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Article

The Effects of Noise and Reverberation Time on Auditory Sustained Attention

by
Lei Li
1,2,*,
Yali Liu
2,* and
Ling Li
3
1
School of Information Engineering, Communication University of Shanxi, Jinzhong 030619, China
2
School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
3
School of New Energy Science and Engineering, Xinyu University, Xinyu 338000, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2023, 13(14), 8256; https://doi.org/10.3390/app13148256
Submission received: 13 April 2023 / Revised: 1 July 2023 / Accepted: 14 July 2023 / Published: 16 July 2023
(This article belongs to the Section Acoustics and Vibrations)
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Versions Notes

Abstract

:
The indoor acoustic environment is very important for auditory learning, and the completion of various auditory learning tasks cannot be separated from auditory sustained attention. Noise and reverberation time (RT) are the two most common acoustic indicators that affect attention. Based on this, experiments on auditory sustained attention under two factors of noise masking and reverberation effect alone were conducted in an anechoic chamber and a reverberation chamber, respectively. In addition, nine kinds of RT and signal-to-noise ratios (SNR) (3 RT × 3 SNRs) were also designed and implemented in the paper, and the impact of RT and noise on the perception of auditory sustained attention was analyzed through the above three experimental scenarios. The experimental results show the following: (1) noise type has no significant effect on auditory sustained attention performance, and SNR has a significant effect on omission errors (OEs), commission errors (CEs), and mean reaction time (MRT). All three decline with the increase in SNR; when SNR reaches a certain level, it tends to stabilize, and according to the experimental results, good auditory sustained attention SNR needs at least 18 dB; (2) The effect of RT on auditory sustained attention perception has significant differences. As RT increases, the number of inattentive OE increases, the number of impulses CE increases, and the response time becomes longer; (3) SNR has a significant negative impact on college students’ auditory sustained attention performance, while RT has a less negative effect compared to SNR; (4) The functional relationships between auditory sustained attention performance and SNR and RT in three scenarios were obtained, which can be used to predict inattentive OE, impulse, and MRT in noisy and reverberant environments. This study is the first attempt to explore the influence of noise and RT on the auditory sustained attention of Chinese university students, providing theoretical support for the acoustic design of indoor learning environments, and the findings can fill the gap of the lack of basic experimental data in the current standards of the indoor acoustic environment in China.

