Psychoacoustic Features Research Articles

Neural markers associated with improved tinnitus perception after tinnitus retraining therapy

Objective Tinnitus retraining therapy (TRT) has been widely used in tinnitus management. However, its efficacy is often assessed through subjective methods. Here, we aimed to assess potential neural changes following TRT using mismatch negativity (MMN). Design Chronic tinnitus (>6 months) patients participated in a six-month TRT program. We collected tinnitus psychoacoustic features and gathered the tinnitus handicap inventory (THI) before and after TRT. We also used a multi-featured paradigm, including frequency, intensity, duration, location and silent gap deviants, to elicit MMN response before and after TRT. Data were analyzed retrospectively. Study sample The study involved 26 chronic tinnitus patients. Results Post-TRT measurements showed that MMN amplitudes significantly increased for all deviant conditions (p ≤ .03). However, we did not find a significant difference in MMN latencies for all deviant conditions (p ≥ .13). The THI scores of the patients significantly decreased following the TRT program (p < 0.001). Our results reveal improved subjective tinnitus perception following the TRT program. Conclusion These findings indicate that TRT might be a viable alternative in tinnitus management. The greater MMN amplitudes and improved subjective tinnitus perception raise the possibility that MMN can be a useful tool in tinnitus research and tinnitus patient follow-up.

Extracting and classifying psychoacoustic features from online restaurant reviews

Recent work has shown that poor acoustic conditions persist in many restaurants. Owners who receive negative reviews of their establishment's soundscape may struggle to interpret the subjective customer responses into actionable corrective measures. Therefore, further work is needed to taxonomize the acoustically relevant keywords and phrases that occur in user reviews. In this study, an open-source database of restaurant, bar, and coffee shop reviews from across the United States has been obtained. Sentiment analysis and keyword count are used to extract positive, negative, and neutral subjective reviews and subjective features related to acoustics. The resulting subjective features are then categorized, weighed, and linked to objective acoustic parameters using machine learning techniques. Results from this study suggest that owners and consultants may be able to utilize online customer reviews to monitor acoustical comfort and ascertain the nature of an acoustical problem, not only in restaurants, but in any business sector where customers submit online user reviews.

Analysis of unsupervised learning approach for classification of vehicle fuel type using psychoacoustic features

Much research has been done in the field of classifying vehicles based on their fuel type. One of the many potential applications is to improve the quality of life in urban areas by separating vehicles based on their pollution level. Real-time classification and implementation of appropriate on-site IoT measurement devices is critical to developing a system that accurately identifies vehicle's fuel type without violating driver privacy. In this paper, a classification system based on psychoacoustic features extracted from sound recordings is investigated. Unsupervised learning was implemented as it is able to detect hidden connections within the input space without relying on labelling data. Our goal was to develop and explore a relatively fast classification system, focusing on a short acquisition time. A self-organizing map with a 10-dimensional input space was used and effective classification with five single-valued features was demonstrated. The analysis of the input space shows the difficulty and complexity of such an approach and leaves room for further improvement.

The influence of psychoacoustic features on the formation of technical and emotionally expressive skills in piano teaching: integrative analysis in the context of sensorimotor integration and musical perception

The influence of psychoacoustic features on the formation of technical and emotionally expressive skills in piano teaching: integrative analysis in the context of sensorimotor integration and musical perception

