Effects of meaningful or meaningless noise on psychological impression for annoyance and selective attention to stimuli during intellectual task
Takahiro Tamesue – email@example.com
1677-1 Yoshida, Yamaguchi
Yamaguchi Prefecture 753-8511
Popular version of poster 4aPPa24, “Effects of meaningful or meaningless noise on psychological impression for annoyance and selective attention to stimuli during intellectual task”
Presented Thursday morning, December 1, 2016
172nd ASA Meeting, Honolulu
Open offices that make effective use of limited space and encourage dialogue, interaction, and collaboration among employees are becoming increasingly common. However, productive work-related conversation might actually decrease the performance of other employees within earshot — more so than other random, meaningless noises. When carrying out intellectual activities involving memory or arithmetic tasks, it is a common experience for noise to cause an increased psychological impression of “annoyance,” leading to a decline in performance. This is more apparent for meaningful noise, such as conversation, than it is for other random, meaningless noise. In this study, the impact of meaningless and meaningful noises on selective attention and cognitive performance in volunteers, as well as the degree of subjective annoyance of those noises, were investigated through physiological and psychological experiments.
The experiments were based on the so-called “odd-ball” paradigm — a test used to examine selective attention and information processing ability. In the odd-ball paradigm, subjects detect and count rare target events embedded in a series of repetitive events. To complete the odd-ball task it is necessary to regulate attention to a stimulus. In one trial, subjects had to count the number of times the infrequent target sounds occurred under meaningless or meaningful noises over a 10 minute period. The infrequent sound — appearing 20% of the time—was a 2 kHz tone burst; the frequent sound was a 1 kHz tone burst. In a visual odd-ball test, subjects observed pictures flashing on a PC monitor as meaningless or meaningful sounds were played to both ears through headphones. The most infrequent image was 10 x 10 centimeter-squared red image; the most frequent was a green square. At the end of the trial, the subjects also rated their level of annoyance at each sound on a seven-point scale.
During the experiments, the subjects brain waves were measured through electrodes placed on their scalp. In particular, we look at what is called, “event-related potentials,” very small voltages generated in the brain structures in response to specific events or stimuli that generate electroencephalograph waveforms. Example results, after appropriate averaging, of wave forms of event-related potentials under no external noise are shown in Figure 1. The so-called N100 component peaks negatively about 100 milliseconds after the stimulus and the P300 component positive peaks positively around 300 milliseconds after a stimulus, related to selective attention and working memory. Figure 2 and 3 show the results of event-related potentials for infrequent sound under the meaningless and meaningful noise. N100 and P300 components are smaller in amplitude and longer in latency because of the meaningful noise compared to the meaningless noise.
Figure 1. Averaged wave forms of evoked Event-related potentials for infrequent sound under no external noise.
Figure 2. Averaged wave forms of evoked Event-related potentials for infrequent sound under meaningless noise.
Figure 3. Averaged wave forms of auditory evoked Event-related potentials under meaningful noise.
We employed a statistical method called, “principal component analysis” to identify the latent components. Results of statistical analysis, where four principal components were extracted as shown in Figure 4. Considering the results, where component scores of meaningful noise was smaller than other noise conditions, meaningful noise reduces the component of event-related potentials. Thus, selective attention to cognitive tasks was influenced by the degree of meaningfulness of the noise.
Figure 4. Loadings of principal component analysis
Figure 5 shows the results for annoyance in the auditory odd-ball paradigms. These results demonstrated that the subjective experience of annoyance in response to noise increased due to the meaningfulness of the noise. The results revealed that whether the noise is meaningless or meaningful had a strong influence not only on the selective attention to auditory stimuli in cognitive tasks, but also the subjective experience of annoyance.
Figure 5. Subjective experience of annoyance (Auditory odd-ball paradigms)
That means that when designing sound environments in spaces used for cognitive tasks, such as the workplace or schools, it is appropriate to consider not only the sound level, but also meaningfulness of the noise that is likely to be present. Surrounding conversations often disturb the business operations conducted in such open offices. Because it is difficult to soundproof an open office, a way to mask meaningful speech with some other sound would be of great benefit for achieving a comfortable sound environment.
How virtual reality technologies can enable better soundscape design.
W.M. To – firstname.lastname@example.org
Macao Polytechnic Institute, Macao SAR, China.
A. Chung – email@example.com
Smart City Maker, Denmark.