1. Introduction

Students spend most of their learning time in the classroom, and the quality of the indoor environment has a significant impact on their learning effectiveness. In recent years, some scholars have investigated and studied the indoor environment of classrooms from the perspectives of air quality [1] and acoustic environment. The quality of the indoor acoustic environment affects students’ learning efficiency. A good classroom acoustic environment is more beneficial for teaching [2], while a poor indoor acoustic environment, such as one with high background noise and long reverberation time, can lead to an additional load on auditory information processing, forcing students to exert more efforts [3], and affecting their attention [4,5], reducing the useful information they can acquire from teacher’s lectures, and especially affecting students’ learning ability [6]. The two most important performance indicators that affect the quality of the indoor acoustic environment are reverberation time and background noise level [7,8]. Reverberation time plays an important role in understanding speeches in a room. Sabine’s equation is the most applied formula in the designing process of room acoustics. Nowoświat et al. used the residual minimization method to develop an appropriate correction to be applied for classrooms [9].
Students need to not only listen to what is being taught clearly in class, but also remember what the teacher said. Auditory patterns and auditory attention are important for learning in many fields. Lasse et al. demonstrated through research that auditory tasks are closely related to classroom acoustic needs [10]. Auditory learning involves more complex processes, such as speech information recognition, storage, and processing, which requires higher indoor acoustic environment support, and further research is needed in this aspect.
Attention, as a condition for the beginning of learning activities, is the basis for the acquisition of abilities, and attention skills are important for academic performance. In a survey of students in grades 11 and 12, it was found that students with better sustained attention skills also performed better in their schools [11]. A study by Commodari also found that different attention levels are significantly related to reading performance [12]. Attention can be divided into three types from the perspective of research content, namely, sustained attention, selective attention, and distributive attention. Sustained attention refers to the state of readiness that individuals maintain to perceive specific, unpredictable, and less frequent signals in each environment, and is also known as vigilance [13], which can be tested by auditory and visual methods. Sustained attention is a fundamental component of attention function and is the basis of higher-level attention (selective attention, distributive attention) and cognitive function. As one of the important indicators to measure the quality of attention, sustained attention is crucial in daily life and work. Without sustained attention, it is difficult to complete various practical tasks [14]. Accordingly, it is necessary to research the effects of indoor acoustic environment on auditory attention.
The continuous performance task (CPT) first appeared when Rosvold [15] et al. demonstrated the sensitivity of the CPT to brain damage, and it has since been used frequently in clinical and research environments [16]. It is one of the most used tools for assisting in assessing attention, and its results are objective and less prone to subjective influence. The CPT has two modes of testing: visual and auditory. The subject is given stimuli in the visual or auditory channel and is required to respond to target stimuli, without responding to the stimuli that do not meet the requirement (Go/No-Go task). The CPT can intuitively and accurately test the subject’s ability to maintain attention, impulsivity, and alertness. Traditional performance indicators include correct responses, omission errors (OEs), commission errors (CEs), and reaction time (RT). A response to the designated target is referred to as a “correct response”, a failure to respond to the target is referred to as an “omission error”, and a response to the non-target is referred to as a “commission error”, or false alarm. Omission errors (OEs) reflect inattention, while commission errors reflect impulsivity [17,18]. Reaction time is the time recorded in which the subject respond when hearing the target stimulus and reflects the subject’s cognitive processing speed [19]. Some researchers have used measurements derived from signal detection theory [20]. In addition, many studies have confirmed the relationship between omission errors and ratings of inattention [21], as well as the relationship between commission errors and ratings of impulsivity [22]. Currently, most of the CPT tests in China are used for school-age children over 6 years old, most auditory stimuli use numbers, while most visual stimuli use letters. In the past 20 years, several versions of the CPT have been used in the test and evaluation of sustained attention in foreign countries.