Improved fetal heartbeat detection using pitch shifting and psychoacoustics

ObjectiveFetal phonocardiography is a cost-effective and non-invasive method for monitoring of fetal health. However, the captured signal is often too noisy and unstable for reliable analysis. Detection of fetal heartbeats therefore requires a robust set of features to differentiate relevant sounds from background noise. We propose a novel set of features based on pitch shifting and psychoacoustics which lead to significant improvements of automatic fetal heartbeat detection. MethodsWe collected, annotated, cleaned, processed and segmented microphone recordings taken from pregnant women. In addition, we employed a well-known simulated dataset to validate the methodology further. A total of 212 features were extracted, including classical audio features, psychoacoustic features, and features based on Empirical Mode Decomposition. Methods from statistical analysis and machine learning were applied to demonstrate the relevance of specific features. Afterwards, we trained a set of machine learning algorithms with different subsets of features. ResultsPsychoacoustic features extracted from pitch-shifted signals are generally shown to be relevant and useful features for fetal heartbeat detection. When psychoacoustic features were added to the baseline features, classification accuracy of a robust classifier significantly increased from 69.24% to 76.16% for the real-world signals, and from 92.69% to 97.42% for one of the simulated datasets. ConclusionThe results demonstrate considerable gains in detection accuracy once pitch shifting and psychoacoustic modeling are applied to the input signal. SignificanceImprovements achieved from applying psychoacoustics on fetal phonocardiographic signals might be an important finding in the effort of making remote monitoring of fetal wellbeing inexpensive and accessible.

Annoyance Prediction of Civil Aircraft Cabin Noise Based on Generalized Additive Models

Noise in civil aircraft cabin is a major contributor to passenger comfort. It is of great significance to predict the noise annoyance by developing a model. However, the low accuracy of linear model and the poor intelligibility of the neural network are the critical problems to be solved. Generalized additive models (GAMs) have become a leading choice for explainable machine learning. This study examines the effectiveness of GAMs in noise annoyance prediction. First, the annoyance of the cabin noise was collected through subjective listening test. Subsequently, three physical acoustic features (sound pressure level, A-weighted sound pressure level, preferred speech interference level) and five psychoacoustic features (loudness, sharpness, roughness, fluctuation strength, tonality) were applied to describe the stimuli. In the third step, three algorithms were used to construct GAMs based on annoyance (dependent variable) and feature parameters (independent variables), and the prediction accuracy was computed by ten-fold cross-validation. The result indicated that GAMs have higher prediction accuracy than linear regression model. The importance of features was evaluated, which is more intelligible than the neural network. This study provides the first assessment of GAMs in the annoyance prediction of civil aircraft cabin noise, and the findings may be of assistance to engineering noise control.

Auditory Perception of Impulsiveness and Tonality in Vocal Fry

Vocal fry is a voice quality that occurs in a healthy voice, but it can also be a sign of a voice disorder. In this study, we investigated the relationship between the parameters of voice production, a dedicated psychoacoustic feature, and the perceptual aspects of vocal fry. Two perceptual experiments were carried out to determine whether the fundamental frequency, the open quotient, and the glottal area pulse skewness affect the perception of vocal fry in synthetic vowels. Thirteen listeners participated in the perceptual experiments to assess the following attributes: binary fry (yes/no) and impulsiveness, tonality, and naturalness (7-point Likert scales). The results suggest that the perception of vocal fry is mainly triggered by a low fundamental frequency, but the open quotient also plays a role, with narrower glottal area pulses slightly increasing the probability of perceived fry. Perceived tonality is inversely related to perceived impulsiveness. Internal reference standards of listeners appear to have fixed elements but may also be affected by anchoring and the short-term (i.e., within-vowel) context of the stimuli. In addition, the prominence of the peaks observed in the loudness curve over time appears to be related to graduations of fry.

Optimization of a contra-rotating propeller rig for reduced psychoacoustic impact

With unmanned aerial vehicles emerging as potential alternatives for people and cargo transport, their noise impact will be a determining factor in their acceptance by the general public. Contra-rotating propeller configurations are often explored due to their improved aerodynamic performance and redundancy in case of failure compared to conventional single-propeller aircraft, but can be much noisier than their single-propeller equivalent. This work describes the optimization of a custom-made contra-rotating propeller rig for reduced psychoacoustic impact. The rig consists of two electric motors mounted on a rotating stand, positioned inside an anechoic chamber. A far-field microphone arc is used to collect acoustic pressure data, and a load cell is used to measure total thrust. The axial distance between the propellers is varied between 0.1 and 1 rotor diameters, and the number of blades is varied between 2 and 6 on both propellers. Meanwhile, the rpm is adjusted to maintain constant thrust across the different configurations. Acoustic pressure signals are investigated in terms of their physical acoustic characteristics and psychoacoustic features (such as Loudness, Sharpness, Tonality, Fluctuation Strength, Roughness and Impulsiveness) in order to determine the trade-offs and optimal choices in reducing the psychoacoustic impact of the rig.

