehensive group theoretical analysis is performed to cultivate rotational; infrared and Raman-active modes, and the nature of molecular vibrations is delineated. The red-shifting of phonon bands and E2g/A1g Raman peaks with increasing ε , associates estimation of Grüneisen parameter. Finally, strain-induced alterations of thermodynamic quantities such as entropy, enthalpy, free energy, heat capacity and Debye temperature are studied, followed by a molecular dynamics-based stability assessment under canonical ensemble.

Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients

Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFCC fusion. Nevertheless, the challenge of overlapping genres, particularly rock and country, demands special attention due to their shared acoustic attributes. The inherent similarities between these genres often lead to misclassification, hampering accuracy. To address this issue, an enhanced feature engineering strategy is devised, leveraging deeper insights into the subtle nuances that differentiate rock and country music. Additionally, a refined KNN distance metric and neighbor selection mechanism are introduced to further refine classification decisions. Experimental results underscore the effectiveness of the refined approach in mitigating genre overlap issues, significantly enhancing classification accuracy for rock and country genres. This study contributes to the advancement of music genre classification techniques, offering an innovative solution for handling overlapping genres and demonstrating the potential of KNN-MFCC synergy in achieving accurate and refined genre classification.

Investigation of acoustic attributes based on preference and perceptional acoustics of Korean traditional halls for optimal design solutions

Investigation of acoustic attributes based on preference and perceptional acoustics of Korean traditional halls for optimal design solutions

A general overview of methods for generating room impulse responses

The utilization of room impulse responses has proven valuable for both the acoustic assessment of indoor environments and music production. Various techniques have been devised over time to capture these responses. Although algorithmic solutions have been in existence since the 1970 s for generating synthetic reverberation in real time , they continue to be computationally demanding and in general lack the accuracy in comparison to measured authentic Room Impulse Responses (RIR). In recent times, machine learning has found application in diverse fields, including acoustics, leading to the development of techniques for generating RIRs. This paper provides a general overview, of approaches and methods for generating RIRs, categorized into algorithmic and machine learning techniques, with a particular emphasis on the latter. Discussion covers the acoustical attributes of rooms relevant to perceptual testing and methodologies for comparing RIRs. An examination of disparities between captured and generated RIRs is included to better delineate the key acoustic properties characterizing a room. The paper is designed to offer a foundational literature base for those interested in RIR generation for music production purposes, with future work considerations also explored.

Musical experience is linked to enhanced dimension-selective attention to pitch and increased primary weighting during suprasegmental categorization.

Speech perception requires the integration of evidence from acoustic cues across multiple dimensions. Individuals differ in their cue weighting strategies, that is, the weight they assign to different dimensions during speech categorization. In two experiments, we investigate musical training as one potential predictor of individual differences in prosodic cue weighting strategies. Attentional theories of speech categorization suggest that prior experience with the task-relevance of a particular dimension leads that dimension to attract attention. Experiment 1 tested whether musicians and nonmusicians differed in their ability to selectively attend to pitch and loudness in speech. Compared to nonmusicians, musicians showed enhanced dimension-selective attention to pitch but not loudness. Experiment 2 tested the hypothesis that musicians would show greater pitch weighting during prosodic categorization due to prior experience with the task-relevance of pitch cues in music. Listeners categorized phrases that varied in the extent to which pitch and duration signaled the location of linguistic focus and phrase boundaries. During linguistic focus categorization, musicians upweighted pitch compared to nonmusicians. During phrase boundary categorization, musicians upweighted duration relative to nonmusicians. These results suggest that musical experience is linked with domain-general enhancements in the ability to selectively attend to certain acoustic dimensions in speech. As a result, musicians may place greater perceptual weight on a single primary dimension during prosodic categorization, while nonmusicians may be more likely to choose a perceptual strategy that integrates across multiple dimensions. These findings support attentional theories of cue weighting, which suggest attention influences listeners' perceptual weighting of acoustic dimensions during categorization. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Information conveyed by voice qualitya).

The problem of characterizing voice quality has long caused debate and frustration. The richness of the available descriptive vocabulary is overwhelming, but the density and complexity of the information voices convey lead some to conclude that language can never adequately specify what we hear. Others argue that terminology lacks an empirical basis, so that language-based scales are inadequate a priori. Efforts to provide meaningful instrumental characterizations have also had limited success. Such measures may capture sound patterns but cannot at present explain what characteristics, intentions, or identity listeners attribute to the speaker based on those patterns. However, some terms continually reappear across studies. These terms align with acoustic dimensions accounting for variance across speakers and languages and correlate with size and arousal across species. This suggests that labels for quality rest on a bedrock of biology: We have evolved to perceive voices in terms of size/arousal, and these factors structure both voice acoustics and descriptive language. Such linkages could help integrate studies of signals and their meaning, producing a truly interdisciplinary approach to the study of voice.