Roebuck et al. [23] explored the influence of visual letters on spoken tone words and auditory pure tones on sustained attention in children and adults. It was found that the stimulus type did not lead to significant changes in adults’ total errors, and the auditory stimuli corresponded to the slowest reaction times. Adults had fewer inattention and impulsive errors than the children group, and they had a shorter MRT than children. Simões et al. [24] selected 160 primary and high school students (43 with ADHD, 117 controls) to carry out a sustained attention test to explore which CPT parameters (auditory and visual modalities) should be used as reliable variables to distinguish ADHD children from healthy controls and to describe the usefulness of visual and auditory CPTs as tools for distinguishing ADHD students from healthy controls. Five variables were extracted for each sensory modality: commission errors and omission errors, reaction time, reaction time variability, and coefficient of variation. The results showed that the ADHD group displayed higher rates for all test variables. Auditory parameters in the attention domain (OE and VRT) can discriminate ADHD from control groups. Both modalities are equally important for the hyperactive/impulsive domain (CE). Mahone et al. [25] studied the construct validity of the ACPT-P as a measure of EF in children with ADHD. The ACPT-P test was conducted on 40 preschool children with ADHD and 40 age- and gender-matched controls. Two auditory stimuli (dog bark and church bell) were used as the target and non-target, respectively, each with a duration of 690 milliseconds and a fixed interval of 5000 milliseconds between stimuli. Fifteen targets and fifteen non-targets were randomly arranged, and the total test time was about 3 min. The stimuli were presented through headphones. Four dependent variables were selected: omission errors, commission errors, mean response time, and variability. The results showed that the performance of the ADHD group on omissions, mean response time, and variability was significantly lower than that of the control group. Maria Renata José et al. [26] conducted an auditory sustained attention experiment on adults using the SAAAT to investigate subjects’ performance in listening to words over some time. Compared with previous tests on children, the adult group had fewer omission errors and had better auditory sustained attention.
To accurately understand children’s inadaptability to the acoustical environment of the school classroom, it is important to assess children’s attention stability in noisy environments from a clinical perspective. However, most traditional CPTs are conducted in a controlled environment without noise, and the results may not reflect the true situation of children in school. Uno et al. [27] studied the use of CPTs in diagnosing ADHD and the response of ADHD children to noise. Among them, eight sine waves of different frequencies in the range of 440–880 Hz were selected for auditory distraction stimulation. The experiment showed that significant differences were observed in all measurements between the control and ADHD groups, except for mean reaction time, indicating that it is useful as a supplementary diagnosis method for ADHD. Compared to noiseless conditions, both commission and omission errors significantly increased in auditory and visual noise conditions. Noise reduced the OE of ADHD children compared to the control children, and background noise improved the auditory attention performance of ADHD children.
The following can be seen from the literature review: (1) There is a close relationship between the factors in the indoor acoustic environment, especially noise level, reverberation time, and students’ auditory sustained attention performance; (2) Most previous studies of auditory sustained attention focused on the problem of attention deficit, using two types of stimuli: auditory speech, such as speech sounds, music, or words; and nonverbal stimuli, such as pure tones. Due to differences in experimental design, such as stimulus type, frequency of the stimulus, duration of the experiment, and proportion of target stimuli, etc., as well as subject specificity in external factors (gender, provision of reward, etc.) and internal factors (implementation of task parameters), the conclusions obtained from these studies are inconsistent; (3) Most studies have mainly discussed children’s performance on tests of sustained attention and less frequently involve adult groups; however, learning does not only occur in childhood, but throughout life, we are constantly learning to acquire new skills, and the learning process requires attention and memory [28]; (4) There are few reports on the influence of different environmental factors on auditory sustained attention characteristics in daily life, and there is currently no research on the comprehensive effects of the indoor acoustic environment on the auditory sustained attention of Chinese college students. Therefore, it is necessary to analyze the mechanism of the impact of acoustic factors, such as noise and reverberation, on auditory sustained attention in adults.
In this paper, an auditory-channel attention-perception experiment in noisy and reverberation environments is designed to explore the influence of different types of noise, different noise intensities, and RT on the auditory sustained attention of Chinese college students. Currently, the standard specification for the classroom acoustic environment in China is a group standard [29]; however, the formulation of this standard does not consider whether auditory learning has different requirements for classroom acoustic environments, or whether it can support students in second-language learning and English listening tests. As a preliminary exploration, this paper provides the theoretical basis and basic data support for the improvement of classroom acoustic environmental standards in China.