B. Schulte-Fortkamp – firstname.lastname@example.org
Technische Universität Berlin, Berlin, Germany.
Popular version of paper 2aNS, “How virtual reality technologies can enable better soundscape design”
Presented Tuesday morning, November 29, 2016
172nd ASA Meeting, Honolulu
The quality of life including good sound quality has been sought by community members as part of the smart city initiative. While many governments have placed special attention to waste management, air and water pollution, acoustic environment in cities has been directed toward the control of noise, in particular, transportation noise. Governments that care about the tranquility in cities rely primarily on setting the so-called acceptable noise levels i.e. just quantities for compliance and improvement . Sound quality is most often ignored. Recently, the International Organization for Standardization (ISO) released the standard on soundscape . However, sound quality is a subjective matter and depends heavily on the perception of humans in different contexts . For example, China’s public parks are well known to be rather noisy in the morning due to the activities of boisterous amateur musicians and dancers – many of them are retirees and housewives – or “Da Ma” . These activities would cause numerous complaints if they would happen in other parts of the world, but in China it is part of everyday life.
According to the ISO soundscape guideline, people can use sound walks, questionnaire surveys, and even lab tests to determine sound quality during a soundscape design process . With the advance of virtual reality technologies, we believe that the current technology enables us to create an application that immerses designers and stakeholders in the community to perceive and compare changes in sound quality and to provide feedback on different soundscape designs. An app has been developed specifically for this purpose. Figure 1 shows a simulated environment in which a student or visitor arrives the school’s campus, walks through the lawn, passes a multifunctional court, and get into an open area with table tennis tables. She or he can experience different ambient sounds and can click an object to increase or decrease the volume of sound from that object. After hearing sounds at different locations from different sources, the person can evaluate the level of acoustic comfort at each location and express their feelings toward overall soundscape. She or he can rate the sonic environment based on its degree of perceived loudness and its level of pleasantness using a 5-point scale from 1 = ‘heard nothing/not at all pleasant’ to 5 = ‘very loud/pleasant’. Besides, she or he shall describe the acoustic environment and soundscape using free words because of the multi-dimensional nature of sonic environment.
Figure 1. A simulated soundwalk in a school campus.
To, W. M., Mak, C. M., and Chung, W. L.. Are the noise levels acceptable in a built environment like Hong Kong? Noise and Health, 2015. 17(79): 429-439.
ISO. ISO 12913-1:2014 Acoustics – Soundscape – Part 1: Definition and Conceptual Framework, Geneva: International Organization for Standardization, 2014.
Kang, J. and Schulte-Fortkamp, B. (Eds.). Soundscape and the Built Environment, CRC Press, 2016.
School of Mechanical and Aerospace Eng., Seoul National University
301-1214, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
Popular version of paper 4aEA1, “Integrated simulation model for prediction of acoustic environment of launch vehicle”
Presented Thursday morning, December 1, 2016
172nd ASA Meeting, Honolulu
Literally speaking, a “sound” refers to a pressure fluctuation of the air. This means, for example, the sound of a bus passing means our ear senses the pressure fluctuation or pressure variation the bus created. During our daily lives, there are rarely significant pressure fluctuations in the air above common noises, but in special cases it happens. Windows are commonly featured in movies breaking from someone screaming loudly or in high pitches in the movie. This is usually exaggerated, but not out of the realm of what is physically possible.
The pressure fluctuations in the air caused by sound can cause engineering problems for loud structures such as rockets, especially given that the pressure nature of the sounds waves that means louder sounds result from larger pressure fluctuations and can cause more damage. Rocket launches are particularly loud and the resulting pressure change in the air can affect the surface of the launched vehicle as the form of the force shown as Figure 1.
Figure 1. The Magnitude of Acoustic Loads on the Launch Vehicle
As the vehicle is launched (Figure. 2),it reaches volumes over 180dB, which corresponds to about 20,000 Pascals in pressure change. This pressure change is about 20% of atmospheric pressure, which is considered very large. Because of the pressure change during launching, communication equipment and antenna panel can incur damage, causing the malfunctioning of the fairing, the protective cone covering the satellite. In the engineering field, the load created by the launching noise is called acoustic load, and many studies are in progress related to acoustic load.
