Psychoacoustic Parameters Research Articles

Evaluation Index for Sporty Engine Sound Reflecting Evaluators’ Tastes, Developed Using K-means Cluster Analysis

Developments in vehicle technology and accompanying improvements in NVH performance have led to increased consumer demand for high sound quality, such as a “sporty” engine sound. As sporty sound is subjective, this study sought to express its meaning quantitatively and to develop a model that accommodates the differences in individuals’ tastes. Engine sounds of four vehicles at wide-open throttle were recorded and the signal was modulated via filtering. Acoustic and psychoacoustic parameters of the samples produced were calculated, and the preferences for sportiness were identified through jury testing. Using K-means clustering, factor analysis, and multiple linear regression, a sound quality index and a model reflecting differences in evaluators’ tastes were developed. The index was retested using new evaluators and new samples, demonstrating its reliability by high correlation. This sound quality evaluation index is useful for producing highly accurate results and reflecting the opinions of groups expressing a variety of commonalities.

Enabling Real-Time Computation of Psycho-Acoustic Parameters in Acoustic Sensors Using Convolutional Neural Networks

Sensor networks have become an extremely useful tool for monitoring and analysing many aspects of our daily lives. Noise pollution levels are very important today, especially in cities where the number of inhabitants and disturbing sounds are constantly increasing. Psycho-acoustic parameters are a fundamental tool for assessing the degree of discomfort produced by different sounds and, combined with wireless acoustic sensor networks (WASNs), could enable, for example, the efficient implementation of acoustic discomfort maps within smart cities. However, the continuous monitoring of psycho-acoustic parameters to create time-dependent discomfort maps requires a high computational demand that prevents real-time computations within the nodes. Moreover, sending audio streams outside of the WASN for their further computation, would require extra communication and computational efforts without warranting a real-time monitoring, with the added problem of violating some privacy laws. As a result, most existing systems for nuisance assessment are usually based on less accurate indicators that require lower computational cost. In this paper, we describe the design and analysis of a deep convolutional neural network (CNN) trained with a big dataset of typical sounds occurrying in a city. The CNN allows to predict the psycho-acoustic parameters considered by the well-known Zwicker’s psycho-acoustic nuisance model with great accuracy, directly from the raw recorded audio signal. The proposed CNN-based system has been tested on both desktop computers and typical WASN devices (such as Raspberry Pi), achieving very fast calculation times that allow real-time operation and a continuous monitoring of psycho-acoustic parameters.

Individualized Treatment of Tinnitus during Sleep Using Combined Tinnitus Signal and Music

Introduction: Tinnitus is a widely seen otological symptom that interferes with daily activities and causes discomfort. Tinnitus treatments can be classified into 4 main groups: pharmacological treatments, cognitive and behavioral therapy, psychological treatments, and combined treatment approaches made up of at least 2 of these 3 treatment methods. Objective: The aim of this study was to assess whether it would be possible to develop an individualized treatment method of tinnitus by application of a combined tinnitus signal and music during sleep. Methods: Forty-three ears of 30 patients who had subjective tinnitus were included. The patients were evaluated using Tinnitus Handicap Inventory, Visual Analogue Scale, and Beck Depression Inventory. The psychoacoustic parameters of tinnitus, such as tinnitus frequency and loudness, and minimal masking levels, were determined. The patients were asked to select musical melodies that they liked. The tinnitus frequency of each patient was taken as the central frequency according to ANSI 2004. All sound files were prepared as stereo channels, with 16-bit resolution and 44,100 Hz sampling rate. The root mean square power value of the music and the band noise’s average root mean square power value were equalized with the “Amplification” command, and 70% of the music and 30% of wide/narrow-band noise were mixed as a stereo channel by the “Mix Paste” command. The patients were instructed to listen to that individualized music/narrow-band noise (tinnitus signal) for 2 h during sleep for a duration of 6 months. Results: Tinnitus frequencies of the patients measured prior to treatment and at the second, fourth, and sixth months of follow-up were not significantly different. A statistically significant decrease was seen in tinnitus loudness, minimal masking levels, and residual inhibition during the follow-up. Tinnitus Handicap Inventory scores decreased significantly during follow-up, and the number of patients who complained of tinnitus decreased (p < 0.05). The Visual Analogue Scale scores significantly decreased during follow-up (p < 0.05). Beck Depression Inventory scores decreased significantly during follow-up (p < 0.05). Conclusion: Stimulation of the auditory and limbic systems during sleep by the tinnitus signal combined with individualized musical melodies seems an alternative, effective, and cheap method in the treatment of tinnitus.

Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network

Higher education institution campuses have been growing in size to become small cities that encompass all the infrastructures and offer many services to the university community. In order to achieve sustainable growth, it is necessary to take into account different environmental aspects, including the acoustic environment. In this paper, the application of a wireless acoustic sensor network is proposed for the long-term analysis of psychoacoustic parameters of the acoustic environment in a university campus. A network of acoustic nodes is designed and four nodes are deployed at both indoor and outdoor locations of the campus for environmental noise monitoring. A measurement campaign has been running continuously for nine months, including three months in which the state of emergency due to COVID-19 pandemic was established and the university switched to online classes. Acquired and stored data include not only the basic sound level parameters, e.g., equivalent or percentile sound pressure levels, but also psychoacoustic parameters, that are more related with noise assessment of the environment, e.g., loudness or sharpness sound levels. After analysing the acoustic environment of the campus through the complete set of nodes, results show two zones with higher noise indicators where some action plan should be developed. A detailed analysis of the temporal evolution of noise levels in these acoustically saturated zones is done, comparing values between a period of regular activity and the period of state of emergency. Different frequency distributions of sound levels are detected between day and evening periods providing a better insight into the noise sources. The results also show that binaural psychoacoustic annoyance values are correlated with loudness values in these locations, however sharpness values have minor effects.

The relation among tinnitus distress, psychoacoustic parameters and anxiety state in hearing-impaired patients with chronic tinnitus: a pilot study

Background and Aim: Anxiety and depression are prevalent psychological disorders accompany tinnitus that have adverse effects on quality of life of these patients and on the outcomes of rehabilitation programs. The goal of this study was determining and quantifying the relation among perceived tinnitus severity, its psycho­acoustic parameters and anxiety symptoms in hearing-impaired individuals with subjective chronic tinnitus and residual inhibition (RI) by using Persian version of Spielberger's State-Trait Anxiety Inventory (STAI), tinnitus handi­cap inventory-Persian version (THI-P) and visual analogue scale (VAS) besides psychometric evaluations. Methods: Fourteen patients with chronic tinnitus were enrolled in this cross-sectional study. Con­ventional psychoacoustic tinnitus evaluations (i.e. determining quality of tinnitus, pitch match­ing, loudness matching, minimum masking level and RI) were conducted for all subjects, and they also completed the Persian version of STAI and THI-P. For screening of tinnitus perceived loud­ness and distress, VAS was used. Results: There was no correlation among THI-P, VAS scores and psychoacoustic parameters. There was a positive significant correlation bet­ween THI-P and Persian STAI scores (r = 0.63; p = 0.01). There was not any significant corre­lation between tinnitus duration and VAS, STAI or THI-P. There also had no significant corre­lation between VAS and THI-P (r = 0.56; p = 0.2). Conclusion: The present pilot study showed that Persian version of THI and STAI are corre­lated questionnaires. Therefore, Persian version of these questionnaires are valuable tools for evaluation of patients with chronic tinnitus. Keywords: Anxiety; tinnitus; tinnitus handicap inventory; state-trait anxiety inventory; visual analog scale; psychoacoustics

A comparison between psychoacoustic parameters and condition indicators for machinery fault diagnosis using vibration signals

There are many methods capable of establishing the diagnosis of machinery. The vast majority make use of different condition indicators, extracting information from the vibration signals obtained in the frame. During this work, in addition to using traditional techniques, it was analysed the validity of psychoacoustic metrics as a tool for detecting mechanical failures. Although these parameters were originally developed to be used with pressure signals, in this work they were applied exclusively to vibrations. A statistically representative group of samples was selected and studied in order to determine the standard behaviour of the device. At the same time, the noise patterns generated by defective samples were catalogued according to the type of failure by means of a subjective analysis. Finally, condition indicators and psychoacoustic metrics were applied to vibration signals, carrying out a comparison between them and establishing the most appropriate parameters for the quality control of gearmotors. The results revealed a high correlation between some indicators and similar outcomes were obtained for both methods. Additionally, the combination of the two techniques provided an improvement of the diagnosis, obtaining near 90% of success.