2. Experimental Design

2.1. Auditory Continuous Performance Test

The experiment uses the classic paradigm of sustained attention testing—the Auditory Continuous Performance Test (ACPT) [30]—which has been used in many previous studies to test subjects’ sustained attention (maintaining attention for a certain amount of time) and alertness (ability to pay attention to and respond to random items). The ACPT uses auditory stimuli and requires the participant to continuously respond to target signals during the experiment, such as pressing keys with a finger or making certain gestures, while not responding to undesired stimuli. In this experiment, the paradigm was adjusted and changed according to the characteristics of the subjects and research needs.
The experimental dependent variables mainly include omission errors, commission errors, the total number of errors (E), and mean reaction time (MRT).

2.2. Experiment 1: ACPT in a Noisy Environment

1.
Experimental environment
The experimental scenario was selected to be conducted in an anechoic chamber, as shown in Figure 1. The fully anechoic chamber has an area of 190 m2, a volume of 1300 m3, a background noise below N1, and a lower limit frequency of 50 Hz, which meets the requirements of the international standard ISO3745 for anechoic chambers.
A total of 5 speakers are needed in the experiment, the speakers are placed in a 5-channel surround sound system. The listening point where the subject is located is regarded as the center of the circle. Each speaker is 2 m away from the listening point, according to the angle regulations, the speaker is placed on the circumference, and the radiation center of the speaker is 1.5 m away from the ground. The middle speaker plays speech signals, and the remaining four speakers play noise signals. The speaker placement is shown in Figure 2. The experimental signal playback is controlled by a computer and equipped with a multichannel sound card connected to a 16-channel digital mixer with a frequency response range of 20 Hz to 20 kHz.
2.
Noise type and SNR
Two typical types of noise, namely pink noise and speech noise, were selected for this experiment. Pink noise is the most common noise in nature, and its energy decays continuously from low to high frequencies, dropping 3 dB per octave in linear coordinates, which is the standard signal in measurement. Speech noise is one of the main sources of background noise in the classroom. Four girls and four boys recorded meaningful Chinese sentences in the recording room and then mixed them to form speech noise, thus simulating the noise environment in a normal classroom. The sound pressure level at the listening point was set to 65 dBA. Referring to a previous study [31], the signal-to-noise ratio was selected for 7 cases: −5 dB, 0 dB, 5 dB, 10 dB, 15 dB, 18 dB, and no noise. The same SNR was set for both noise interference experiments, with a total of 13 SNR conditions.
3.
Subjects
A total of thirty Chinese college students (age: 22.4 ± 0.9 years) participated in the experiment; they were able to speak standard Mandarin Chinese and had no cognitive impairment and normal hearing. All subjects received listening training.
4.
Procedure
Before the formal test, subjects needed to practice in order to make the subjects familiar with the test software and the experimental procedure. The experiment was conducted using E-prime3.0 software, in which a computer was connected to a speaker to play the speech signals. The participants were presented with numbers from 1 to 50 in a random order through audio, and target stimuli were numbers with a unit digit containing 7, such as 7, 17, 27, 37, and 47, while the remaining numbers were distractor stimuli. Subjects were required to click the computer space bar when they heard the stimuli that met the requirements and to not respond if they did not meet the requirements. The digital signals were played randomly, with each digit lasting 600 ms with 1000 ms intervals in between. The total stimulus had 450 characters, and the target stimuli accounted for 20%. When the experiment was complete, the screen would display “Experiment completed, thank you for your participation”. The experiment was conducted on one person at a time, and the duration of each test was approximately 12 min. In order to prevent the effects of practice and subject fatigue on the reliability of experimental results, the tests between each acoustic transmission condition needed to be separated by 2 weeks.

2.3. Experiment 2: ACPT in a Reverberant Environment

1.
Experimental environment
The experiment was conducted in an anechoic chamber and a reverberation chamber, with an area of 40 m2, a volume of 200 m3, and a background noise of below NR25. The reverberation chamber is shown in Figure 3.
2.
Setting of RT
By adding the number of sound absorbers, and by changing the position and placement of absorbers in the reverberation room, a uniform reverberation environment with reverberation times of 1 s, 2 s, 3 s, and 7 s was obtained, with RT taking the average of the intermediate frequencies of 500 Hz and 1000 Hz. In addition, the 0 s of the anechoic chamber was added, making a total of 5 experimental scenarios.
3.
Subjects
Twenty-eight Chinese college students (age: 21.2 ± 1.1 years) were recruited in the experiment, and the subjects had no cognitive impairment and normal hearing. The audiometer was used to test the hearing loss of the subjects, the average pure tone threshold of 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz was less than 25 dB HL in the case of the air-conduction test. All subjects had received listening training.
4.
Procedure
Subjects were required to practice before the formal test, and the experimental test was conducted in the same way as experiment 1; a computer was connected to a speaker to play the voice signals at 2 m from the subject. Each subject was required to complete five different RTs of the tests. In order to prevent the effects of practice and subject fatigue on the reliability of experimental results, the tests between different reverberation times needed to be separated by 2 weeks.

2.4. Experiment 3: ACPT in a Noisy and Reverberant Environment

1.
Experimental environment
The test was conducted in a classroom with a length of 10 m, a width of 7.8 m, and a height of 4 m, which is a small-sized classroom. The experimental classroom is shown in Figure 4.
2.
Setting of acoustic scenes
Referring to the results of experiments 1 and 2, three RTs (0.34 s, 0.76 s, and 1.80 s) and three SNRs (−5 dB, 5 dB, and 15 dB) representing low, medium, and high signal-to-noise ratios in order, were selected for a total of 3 × 3 = 9 acoustic transmission conditions.
The experimental corpus was played using a directional sound source like the human voice. The sound source was placed in the middle of the classroom podium, and its radiation center was 1.5 m above the ground, and a speaker was placed at each of the four corners to play the noise. The subjects sat in the second row near the middle of the classroom. The experimental equipment also required a multichannel sound card and a 16-channel digital mixer.
3.
Subjects
Twenty-eight Chinese college students participated in the test, aged 21.9 ± 0.8, without cognitive impairment and normal hearing. All subjects received listening training.
4.
Procedure
Subjects were required to perform a pre-experiment before the formal test. Each subject was required to complete tests under 9 different acoustic conditions, with an interval of 2 weeks between the different experimental conditions in order to prevent the effects of practice and subject fatigue on the reliability of the experimental results.

