Psychoacoustic Model Research Articles

Railway psychoacoustic annoyance - an indicator for planning infrastructure projects

The joint project "Real-time Calculation, Auralization and Visualization of Sound Propagation and Noise Protection Measures for Infrastructure Projects" (EAV-Infra), funded by the German Federal Ministry for Economic Affairs, investigates the potentials of digital infrastructure planning based on Building Information Modeling (BIM) in combination with state-of-the art Auralization and Visualization technology as well as Psychoacoustic Modelling. Currently, log-scaled sound pressure levels, which are only informative to experts, are used to communicate the topic of noise exposure to the public. Auralization, in combination with a hearing-oriented noise index, will constitute a more intuitive and understandable evaluation basis for residents. One focal point of EAV-Infra is the development of a linearly-scaled metric for railway noise, which correlates better with human perception than the LAeq. Binaural recordings of a wide spectrum of different railway pass-by scenarios were presented in listening experiments to obtain semantic-differential information about the sounds as well as global and continuous annoyance ratings for individual passings. Currently, a railway psychoacoustic annoyance metric is being developed by comparing two fitting methods: a classical model based on common psychoacoustic quantities, as well as an AI model with a Mel-log-spectrogram-CRNN architecture. The approach and first results will be discussed in this contribution.

Psychoacoustic model for detecting the sensation of impulsivity in acoustic signals from refrigerators

This study investigates the perception of impulsivity in audio signals specifically caused by thermal expansion (cracking noise) in domestic refrigerators. It employs a multifaceted approach encompassing an analysis of sensitivity to impulsive events, an assessment of the prominence of impulse signals, and a correlation analysis of these data. The investigation revealed different responses to various sound samples, highlighting subjective variations. Using linear regression, correlation analysis demonstrated a robust and positive relationship (Pearson correlation coefficient of 0.851) between perceived impulsivity and the Impulsivity Prediction Model (IPM). This alignment underscores the reliability of the developed IPM in capturing and predicting subjective perceptions of low-amplitude transient signals. Comparisons between groups of participants, conducted using both Analysis of Variance (ANOVA) and t-tests, explored potential disparities related to gender, age, and acoustic knowledge. The results indicated no statistically significant differences in the perception of impulsivity concerning gender, age groups, or acoustic knowledge. In conclusion, this study provides insights into the perceptual aspects of impulsivity in audio signals from home refrigerators, specifically addressing thermal expansion noises, and establishes the reliability of the Impulsivity Prediction Model (IPM) as a tool for objective assessment. The congruence between subjective judgments and objective metrics enhances the applicability of IPM in diverse fields, from acoustic engineering to psychoacoustic research.

An annoyance model for urban air mobility vehicle noise in the presence of a masker

Proposed Urban Air Mobility (UAM) operations offer an alternative to road and rail traffic for local and regional movement of people and goods. To allow for large-scale adoption of UAM vertical takeoff and landing (VTOL) aircraft, it is critical to predict human annoyance response to the noise generated by these vehicles. This paper proposes a model that predicts an individual's perceived level of annoyance when presented with UAM VTOL aircraft noise in addition to a masking noise. The model predicts changes in annoyance based on subjective evaluation of detection, noticeability, and annoyance of noise in the presence of a masker. The application of this annoyance model will influence future work for a psychoacoustic annoyance model that incorporates UAM sound quality.

An Analog MP3 Compression Psychoacoustic Model Implemented on a Field-Programmable Analog Array

This effort describes a low-power analog MP3 psychoacoustic model designed, implemented, and demonstrated using a large-scale Field-Programmable Analog Array (FPAA) to allocate bits for MP3 compression. Although MP3 encoders are assumed to be a digital algorithm, this effort looks to create analog algorithm versions. This block could enable low-power real-time MP3 encoding of microphone input signals for higher-quality acoustic transmissions, significantly improving the audio quality of the compressed transmitted signal from battery-powered devices. An exponentially spaced filterbank enables a low-power representation consistent with human hearing transduction. An analog psychoacoustic model uses signal masking to determine channel bit rates. These designs would enable a fully integrated MP3 encoder when implementing the straightforward aspects of the MDCT.

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.

Planning Infrastructure Projects by means of Psychoacoustic Quantities in the Joint Project "EAV-Infra"

Within the joint project "Real-time Calculation, Auralization and Visualization of Sound Propagation and Noise Protection Measures for Infrastructure Projects" (EAV-Infra), funded by the Federal Ministry for Economic Affairs, the potentials of digital infrastructure planning based on Building Information Modelling (BIM) and the auralization of different states of planning will be combined. In this contribution, the objectives of the project will be presented with particular focus on the intended application of psychoacoustics. Currently, planning noise protection measures encompasses an elaborate comparison of various planning alternatives in a time-consuming process. Sound pressure levels, which are only informative to experts, are used to communicate states of planning to the public. Auralized signals of noise immissions and a description of the effectiveness of noise protection measures by hearing-oriented quantities could constitute a more intuitive and intelligible evaluation basis for project managers and residents alike. In EAV-Infra, noise exposure will be evaluated based on psychoacoustic quantities. Both classical quantities and newly developed indices, will be considered. In order to find a suitable psychoacoustic annoyance model for the railway context, listening experiments will be conducted, in which subjects evaluate various noise situations. Typical inner- and outer-city scenarios and various noise protection measures will be assessed.