Validation of artificial neural networks to model the acoustic behaviour of induction motors

In the last decade, the sound quality of electric induction motors is a hot topic in the research field. Specially, due to its high number of applications, the population is exposed to physical and psychological discomfort caused by the noise emission. Therefore, it is necessary to minimise its psychological impact on the population. In this way, the main goal of this work is to evaluate the use of multitask artificial neural networks as a modelling technique for simultaneously predicting psychoacoustic parameters of induction motors. Several inputs are used, such as, the electrical magnitudes of the motor power signal and the number of poles, instead of separating the noise of the electric motor from the environmental noise. Two different kind of artificial neural networks are proposed to evaluate the acoustic quality of induction motors, by using the equivalent sound pressure, the loudness, the roughness and the sharpness as outputs. Concretely, two different topologies have been considered: simple models and more complex models. The former are more interpretable, while the later lead to higher accuracy at the cost of hiding the cause-effect relationship. Focusing on the simple interpretable models, product unit neural networks achieved the best results: 38.77 for MSE and 13.11 for SEP. The main benefit of this product unit model is its simplicity, since only 10 inputs variables are used, outlining the effective transfer mechanism of multitask artificial neural networks to extract common features of multiple tasks. Finally, a deep analysis of the acoustic quality of induction motors in done using the best product unit neural networks.

A Numerical Approach for Sound Quality of Vehicle Doors

A Vibro-Acoustic Finite Element Method (FEM) model capable of calculating the transient sound pressure generated by the door slam of a vehicle was developed in this study. A design sensitivity analysis (DSA) was performed for investigating the effects of major design variables on the related sound quality metrics. The methodology was developed using a sedan-car and its FEM model. This paper shows that a Computer Aided Engineering (CAE) model can be used as a rather powerful tool for giving design change decisions for the door components from sound quality point of view during vehicle body development according to psychoacoustic parameters.

Perceptual evaluation of the citizen’s acoustic environment from classic noise monitoring

ABSTRACT Noise is one of the main environmental pollutants and it is becoming a growing public health concern due to its impact on the quality of life in urban areas. In order to monitor critical locations, Wireless Acoustic Sensor Networks are being deployed, mainly in European and American cities. These networks focus primarily on measuring noise levels associated with road traffic. However, a number of different sounds are also present in urban areas and if our overall aim is to improve the quality of life of city inhabitants, then these additional sounds should be considered in accordance with the soundscape approach. This think piece proposes a step forward in the analysis of citizens’ perception of noise annoyance. We want to analyse the degree of annoyance caused by different sounds and study how this is related to psychoacoustic parameters. To this end, we propose to assess the acoustic environment by conducting perceptual tests on data gathered in a noise monitoring network. The proposed methodology is presented and discussed, and several open questions are posed to the research community.

The Co-Pilot Position of Vehicle Noise Quality Prediction Based on SVM

In three different types of domestic cars for example, the paper studied interior noise sound quality when engine idle at a constant speed in different speed operation. Noise test using HEAD Acoustics multi-channel noise test system, sound quality subjective evaluation experiment was carried out in a semi-anechoic. Vehicle sound quality was studied using support vector machine (SVM) model, the results showed that car interior sound quality mainly by four psycho-acoustical parameters: A-weighted sound pressure level, loudness, roughness and jitter degree. The support vector machine forecasting method can guarantee the accuracy of prediction results for small sample data, which has higher accuracy in predicting, error lower.

A Vigilance Explanation of Musical Chills? Effects of Loudness and Brightness Manipulations

Recent research has suggested that low-level psychoacoustic parameters such as loudness and spectral brightness are correlated with musical chills, a subjective emotional experience accompanied by goosebumps, shivers, and tingling sensations. These relationships may be explained by a vigilance theory of chills, through the process of auditory looming; however, these correlations or theories have never been causally tested. In the current study, participants ( N = 40) listened to five variations (original, low loudness, high loudness, low brightness, high brightness) of an experimental and control piece of chills music, characterized by a crescendo and guitar solo respectively; this qualitative distinction was made based on whether the underlying musical structure of the pieces was or was not capable of engaging auditory looming processes. It was predicted that increases in loudness would result in increased chills frequency across participants, indicated by button presses; brightness was included as an exploratory parameter. Results show that for the experimental piece, increases in loudness resulted in significantly more frequent chills experiences, and increases in brightness significantly reduced the frequency of chills, whereas no effects were found for the control piece. Findings are discussed in terms of vigilance and social bonding theories of chills, and the complex interactions between low-level psychoacoustic properties and higher-level musical structures.