3. Results

3.1. Impact of Noise on Auditory Sustained Attention

After the data were processed, OE, CE, and MRT were calculated in both pink and speech-noise environments, and all subjects’ experimental data met the 2-fold standard-deviation intra-group consistency reliability test.
Figure 5 shows the influence of noise types on auditory sustained attention. The results show that the curves of OE, CE, and MRT change in the same trend when auditory sustained attention is disturbed by pink and speech noise, that is, they all decrease as the SNR increases. Interference noise can cause irrelevant sound effects, which can distract people’s attention and cause emotional tension. In this study, it is possible that under low SNR conditions, noise influenced the detection or recognition of target stimuli, resulting in longer reaction times and an increase in both OE and CE. Significance analysis of OE, CE, and MRT in different noise environments showed that the differences between OE (p = 0.480), CE (p = 0.416), and MRT (p = 0.691), corresponding to pairwise noises, were not significant. The impact of the signal-to-noise ratio on auditory sustained attention was significant for OE (p = 0.000), CE (p = 0.000), and MRT (p = 0.000). To further determine the data differences between different SNR conditions, post-hoc pairwise comparisons were made using Bonferroni correction, and for the number of OE, except for the non-significant differences between 5 and 10 dB, 10 and 15 dB, 15 and 18 dB of SNR, there were statistical differences between the other two SNR conditions. When comparing SNR = −5 dB and SNR = 5 dB, SNR = 10 dB, SNR = 15 dB, and SNR = 18 dB pairwise, there were significant differences in CE, with Bonferroni p-values all less than 0.002. There were significant differences in MRT when the SNR was between −5 dB and 15 dB and −5 dB and 18 dB (Bonferroni p = 0.000), and there was also a difference between 0 dB and 18 dB.
In order to further investigate the characteristics of auditory attention perception in noisy environments, the perceptual data under different SNR conditions were analyzed separately. As the differences in ACPT performance under different noise conditions were not significant, the scatter plots of OE, CE, and MRT versus SNR were obtained by taking the mean values of the test results under two noise interference conditions, and then fitted using the least-squares fitting method, the perceptual characteristic curve corresponding to noise interference is obtained as shown in Figure 6.
As can be seen from Figure 6, OE and CE show a logarithmic decrease with the increase in SNR, while MRT decreases exponentially with the increase in SNR. When the SNR is small, the three decrease faster, and the declining trend slows down significantly when the SNR increases to a certain extent. OE and CE are close to the value in the noiseless condition at SNR = 18 dB, and MRT is close to the value in the noiseless condition at SNR = 15 dB. The reason may be that when the SNR reaches a certain threshold, the participants’ auditory attention is focused on specific stimuli, while actively suppressing environmental interference, such as noise.
The fitting formulas are shown in Equations (1)–(3):
OE = 14.857 − 3.171ln (SNR + 6), R2 = 0.96,
CE = 5.519 − 0.851ln (SNR + 6), R2 = 0.91,
MRT = 835.09 − 15.71ln (SNR + 6), R2 = 0.93,
where SNR represents the signal-to-noise ratio, R2 is the coefficient of determination. When only noise interference exists, the above-fitted curves can be used directly to predict auditory sustained attention perception based on the actual measured values.