Developing children's ASR system under low-resource conditions using end-to-end architecture

The work presented in this paper aims at enhancing the performance of end-to-end (E2E) speech recognition task for children's speech under low resource conditions. For majority of the languages, there is hardly any speech data from child speakers. Furthermore, even the available children's speech corpora are limited in terms of the number of hours of data. On the other hand, large amounts of adults' speech data are freely available for research as well as commercial purposes. As a consequence, developing an effective E2E automatic speech recognition (ASR) system for children becomes a very challenging task. One may develop an ASR system using adults' speech and then use it to transcribe children's data, but this leads to very poor recognition rates due to the stark differences in the acoustic attributes of adults' and children's speech. In order to overcome these hurdles and to develop a robust children's ASR system employing E2E architecture, we have resorted to several out-of-domain and in-domain data augmentation techniques. For out-of-domain data augmentation, we have explicitly modified adults' speech to render it acoustically similar to that of children's speech before pooling into training. On the other hand, in the case of in-domain data augmentation, we have slightly modified the pitch and duration of children's speech in order to create more data capturing greater diversity. Data augmentation approaches helps in mitigating the ill-effects resulting from the scarcity of data from child domain to a certain extent. This, in turn, reduces the error rates by a large margin. In addition to data augmentation, we have also studied the efficacy of Gamma-tone frequency cepstral coefficients (GFCC) and frequency domain linear prediction (FDLP) technique along with the most commonly used Mel-frequency cepstral coefficients (MFCC) for front-end speech parameterization. Both MFCC as well as GFCC capture and model the spectral envelope of speech. On the other hand, application of linear prediction on the frequency domain representation of speech signal helps to effectively capture the temporal envelope during front-end feature extraction. Employing FDLP features that model the temporal envelope provides important cues for the perception and understanding of stop bursts and, at times, complete phonemes. This motivated us to perform a comparative experimental study of the effectiveness of the three aforementioned front-end acoustic features. In our experimental explorations, the use of proposed data augmentation in combination of FDLP features has shown a relative improvement in character error rate by 67.6% over the baseline system. The combination of data augmentation with MFCC or GFCC features is observed to result in lower recognition performances.

Auditory processing as perceptual, cognitive, and motoric abilities underlying successful second language acquisition: Interaction model.

A growing amount of attention has been given to examining the domain-general auditory processing of individual acoustic dimensions as a key driving force for adult L2 acquisition. Whereas auditory processing has traditionally been conceptualized as a bottom-up and encapsulated phenomenon, the interaction model (Kraus & Banai, 2007) proposes auditory processing as a set of perceptual, cognitive, and motoric abilities-the perception of acoustic details (acuity), the selection of relevant and irrelevant dimensions (attention), and the conversion of audio input into motor action (integration). To test this hypothesis, we examined the relationship between each component and the L2 outcomes of 102 adult Chinese speakers of English who varied in age, experience, and working memory background. According to the results of the statistical analyses, (a) the tests scores tapped into essentially distinct components of auditory processing (acuity, attention, and integration), and (b) these components played an equal role in explaining various aspects of L2 learning (phonology, morphosyntax) with large effects, even after biographical background and working memory were controlled for. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Courtship behaviour reveals temporal regularity is a critical social cue in mouse communication.

While animals navigating the real world face a barrage of sensory input, their brains evolved to perceptually compress multidimensional information by selectively extracting the features relevant for survival. Notably, communication signals supporting social interactions in several mammalian species consist of acoustically complex sequences of vocalisations. However, little is known about what information listeners extract from such time-varying sensory streams. Here, we utilise female mice's natural behavioural response to male courtship songs to identify the relevant acoustic dimensions used in their social decisions. We found that females were highly sensitive to disruptions of song temporal regularity and preferentially approached playbacks of intact over rhythmically irregular versions of male songs. In contrast, female behaviour was invariant to manipulations affecting the songs' sequential organisation or the spectro-temporal structure of individual syllables. The results reveal temporal regularity as a key acoustic cue extracted by mammalian listeners from complex vocal sequences during goal-directed social behaviour.

Courtship behaviour reveals temporal regularity is a critical social cue in mouse communication

While animals navigating the real world face a barrage of sensory input, their brains evolved to perceptually compress multidimensional information by selectively extracting the features relevant for survival. Notably, communication signals supporting social interactions in several mammalian species consist of acoustically complex sequences of vocalisations. However, little is known about what information listeners extract from such time-varying sensory streams. Here, we utilise female mice’s natural behavioural response to male courtship songs to identify the relevant acoustic dimensions used in their social decisions. We found that females were highly sensitive to disruptions of song temporal regularity and preferentially approached playbacks of intact over rhythmically irregular versions of male songs. In contrast, female behaviour was invariant to manipulations affecting the songs’ sequential organisation or the spectro-temporal structure of individual syllables. The results reveal temporal regularity as a key acoustic cue extracted by mammalian listeners from complex vocal sequences during goal-directed social behaviour.

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.