Quantification and characterization of the role of subjective preferences on vehicle acceleration sound quality

The sound quality of vehicle acceleration, which directly reflects a consumer’s subjective perception of the driving ability and riding comfort of a particular vehicle, has received significant interest. To investigate consumer perception of acceleration sounds, a subjective evaluation with 14 acceleration sound samples was conducted using a grouped pairwise comparison (GPC). After clustering the valid evaluation data, two main subjective acceleration preferences (SAPs) were found. Because a subjective evaluation includes certain misjudgments, a fuzzy reconstructed algorithm is proposed to quantify the subjective evaluation results while a GPC is used. Nine objective parameters highly correlated with the SAP, including psychoacoustic parameters and typical acceleration order characteristics, were selected through a correlation analysis. A support vector regression algorithm with particle swarm optimization (SVR-PSO) was applied for modeling the SAP. Through a quantitative analysis of the relationship between the SAP and objective parameters, two main SAPs, classified as luxury and sporty sound qualities, were characterized. To verify the characteristics of luxury and sporty sound qualities, some acceleration sound samples were modified and compared with the original acceleration sound samples. The experimental results validate the accuracy of the predicted model and the characteristics of the two types of preferred acceleration sound quality.

A comparative study on subjective feeling of engine acceleration sound by automobile types

To achieve marketing success, automobile companies are constantly striving to develop design elements that can create effective experiences for customers and establish their brand identity. Among the various design elements, engine sound is an important feature, which has been widely studied to understand the subjective feeling of the sound quality. This study aims to identify the representative affective variables of engine acceleration sound based on different vehicle types, and analyze their relation with psychoacoustic parameters. Affective adjectives were collected from previous researches and used to evaluate the engine acceleration sounds of different vehicle types, including 14 sedan cars and 10 sports cars, which were selected for the experiment. The third gear wide open throttle sounds of each automobile were recorded and evaluated by 36 adults. To quantify the representative affective variables, four psychoacoustic parameters namely loudness, sharpness, roughness, and tonality were utilized. The results showed there are two representative affective variables namely “comfort” and “sportiness”, which are significantly different across the vehicle types. The regression models for the affective variables showed distinct differences in the vehicle types. Furthermore, the results showed that the subjective feelings for engine acceleration sound depends on the vehicle type. The results of this research can be used to develop engine acceleration sounds that optimize subjective feelings for various vehicle types.

Psychoacoustic active noise control system based on empirical mode decomposition

A psychoacoustic active noise control (ANC) system based on empirical mode decomposition (EMD) is proposed and implemented to improve the noise reduction performance of the control system. The noise source signal is decomposed by EMD, and the psychoacoustic parameter â–œloudnessâ–? of each intrinsic mode function (IMF) is initially calculated in such a system. Thereafter, the high-pass psychoacoustic weighting filter used to shape the error and reference signals is designed adaptively and automatically according to the loudness, peak frequency, and amplitude of each IMF. Three different ANC systems are simulated, and the sound pressure levels and loudness of their residual error signals are compared. The results demonstrate that the filter designed using this method can restrain the components of noise sources with small loudness better than the A-weighting shaping filter, so that the proposed control system can improve the noise reduction compared to those of the filtered-x least mean square and A-weighting shaping filters. Finally, the computational complexity of the three ANC systems is analyzed and compared.

The Effect of Noise Exposure on Cognitive Performance and Brain Activity Patterns.

BACKGROUND:It seems qualitative measurements of subjective reactions are not appropriate indicators to assess the effect of noise on cognitive performance.AIM:In this study, quantitative and combined indicators were applied to study the effect of noise on cognitive performance.MATERIAL AND METHODS:A total of 54 young subjects were included in this experimental study. The participants’ mental workload and attention were evaluated under different levels of noise exposure including, background noise, 75, 85 and 95 dBA noise levels. The study subject’s EEG signals were recorded for 10 minutes while they were performing the IVA test. The EEG signals were used to estimate the relative power of their brain frequency bands.RESULTS:Results revealed that mental workload and visual/auditory attention is significantly reduced when the participants are exposed to noise at 95 dBA level (P < 0.05). Results also showed that with the rise in noise levels, the relative power of the Alpha band increases while the relative power of the Beta band decreases as compared to background noise. The most prominent change in the relative power of the Alpha and Beta bands occurs in the occipital and frontal regions of the brain respectively.CONCLUSION:The application of new indicators, including brain signal analysis and power spectral density analysis, is strongly recommended in the assessment of cognitive performance during noise exposure. Further studies are suggested regarding the effects of other psychoacoustic parameters such as tonality, noise pitch (treble or bass) at extended exposure levels.