3.2. Effects of RT on Auditory Sustained Attention

The OE, CE, total errors (E), and MRT of the subjects under different RT conditions were obtained statistically. The data were tested for reliability and validity and met the requirements. The ANOVA test was performed on the data under different RT scenarios, and it was found that reverberation time significantly affected the perception of OE (p = 0.000), CE (p = 0.000), E (p = 0.000), and MRT (p = 0.000). The impact of different RT on ACPT is shown in Figure 7.
The OE at 0 s and 1 s of RT (mean = 5.0 and 6.9) are significantly lower than the OE at RT = 3 s (mean = 10.0). There is a significant difference in CE between RT = 0 s, 1 s, and 2 s (mean = 3.1, 3.0, and 3.3). The MRT at RT = 0 s (mean = 780.1) is significantly lower than that at RT = 3 s and (mean = 836.0 and 850.5).
Figure 8 shows the perceptual characteristic curves of OE, CE, E, and MRT under different RT. As can be seen from Figure 8, OE, CE, E, and MRT all increase with the increase in RT. Within the range of 0 s to 3 s of RT, OE increases significantly, and OE still holds an increasing trend within 3 s to 7 s, but the increase becomes significantly smaller. CE has a very small range of slow growth within 0 s to 2 s, and CE corresponding to 1 s of RT is the smallest, and it starts to grow rapidly from 2 s to 7 s. The MRT of the subjects fluctuated greatly in the range of RT from 0 s to 1 s and from 2 s to 3 s, increased slowly from 3 s to 7 s, and then stabilized. It was analyzed that for every 1 s decrease in RT, the reaction time of auditory sustained attention could decrease by about 2%. The above results indicate that the masking effect of reverberation can affect human perception of speech and reduce perceptual performance. As shown in Figure 8d, the scatter points of error perception values are all distributed near the center line, indicating that the perception has high precision on OE, CE, and E in the reverberant environment.
The fitting formulas are shown in Equations (4)–(7):
OE = 6.382 + 2.429ln (RT + 0.5), R2 = 0.97,
CE = 2.917e 0.107RT, R2 = 0.92,
E = 9.546 + 3.502ln (RT + 0.5), R2 = 0.91,
MRT = 917.817 + 31.399ln (RT + 0.5), R2 = 0.90,
where E is the total error, and R2 is the coefficient of determination. For the case where only reverberant interference is present, the auditory sustained attention-perceptual values can be predicted directly from the above-fitted curves based on the actual measured values.

3.3. Effects of Noise and Reverberation on Auditory Sustained Attention

The reliability test was completed for all subjects’ data, and the Cronbach alpha coefficient was measured to be 0.865, indicating that the internal reliability of the experiment was good. A two-way repeated ANOVA was conducted with the total number of errors E as the dependent variable and SNR and RT as the within-subjects variables; the data were approximately normally distributed, following Mauchly’s sphericity test (p > 0.05). The results showed that SNR (F = 35.846, p = 0.000) and RT (F = 9.473, p = 0.001) had a significant effect on E, but there was no interaction between SNR and RT, the single factor F value SNR > RT, indicating that the effect of SNR was greater than that of RT.
The total errors for different SNR and RT scenarios were analyzed by pairwise comparison, and it was found that E was significantly different between SNR = −5 dB, 5 dB, and 15 dB. The total number of errors at RT = 0.34 s (13.0) was significantly lower than RT = 1.8 s (16.1), The difference in perception data between 0.34 s and 0.76 s and between 0.76 s and 1.8 s of RT was not obvious.
After analysis of variance, the difference in the total errors between different SNRs for each RT was significant (p = 0.000). A significant difference in the total errors between different RT was found for SNR = −5 dB (p = 0.031), and when SNR = 5 dB and 15 dB, no significant difference was found in the total errors between different RT (p = 0.145 and p = 0.225). Under a large SNR, when RT is varied within a suitable range, speech intelligibility value remains high [32], which may be the reason why RT has almost no effect on the number of errors of participants under a large SNR.
The perceptual relationship between the total number of errors, SNR, and RT was obtained through the listening experiments, and Figure 9 shows the variation of E with both in a two-dimensional distribution.
From the above figure, when both reverberation and noise factors exist simultaneously, the characteristics of E affected by both factors are the same as those of E affected by a single factor. Under the same SNR, the larger RT corresponds to a higher total number of errors; when RT is the same, the higher SNR corresponds to a lower total number of errors. Compared with the perceptual features of auditory sustained attention under the influence of a single factor, adding one interference condition resulted in more total errors for the subjects.
Based on the functional relationship between E, SNR, and RT under the influence of a single interference factor, the perception model is established when the two factors act together, and multiple regression analysis is used to fit SNR and RT to obtain the functional relationship of E. The fitting formula is shown in Equation (8):
E = 21.985 − 3.867ln (SNR + 6) + 1.815lnRT, R2 = 0.93,
where E represents the total errors, SNR represents the signal-to-noise ratio, and R2 is the coefficient of determination.
Figure 10 shows the fitting precision.
The scatter points in Figure 10 are distributed near the central line, indicating a high fitting accuracy.