Acoustic Measurements and Psychoacoustic Analyses of Ventilation Diffusers

People in industrialised countries spend most of their days indoors and are often exposed to ventilation sounds. Since ventilation systems are not only used in industry but also increasingly in residential buildings, it is important to study its sound radiation and the consequences for our well-being. This research project aims to identify if geometric features of air diffusers affect the rated annoyance of ventilation systems. It is known that the A-weighted sound serves as a criterion to a limited extent. Therefore, psychoacoustic analyses and evaluations need to be included to find out if there are correlations between psychoacoustic features and flow phenomena caused by the geometric features of the air diffusers. Several air diffusers were acoustically measured in a hemi-anechoic room using a developed mobile ventilation unit that supplied the necessary air volume flow. The ventilation unit could be operated in both supply and extract air configuration of the diffusers. The measured data not only includes the acoustic directivity of the air diffusers but also recordings that are needed to conduct listening tests. To increase the ecological validity and to study the role of the environment, future listening experiments will be also extended to audio-visual experiments in virtual reality.

Psychoacoustic features explain creakiness classifications made by naive and non-naive listeners

We compared the classifications of words between creaky and non-creaky made by two groups of listeners whose native languages differ on whether creakiness is used for phonemic contrast. Japanese (naive group) and Vietnamese (non-naive group) listeners classified words produced by a single speaker of Du’an Zhuang, an unintelligible language for both groups that shares some tonal similarities with Hanoi Vietnamese. We found little differences between the accuracy achieved by the two groups, and that the non-naive group was more likely to rate words as creaky relative to the naive group. In addition, there seems to be no benefit of background linguistic knowledge on the accuracy with which the non-naive group classified words. The sensitivity to creakiness observed in classifications made by experts (inspecting the waveforms and spectrograms in addition to listening to the words) and made by a machine learning algorithm based on these kind of classifications was unmatched by both cohorts. This result challenges the perceptual validity of such refined classifications commonly used in phonation studies. In addition, we found a positive association between psychoacoustic roughness and the probability of a word to be judged as creaky. We also found a positive association of loudness with creaky judgments, whereas pitch was negatively associated. We found no evidence of sharpness and creaky association. Finally, the accuracy to predict subjective creakiness via a recurrent neural network classifier was best when the traces of all the considered psychoacoustic features were included as predictors.

"GENTLE DEEP SUBLIME ROUGH AZURE": ONOMATOPOEIA OF ONE NAME

The purpose of the proposed article is, by restoring the original meaning of onomatopoeia as the skill of giving names and based on modern research in psychoacoustics and phonosemantics, to reveal the influence of the sound of one's name on the character of its bearer, and once again raise the issue of the consequences of transposing anthroponyms to another language. The object is the proper name of the famous Ukrainian linguist Dmytro Semenovych Ishchenko (in Russian, "Dmytry Semionovych Ishchenko"). The subject of the study is the etymological, psychoacoustic features of the name in the Ukrainian and Russian versions in correlation with the linguistic reflection of its bearer and the objective features of the linguistic personality. The main result of the study is the identification the opposite synesthetic characteristics in the Ukrainian version of the anthroponym such as gentleness – roughness, depth – sublimity, energy – lethargy and others, which are not preserved when the name is transposed into the Russian language. The relevance of the problem is determined by the modern anthropocentric paradigm of science, the need for a holistic understanding of personal names from the point of view of ethno-, psycho-, socio-, cognitive linguistics and other aspects of the theory of linguistic existence of the individual, as well as the need to develop practical recommendations for transposing proper names to another language. Conclusions: The personal name that is given to a child and that a person hears during his or her life, with its sound and perceived etymology, affects the character of the linguistic personality, and therefore, in the absence of significant phonemic differences between languages, it should be transmitted using transliteration.

Automated identification and assessment of environmental noise sources

Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.