A Virtual-Reality and Soundscape-Based Approach for Assessment and Management of Cultural Ecosystem Services in Urban Forest

The work develops an analysis based on integrated perception of landscape and soundscape in urban forests (UF) to classify recreational suitability at spatial level. Scientific literature stresses a lack of decision support systems allowing for management of cultural ecosystem services in UF. An innovative approach grounded on landscape and soundscape’s individual perceptions are implemented to cope with this need. Geographic Information System, virtual reality and psychoacoustic parameters are merged to allow for an improved elicitation of willingness to visit UF. Geostatistical methods and the use of the Street View application enable for spatialization of output. The test area is located in an urban park of the city of Florence (Italy). Results stress the importance of logistics and tree variables (e.g., density) to assess the cultural service in the case of visual perception. Natural and people-related sounds as well as aural loudness seem to be significant for integrated perception, in addition to visual parameters. The open-source approach applied in the research can simplify replication to other case studies and the updating of the output. Future improvement and integration of the work for UF recreational planning are suggested.

Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

Research and development of sound quality in portable testing and evaluation system based on self-adaptive neural network

Ordinary microphone cannot accurately reflect authentic human feelings in the sound quality testing and evaluation. By contrast, professional artificial head system can produce similar results with human feelings through the simulation of human auditory system, but it is expensive and unable to occupy the driver's seat to record the vehicle sound. Here we design a portable system based on self-adaptive neural network (NN) which can obtain the result of sound quality evaluation similar with the artificial head. The system consists of binaural microphone, signal conditioning units (SCU), and mobile phone. Firstly, sound is perceived by binaural microphone, processed by SCU, and collected by mobile phone. Initial sound quality objective parameters are calculated after noise reduction for sound signals and then the accurate objective parameters can be obtained from the initial parameters through the trained NN model. To train the model, the portable system and the HEAD system are simultaneously used to collect sound sample as the input and output sample sets under different conditions. After the input samples are preprocessed, wavelet entropy eigenvalues based on the best tree wavelet packet analysis are calculated along with psychoacoustic parameters of all the samples. Finally, we obtain the combined feature vectors and successfully train the model. Checked by the practical data, the relative error of sound quality objective parameters produced by the NN model is less than 5% compared with the HEAD system, which conforms to general engineering requirements. This inexpensive and user-friendly portable system paves a new way in the sound quality testing and evaluation that are highly desirable for vehicle acoustical designs and improvements.

Short-term annoyance reduction evoked by active noise control method on turboprop regional aircraft cabin noise

Since the 2000s, the advancing global economy has increased the demand for turboprop regional aircraft, and various studies have been conducted to resolve aircraft noise-related problems. Despite the progress made by previous studies, turboprop regional aircraft still have a poor noise environment compared to other vehicles. In addition, although an application of active noise control (ANC) method had improved the noise environment of the cabin, none of studies were conducted in consideration of a relationship between the application of ANC method and the resulting change of an (short-term) annoyance. Therefore, in the current study, an annoyance reduction evoked by active noise control (ANC) methodology on a turboprop regional aircraft cabin noise was investigated in three experiments. In the first experiment, 50 participants estimated the perception limit of psychoacoustic parameters on aircraft interior noise to identify the cognitive characteristic of the noise. Furthermore, the noise reduction achieved by applying ANC was identified based on the empirical results of the second experiment. Finally, the third experiment examined the relationship between psychoacoustic parameters (loudness and sharpness) and annoyance with the assistance of 50 participants. As a result, the difference between the noise reduction and the annoyance reduction was identified. Furthermore, the degree of annoyance reduction of implementing ANC in cabin noise was identified.

Evaluating Psychoacoustic Parameters and Quality of Transmitted Speech Over Wireless Networks

Psychoacoustic parameter of sound known as loudness is a major quality factor for assessing the perceptual quality of service of speech signals transmitted through telecommunication networks. The Zwicker and Fastl loudness model is a preferred loudness model and in this work has been programmed to obtain both loudness and loudness level of speeches transmitted over wireless. Here,the best maximum instantaneous loudness of the transmitted speeches is 42.55% of that of the original speech. While the best maximum instantaneous loudness level of the transmitted speeches is 87.06% of that of the original speech. These showed an intuitive and innovative representation of the degradation suffered by the transmitted speeches with respect to the original speech.