4. Discussion

4.1. The Effect of Noise Type

Although pink noise and speech noise have different frequency domain characteristics and spectral features, the differences between OE, CE, and MRT corresponding to different types of noise were not significant, that is, noise type was not significantly related to the inattention and impulsivity of college students. Sun found insignificant difference in the CE of auditory sustained attention between ASD children and control children under different types of background sound conditions, and insignificant differences in OE and MRT correspond to ambient sound (running water) and speech sounds in control children [33], which is like the noise perception characteristics obtained in the paper.
When the SNR is low, the processing speed of the subjects slows down, and there are more cases of not paying attention, resulting in an increase in OE. Compared with the condition without noise, OE increased by up to three times, CE increased by one time, and MRT increased by up to 7%, which indicates that noise has a more significant negative impact on college students’ auditory sustained attention. Liu et al. found that the higher the SNR, the longer the attention stabilization time for both non-disabled children and children with intellectual disabilities [34], which is like our research results. Although the test methods, distraction stimuli, and subjects of the above study are different from this experiment, the findings all indicated that environmental sounds and noise interfere with auditory sustained attention.
In the absence of noise, the mean = 3.6, SD = 1.28 for OE and mean = 2.4, SD = 1.02 for CE, which are lower than the results obtained in the study of Chinese children [33]. A study applying the SAAAT [26] to adults in a noiseless environment found that compared with children, the number of different types of errors in adults decreased, which is consistent with our findings. This suggests that children’s auditory sustained attention ability may continue to develop and improve with age and reach stability in adulthood. Carriere et al. [35] predicted that adults would have higher accuracy and faster response time in CPT tasks than children. The results of this study confirm the hypothesis of Carrie et al. to some extent. Adults performed better because they had lower levels of inattention and impulsive errors.

4.2. The Influence of Noise and Reverberation

SNR had a significant impact on college students’ auditory sustained attention under three RT conditions, while RT only had an impact when SNR = −5 dB, indicating that subjects’ auditory attention was more sensitive to changes in SNR when the two interference factors acted simultaneously. Controlling SNR may be better than changing RT in improving the indoor acoustic environment. Bradley et al. studied the influence of indoor acoustic variables on intelligibility and found a more critical effect of SNR [36], as well as Peng et al. [37], who found that SNR had a much greater impact on speech comprehension performance than RT. These are like the experimental results in the paper.
This experiment selected a consistent sample of college students, without considering the impact of educational level, and more adult participants with different education levels will be selected in future experiments. In future research, the influence of other interference factors on auditory sustained attention will be considered, and the objective evaluation model of auditory sustained attention will be improved.

5. Conclusions

In this paper, we designed and carried out auditory sustained attention experiments in a single-factor noise environment, a reverberant environment, and two factors of noise reverberation interference; obtained the rule of noise and reverberation time affecting auditory sustained attention; and established a perceptual prediction model. The experimental results can provide a theoretical basis for the design of indoor learning environments and basic data support for the establishment of classroom acoustic standards. The main conclusions are as follows:
(1)
The impact of the noise type on auditory sustained attention perception is not significant. There was a significant difference in the influence of SNR on auditory sustained attention. In both noise conditions, OE and CE show a logarithmic decrease trend and tend to stabilize as SNRs increase, and MRT shows an exponential decay trend as SNRs increase and becomes stable when SNRs increase to a certain extent. The functional relationship between OE, CE, and MRT and SNR was obtained, and this model can be used to estimate the perceptual results when there is only noise interference. In order to achieve the best performance of sustained auditory attention of college students, the suggested SNR is greater than or equal to 18 dB;
(2)
In the reverberant environment, OE and MRT show a logarithmic growth trend as the RT increases, while CE shows an exponential growth trend, when there is only reverberation time interference, the obtained prediction model can be used to estimate auditory sustained attention performance. RT significantly affects the perception of OE, CE, and MRT;
(3)
When both noise and reverberation interfere, the SNR significantly impacts auditory sustained attention perception under all RT transmission conditions, and the smaller the SNR, the greater the negative effect. Compared with SNR, RT has a much smaller impact on auditory sustained attention perception, with a significant effect only at SNR = −5 dB. Compared to reverberation time, college students’ auditory sustained attention performance is more sensitive to changes in noise. In the design of indoor acoustic environment, it is necessary to set a reasonable RT, and a higher SNR is important for better auditory attention performance of the subjects.