Active sound quality control of vehicle interior noise using a dual sampling-rate active noise equalization algorithm

In-vehicle active sound quality control (ASQC) system is an advanced application of active noise control (ANC) technology, which always suppresses the interior sound pressure or improves the psychoacoustic features. However, the connection between the sound quality control state and the vehicle's working state is normally ignored, and the passenger's sound perception typically expects a linear relationship between the vehicle speed and sound loudness. This paper presents a dual sampling-rate active noise equalization algorithm. The low sampling-rate signal is used to improve system computing efficiency, the high sampling-rate signal is used to improve the ASQC effect, and their conversion communication uses a hold (low to high) and a sampler (high to low). The data relating to the ASQC system was collected, and some sound quality evaluations were conducted for determining two appropriate sampling-rate values. Combining a genetic algorithm and the corresponding ASQC simulation system, the algorithmic optimal convergence coefficients and gain coefficients were further determined at different engine speeds. The verification results of ASQC using this proposed algorithm show that the loudness of interior noise is effectively suppressed; the nonlinear index is reduced to 1.37, improving by 37% relative to the original noise and by 20% compared to the ANC system.

Investigation of Metrics for Assessing Human Response to Drone Noise.

Novel electric air transportation is emerging as an industry that could help to improve the lives of people living in both metropolitan and rural areas through integration into infrastructure and services. However, as this new resource of accessibility increases in momentum, the need to investigate any potential adverse health impacts on the public becomes paramount. This paper details research investigating the effectiveness of available noise metrics and sound quality metrics (SQMs) for assessing perception of drone noise. A subjective experiment was undertaken to gather data on human response to a comprehensive set of drone sounds and to investigate the relationship between perceived annoyance, perceived loudness and perceived pitch and key psychoacoustic factors. Based on statistical analyses, subjective models were obtained for perceived annoyance, loudness and pitch of drone noise. These models provide understanding on key psychoacoustic features to consider in decision making in order to mitigate the impact of drone noise. For the drone sounds tested in this paper, the main contributors to perceived annoyance are perceived noise level (PNL) and sharpness; for perceived loudness are PNL and fluctuation strength; and for perceived pitch are sharpness, roughness and Aures tonality. Responses for the drone sounds tested were found to be highly sensitive to the distance between drone and receiver, measured in terms of height above ground level (HAGL). All these findings could inform the optimisation of drone operating conditions in order to mitigate community noise.

Perception of loudness changes induced by a phononic crystal in specific frequency bands

To study the influence of classical phononic crystal (PC) structures on the acoustical characteristics of a sound source, a combined acoustics/perceptual analysis is conducted on a PC specially designed to exhibit several spectral and wave vector properties in different audible frequency ranges. The properties, confirmed by both numerical calculations and experiments, consist in both partial and absolute band gaps, as well as a negative refraction band. A psychoacoustic feature, namely the loudness in third-octave bands, is estimated from numerical simulations of the acoustic field behind the crystal. Additional perceptual tests are conducted to evaluate the efficiency of the PC slab. In the frequency range of the band gaps, sound stimuli filtered by the PC’s impulse response are perceived as softer than stimuli resulting from a free-field propagation (FF), they also are perceived as equally (or close to equally) loud than sounds attenuated by a free-standing rigid wall (FS). In the frequency range of the focalization (negative refraction), PC sound stimuli sound louder than both FS and FF sound stimuli. The possibility of designing an efficient sound barrier based on the considered PC is finally discussed.

Cambridge Psycholinguistic Inventory of Christian Beliefs: A registered report of construct validity, internal consistency and test\u2013retest reliability