The Zwicker's psychoacoustic annoyance models for noise-induced discomfort in the electric vehicle cabin

Acoustic comfort has become an essential feature with the rapid development of the electric vehicle (EV) industry. As a representative nonlinear model, Zwicker's psychoacoustic annoyance (PA) model and a series of modified ones are established to evaluate the acoustic comfort of different products. However, whether these PA models apply to the noise-induced discomfort in the EV cabin remains uncertain. We investigated the psychoacoustic metrics and subjective discomfort of the EV cabin noise running at various speeds from 10-110km/h under four typical road conditions, i.e., asphalt, concrete, gravel, and bump. Thirty subjects rated the discomfort caused by 16 noise stimuli on the standardized 0-10 point numerical scale. The correlation and partial correlation analysis confirmed that the loudness of EV cabin noise dominated, and roughness and sharpness significantly influenced the discomfort. We identified that the original Zwicker's PA model performed well in evaluating the discomfort caused by the EV cabin interior noise and proposed a modified Zwicker's PA model that would be appropriate. Keywords: Electric vehicle, Cabin noise, Psychoacoustic annoyance, Noise discomfort, Sound quality

Pyhre: Psychoacoustic model for human-robot emotional integration

There are still many design problems for service robots. Among the most important problems is human-robot integration, a problem that has many edges, both at the level of physical interaction and at the emotional level. The research group is evaluating different algorithms on its robotic platform ARMOS TurtleBot. Among these algorithms, it recently developed a scheme for the identification of a person's emotions from identifiable facial characteristics in a person's face. Under laboratory conditions, the scheme reached a 92% success rate, however, in low-light conditions or when the person had the face partially covered this rate decreased considerably. Consequently, we propose the design of an alternative loop as a support to increase the success rate by estimating the emotional state of the person from the voice. For this purpose, we train a convolutional neural network with spectral images built from audio characteristics of the same 7 emotions used in the first algorithm. The model achieved a 69% hit rate, and together with our face algorithm raised the total performance of the system to 96.5%.

Experimental investigations on aerodynamic and psychoacoustic characteristics of three-blade looprop propeller

Aeroacoustic noise in multiple rotor drones has been increasingly recognized as a crucial issue. This study focuses on addressing this challenge by introducing a novel low-noise looprop propeller design with three blades. Unlike traditional propellers, the looprop propeller utilizes three closed-loops to generate thrust. Through comprehensive experimental investigations conducted in an anechoic chamber using a hover stand test, we conducted an integrated study of the propeller's aerodynamic, aeroacoustic, and psychoacoustic characteristics. The results demonstrate that the three-blade looprop propeller achieves a notable reduction in the overall sound pressure level (OASPL), particularly in tonal noise at the blade passing frequency where the tone reduction amounts to approximately 15 dB. Additionally, we employed Zwicker psychoacoustic annoyance models to evaluate the propellers' psychoacoustic performance and discussed the environmental noise mask effect. The findings indicate that the three-blade looprop propeller exhibits an improved sound quality while maintaining aerodynamic performance comparable to that of conventional propellers. Overall, our study suggests that the looprop propeller design offers significant advancements in acoustic performance, particularly in terms of psychoacoustic characteristics. These findings hold promising implications for the field of drone technology and the potential for notable improvements in noise reduction.

Model predictive PESQ-ANFIS/FUZZY C-MEANS for image-based speech signal evaluation

This paper presents a new method to evaluate the quality of speech signals through images generated from a psychoacoustic model to estimate PESQ (ITU-T P862) values using a first-order Fuzzy Sugeno approach implemented in the Adaptive Neuro-Fuzzy Inference System - ANFIS. The factors feeding the network were obtained using an image-processing technique from the perceptual model coefficients. All simulations were performed using a database containing clean and corrupted signals by eight types of noises found in everyday situations. The proposal uses the PESQ values of the signals to train the network. The analyses proved that the predictive performance will depend on the choice of a psychoacoustic model, the factor extraction technique, the combination of these factors, the fuzzification algorithm, and the type of membership function in the ANFIS input space. The data sets for training and testing for each signal directory were randomly created and executed fifty times. The proposal achieves the best prediction values for PESQ when the averages of the measurements reach MAPE ≤0.09, RMSE ≤0.20, and R2≥95. In general, the approach provided satisfactory results compared to Multilayer Perceptron networks with their different learning algorithms, compared to another psychoacoustic model, to ITU-T P.563 and other non-intrusive methods that evaluate the quality of voice signals, and it was efficient regardless of the number of signals and the database used.

Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network

The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.

Sustainable Soundscape Monitoring of Modified Psycho-Acoustic Annoyance Model with Edge Computing for 5G IoT Systems

Next-generation IoT systems will allow sustainable performance in long-term monitoring systems. This sustainability concept applies to soundscape description, as it allows monitoring in urban environments. In this work, the implementation of psycho-acoustic annoyance models in a 5G-enabled IoT system is proposed, applying two edge-computing approaches. A modified Zwicker’s model is adopted in this research, introducing a term that takes into account the tonal component of the captured sound. These implementations have been validated in a measurement campaign where several IoT devices have been deployed to evaluate different sound environments of a university campus. Then, the analysis of the sound-quality metrics is conducted in a different location, showing that if tonality is present in a noisy environment, it results in greater subjective annoyance. Moreover, the Just-Noticeable Difference of these results is derived from Zwicker’s psycho-acoustic annoyance to establish a limitation for this metric.

The structure of acoustic voice variation in bilingual speech.

When a bilingual switches languages, do they switch their voice? Using a conversational corpus of speech from early Cantonese-English bilinguals (n = 34), this paper examines the talker-specific acoustic signatures of bilingual voices. Following the psychoacoustic model of voice, 24 filter and source-based acoustic measurements are estimated. The analysis summarizes mean differences for these dimensions and identifies the underlying structure of each talker's voice across languages with principal component analyses. Canonical redundancy analyses demonstrate that while talkers vary in the degree to which they have the same voice across languages, all talkers show strong similarity with themselves, suggesting an individual's voice remains relatively constant across languages. Voice variability is sensitive to sample size, and we establish the required sample to settle on a consistent impression of one's voice. These results have implications for human and machine voice recognition for bilinguals and monolinguals and speak to the substance of voice prototypes.

Imperceptible adversarial audio steganography based on psychoacoustic model

Imperceptible adversarial audio steganography based on psychoacoustic model

Integrated Evaluation of the Aeroacoustics and Psychoacoustics of a Single Propeller.

Aeroacoustic noise in multiple rotor drones has been increasingly recognized as a crucial issue, while noise reduction is normally associated with a trade-off between aerodynamic performance and sound suppression as well as sound quality improvement. Here, we propose an integrated methodology to evaluate both aeroacoustics and psychoacoustics of a single propeller. For a loop-type propeller, an experimental investigation was conducted in association with its aerodynamic and acoustic characteristics via a hover stand test in an anechoic chamber; the psychoacoustic performance was then examined with psychoacoustic annoyance models to evaluate five psychoacoustic metrics comprising loudness, fluctuation strength, roughness, sharpness, and tonality. A comparison of the figure of merit (FM), the overall sound pressure level (OASPL) and psychoacoustic metrics was undertaken among a two-blade propeller, a four-blade propeller, the loop-type propeller, a wide chord loop-type propeller, and a DJI Phantom III propeller, indicating that the loop-type propeller enables a remarkable reduction in OASPL and a noticeable improvement in sound quality while achieving comparable aerodynamic performance. Furthermore, the psychoacoustic analysis demonstrates that the loop-type propeller can improve the psychological response to various noises in terms of the higher-level broadband and lower-level tonal noise components. It is thus verified that the integrated evaluation methodology of aeroacoustics and psychoacoustics can be a useful tool in the design of low-noise propellers in association with multirotor drones.

Bilingual acoustic voice variation: the case of Sorani Kurdish-Persian speakers

Many individuals around the world speak two or more than two languages. This phenomenon adds a fascinating dimension of variability to speech, both in perception and production. But do bilinguals change their voice when they switch from one language to the other? It is typically assumed that while some aspects of the speech signal vary for linguistic reasons, some indexical features remain unchanged across languages. Yet little is known about the influence of language on within- and between-speaker vocal variability. The present study investigated how acoustic parameters of voice quality are structured in two languages of a bilingual speaker and to what extent such features may vary between bilingual speakers. For this purpose, speech samples of 10 simultaneous Sorani Kurdish-Persian bilingual speakers were acoustically analyzed. Following a psychoacoustic model proposed by Kreiman (2014) and using a series of principal component analyses, we found that Sorani Kurdish-Persian bilingual speakers followed a similar acoustic pattern in their two different languages, suggesting that each speaker has a unique voice but uses the same voice parameters when switching from one language to the other.