Author Contributions

Conceptualization, L.L. (Lei Li) and Y.L.; methodology, L.L. (Lei Li), Y.L., and L.L. (Ling Li); software, L.L. (Lei Li); validation, L.L. (Lei Li); formal analysis, L.L. (Lei Li); investigation, L.L. (Lei Li), Y.L. and L.L. (Ling Li); resources, L.L. (Lei Li); data curation, L.L. (Lei Li) and Y.L.; writing—original draft preparation, L.L. (Lei Li); writing—review and editing, L.L. (Lei Li) and Y.L.; visualization, L.L. (Lei Li) and L.L. (Ling Li); supervision, Y.L.; project administration, L.L. (Lei Li); funding acquisition, L.L. (Ling Li). All authors have read and agreed to the published version of the manuscript.

Funding

This work has been supported by the Research Fund for Teaching Reform in Higher Education of Jiangxi Province of China (No. JXJG222023).

Institutional Review Board Statement

The research was approved by the Research Ethics Committee of Communication University of China.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data for this study are available from the corresponding author on request.

Acknowledgments

The authors would like to thank all the college students who participated in the experiment and the reviewers and editors for their reviews of this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Anechoic chamber environment.
Figure 1. Anechoic chamber environment.
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Figure 2. Speaker placement.
Figure 2. Speaker placement.
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Figure 3. Reverberation room environment.
Figure 3. Reverberation room environment.
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Figure 4. Experimental scene.
Figure 4. Experimental scene.
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Figure 5. Influence of noise type on ACPT: (a) OE and CE; (b) MRT.
Figure 5. Influence of noise type on ACPT: (a) OE and CE; (b) MRT.
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Figure 6. Perception of ACPT in noise environment: (a) OE and CE; (b) MRT.
Figure 6. Perception of ACPT in noise environment: (a) OE and CE; (b) MRT.
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Figure 7. Comparison of ACPT performance under different RT: (a) OE; (b) CE; (c) MRT. (Error bars means Mean ± SE; * means p < 0.05; ** means p < 0.01).
Figure 7. Comparison of ACPT performance under different RT: (a) OE; (b) CE; (c) MRT. (Error bars means Mean ± SE; * means p < 0.05; ** means p < 0.01).
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Figure 8. Characteristics of auditory continuous attention perception in reverberation environment. (a) OE and CE; (b) E; (c) MRT; (d) the fitting effect of OE, CE, and E.
Figure 8. Characteristics of auditory continuous attention perception in reverberation environment. (a) OE and CE; (b) E; (c) MRT; (d) the fitting effect of OE, CE, and E.
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Figure 9. Two-dimensional distribution of error number when noise and reverberation work together.
Figure 9. Two-dimensional distribution of error number when noise and reverberation work together.
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Figure 10. Scatter diagram of measured and predicted number of total errors.
Figure 10. Scatter diagram of measured and predicted number of total errors.
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Li, L.; Liu, Y.; Li, L. The Effects of Noise and Reverberation Time on Auditory Sustained Attention. Appl. Sci. 2023, 13, 8256. https://doi.org/10.3390/app13148256

AMA Style

Li L, Liu Y, Li L. The Effects of Noise and Reverberation Time on Auditory Sustained Attention. Applied Sciences. 2023; 13(14):8256. https://doi.org/10.3390/app13148256

Chicago/Turabian Style

Li, Lei, Yali Liu, and Ling Li. 2023. "The Effects of Noise and Reverberation Time on Auditory Sustained Attention" Applied Sciences 13, no. 14: 8256. https://doi.org/10.3390/app13148256

APA Style

Li, L., Liu, Y., & Li, L. (2023). The Effects of Noise and Reverberation Time on Auditory Sustained Attention. Applied Sciences, 13(14), 8256. https://doi.org/10.3390/app13148256

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