While religious beliefs are typically studied using questionnaires, there are no standardized tools available for cognitive psychology and neuroscience studies of religious cognition. Here we present the first such tool—the Cambridge Psycholinguistic Inventory of Christian Beliefs (CPICB)—which consists of audio-recorded items of religious beliefs as well as items of three control conditions: moral beliefs, abstract scientific knowledge and empirical everyday life knowledge. The CPICB is designed in such a way that the ultimate meaning of each sentence is revealed only by its final critical word, which enables the precise measurement of reaction times and/or latencies of neurophysiological responses. Each statement comes in a pair of Agree/Disagree versions of critical words, which allows for experimental contrasting between belief and disbelief conditions. Psycholinguistic and psychoacoustic matching between Agree/Disagree versions of sentences, as well as across different categories of the CPICB items (Religious, Moral, Scientific, Everyday), enables rigorous control of low-level psycholinguistic and psychoacoustic features while testing higher-level beliefs. In the exploratory Study 1 (N = 20), we developed and tested a preliminary version of the CPICB that had 480 items. After selecting 400 items that yielded the most consistent responses, we carried out a confirmatory test–retest Study 2 (N = 40). Preregistered data analyses confirmed excellent construct validity, internal consistency and test–retest reliability of the CPICB religious belief statements. We conclude that the CPICB is suitable for studying Christian beliefs in an experimental setting involving behavioural and neuroimaging paradigms, and provide Open Access to the inventory items, fostering further development of the experimental research of religiosity.

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

The recent development and deployment of Wireless Acoustic Sensor Networks (WASN) present new ways to address urban acoustic challenges in a smart city context. A focus on improving quality of life forms the core of smart-city design paradigms and cannot be limited to simply measuring objective environmental factors, but should also consider the perceptual, psychological and health impacts on citizens. This study therefore makes use of short (1–2.7 s) recordings sourced from a WASN in Milan which were grouped into various environmental sound source types and given an annoyance rating via an online survey with N=100 participants. A multilevel psychoacoustic model was found to achieve an overall R2=0.64 which incorporates Sharpness as a fixed effect regardless of the sound source type and Roughness, Impulsiveness and Tonality as random effects whose coefficients vary depending on the sound source. These results present a promising step toward implementing an on-sensor annoyance model which incorporates psychoacoustic features and sound source type, and is ultimately not dependent on sound level.

Recognition of sound source components in soundscape based on deep learning

Sound source components are a crucially important aspect in soundscape, since humans’ perception and evaluation of soundscape strongly depend on the recognition of the constituent sound sources of the acoustic environment. Over the last few years, numbers of studies examined the methods of automated recognition of environmental sound sources/events using machine learning technology. However, the recognition of sound source components in soundscapes remains a challenge for real applications. This present study utilizes the deep learning, a latest developed and advanced machine learning technology, to develop an algorithm to solve this challenge. Without manually extracted acoustic or psychoacoustic features based on expertise, this algorithm automatically extracts features from the spectrogram representation of the sound signals and makes the recognition. It produces the sound source components recognized in the acoustic environment and their respective present time. The algorithm achieves high recognition accuracy, based on binaural recordings of outdoor acoustic environment. For new recordings, i.e., test samples, the recognition accuracies vary from 81.3% to 100%, depending on the specific sound source. This algorithm developed can greatly help soundscape research, and can be applied in automatic soundscape description of sound source components, prediction of soundscape perception and evaluation, and soundscape management.

Soft computing based audio signal analysis for accident prediction

PurposeRoad accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.Design/methodology/approachThe authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.FindingsTemporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.Practical implicationsDenoising of the audio samples for perfect feature extraction was a tedious chore.Originality/valueThe existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

Being emotionally moved is associated with phasic physiological calming during tonic physiological arousal from pleasant tears

The experience of being emotionally “moved” is considered a valuable emotional experience. Although pleasant chills (goosebumps and shivers) and tears (weeping and a lump in the throat) are commonly associated with the experience of being emotionally moved, no previous studies have examined which of these psychophysiological responses is the most intrinsic to the phenomenon of being moved. We conducted two music listening experiments to examine this question. Both experiments revealed that, when chills and tears were reliably separated, chills evoked phasic increases in electrodermal activity, whereas tears induced phasic decreases in heart and respiration rate during tonic physiological arousal. Importantly, whereas tears predicted the experience of being moved, experiencing chills did not. Furthermore, psychoacoustic features of music did not explain the physiological response of chills and tears. The results demonstrated that the experience of being moved involved a sense of pleasure coupled with psychophysiological relief from tension. Based on extended attachment theory, the sequential process of physiological arousal to physiological calming, which is derived from abstract life-guiding ideas via the combination of sound and lyrics, may be important for evoking the experience of being emotionally moved. These psychophysiological characteristics could explain why people seek to be moved.