Development of psychoacoustics index for motorcycle exhaust noise by multiple regression method

Exhaust sound is an essential characteristic of a motorcycle when it comes to the interest of young motorcycle riders. It has become such an essential characteristic that riders, after looking into the performance, style, and ergonomics, look into the exhaust sound produced by the muffler. It has thus become critical for motorcycle manufacturers to tune their mufflers in such a way that they are appealing to the customers while adhering to sound emission regulations. As per the current trend of Indian motorcycle rider’s scenario, more attention is given to sound attributes like beat feeling, sportiness, and pleasantness. This research paper aims to develop a psychoacoustic model that can rate the exhaust sound of motorcycles targeting the Indian customer’s preferences and interests with regard to the engine exhaust sound. Presently, a study has been carried out on beat feeling, sportiness, and pleasantness, a perceivable attribute of sound in the family of motorcycles. Motorcycle buyers often perceive the exhaust sound of motorcycles in idling conditions at some distance away from the muffler, which formed the basis while recording exhaust sounds. Initially, exhaust sounds were recorded, which led to the calculation of psychometrics as objective variables. The calculated objective variables were given as input to the multiple regression model. A jury panel consisting of experienced NVH professionals and riders evaluated these sounds on a 7-point evaluation scale. These subjective ratings were given as the target variable to the multiple regression model. The obtained model was later validated with subjective data of motorcycles, including those at the prototype stage. Thus, a model has been established to aid exhaust designers in comparing their motorcycle at different prototype stages with the competitor’s vehicle, allowing them to make an early decision with respect to the muffler design modifications, thereby reducing the time-consuming and expensive jury tests.

The Improving Effect of Intelligent Speech Recognition System on English Learning

To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ears, and studies a coding strategy of a psychoacoustic masking model based on the characteristics of human ears. Moreover, this study analyzes in detail the basic principles and implementation process of the psychoacoustic model coding strategy based on the characteristics of the human ear and completes the channel selection by calculating the masking threshold. In addition, this study verifies the effectiveness of the algorithm in this study through simulation experiments. Finally, this study builds a smart speech recognition system based on this model and uses simulation experiments to verify the effect of smart speech recognition on English learning. To improve the voice recognition effect of smart speech, band-pass filtering and envelope detection adopt the gammatone filter bank and Meddis inner hair cell model in the mathematical model of the cochlear system; at the same time, the masking effect model of psychoacoustics is introduced in the channel selection stage to prevent noise. Sex has been improved, and the recognition effect of smart voice has been improved. The analysis shows that the intelligent speech recognition system proposed in this study can effectively improve the effect of English learning. In particular, it has a great effect on improving the effect of oral learning.

Identification of prominent noise components of an electric powertrain using a psychoacoustic model

Because of the electric power transmission system has no sound masking effect compared with the traditional internal combustion power transmission system, electric powertrain noise has become the prominent noise of electric vehicles, adversely affecting the sound quality of the vehicle interior. Because of the strong coupling of motor and transmission noise, it is difficult to separate and identify the compositions of the electric powertrain by experiments. A psychoacoustic model is used to separate and identify the noise sources of the electric powertrain of a vehicle, considering the masking effect of the human ear. The electric powertrain noise is tested in a semi-anechoic chamber and recorded by a high-precision noise sensor. The noise source compositions of the electric powertrain are analyzed by the computational auditory scene analysis and robust independent component analysis. Five independent noise sources are obtained, i.e., the fundamental frequency of the first gear mesh noise, fundamental frequency of the second gear mesh noise, double frequency of the second gear mesh noise, radial electromagnetic force noise and stator slot harmonic noise. The results provide a guide for the optimization of the sound quality of the electric powertrain and for the improvement of the sound quality of the vehicle interior.

Psychoacoustic modelling of rotor noise.

The aviation sector is rapidly evolving with more electric propulsion systems and a variety of new technologies of vertical take-off and landing manned and unmanned aerial vehicles. Community noise impact is one of the main barriers for the wider adoption of these new vehicles. Within the framework of a perception-driven engineering approach, this paper investigates the relationship between sound quality and first order physical parameters in rotor systems to aid design. Three case studies are considered: (i) contra-rotating versus single rotor systems, (ii) varying blade diameter and thrust in both contra-rotating and single rotor systems, and (iii) varying rotor-rotor axial spacing in contra-rotating systems. The outcomes of a listening experiment, where participants assessed a series of sound stimuli with varying design parameters, allow a better understanding of the annoyance induced by rotor noise. Further to this, a psychoacoustic annoyance model optimised for rotor noise has been formulated. The model includes a novel psychoacoustic function to account for the perceptual effect of impulsiveness. The significance of the proposed model lies in the quantification of the effects of psychoacoustic factors, such as loudness as the dominant factor, and also tonality, high frequency content, temporal fluctuations, and impulsiveness on rotor noise annoyance.