Studies focused on the relationship between a launching vehicle and its acoustic load is categorized, to rocket engineers, under “prediction and control.” Prediction is divided into two aspects: internal acoustic load; and external acoustic load. Internal acoustic load refers to sound delivered from outside to inside, while external acoustic load is the noise directly from the jet fire. There are two ways to predict the external acoustic load, namely an empirical method and numerical method. The empirical method was developed by NASA in 1972 and uses the collected information from various studies. The numerical method employs mathematical formulas related to noise and electric wave calculated using computer modeling. As computers become more powerful, this method continues to gain favor. However, because numerical methods require so much calculation time, they often require the use of dedicated computing centers. Our team instead focused on using the more efficient and faster empirical method.
Figure 3 shows the results of our calculations, depicting the expected sound spectrum. We can consider various physics principles involved during a lift-off, such as sound reflection, diffraction and impingement that could affect the original empirical method results.
Meanwhile, our team used a statistical energy analysis method to predict the internal acoustic load caused by the predicted external acoustic load. This method is used often to predict internal noise environments. It is used to predict the internal noise of a launching vehicle as well as aircraft and automobile noise. Our research team used a program called, VA One SEA, for predicting these noise effects, shown as figure. 4.
Figure 4. Modeling of the Payloads and Forcing of the External Acoustic Loads
After predicting internal acoustic load, we decreased the acoustic load to conduct an internal noise control study. A common way to do this is by sticking noise-reducing material to the structure. However, the extra weight from the noise-reducing material can cause decreased performance. To overcome this side effect, we also conducted a study about active noise control, which is in progress. Active noise control refers to reducing the noise by making antiphase waves of the sound for cancelling. Figure 5 shows the experimental results of applied SISO Noise Control, showing the reduction of noise is significant, especially for low frequencies.
Figure 5. Experimental Results of SISO Active Noise Control
Our research team applied the acoustic load prediction method and control method to the Korean launching vehicle, KSR-111. Through this application, we developed an improved empirical prediction method that is more accurate than previous methods, and we found usefulness of the noise control as we established the best algorithm for our experimental facilities and the active noise control area.
Popular version of poster 5aMU1
Presented Friday morning, May 22, 2015, 8:35 AM – 8:55 AM, Kings 4
169th ASA Meeting, Pittsburgh
In this paper the relationship between musical instruments and the rooms they are performed in was investigated. A musical instrument is typically characterized as a system that consists of a tone generator combined with a resonator. A saxophone for example has a reed as a tone generator and a comical shaped resonator that can be effectively changed in length with keys to produce different musical notes. Often neglected is the fact that there is a second resonator for all wind instruments coupled to the tone generator – the vocal cavity. We use our vocal cavity everyday when we speak to form characteristic formants, local enhancements in frequency to shape vowels. This is achieved by varying the diameter of the vocal tract at specific local positions along its axis. In contrast to the resonator of a wind instrument, the vocal tract is fixed its length by the dimensions between the vocal chords and the lips. Consequently, the vocal tract cannot be used to change the fundamental frequency over a larger melodic range. For out voice, the change in frequency is controlled via the tension of the vocal chords. The musical instrument’s instrument resonator however is not an adequate device to control the timbre (harmonic spectrum) of an instrument because it can only be varied in length but not in width. Therefore, the players adjustment of the vocal tract is necessary to control the timbre if the instrument. While some instruments posses additional mechanisms to control timbre, e.g., via the embouchure to control the tone generator directly using the lip muscles, for others like the recorder changes in the wind supply provided by the lungs and the changes of the vocal tract. The role of the vocal tract has not been addressed systematically in literature and learning guides for two obvious reasons. Firstly, there is no known systematic approach of how to quantify internal body movements to shape the vocal tract. Each performer has to figure out the best vocal tract configurations in an intuitive manner. For the resonator system, the changes are described through the musical notes, and in cases where multiple ways exist to produce the same note, additional signs exist to demonstrate how to finger this note (e.g., by providing a specific key combination). Secondly, in western classic music culture the vocal tract adjustments predominantly have a correctional function to balance out the harmonic spectrum to make the instrument sound as even as possible across the register.
PVC-Didgeridoo adapter for soprano saxophone
In non-western cultures, the role of the oral cavity can be much more important to convey musical meaning. The didgeridoo, for example, has a fixed resonator with no keyholes and consequently it can only produce a single pitched drone. The musical parameter space is then defined by modulating the overtone spectrum above the tone by changing the vocal tract dimensions and creating vocal sounds on top of the buzzing lips on the didgeridoo edge. Mouthpieces of Western brass instruments have a cup behind the rim with a very narrow opening to the resonator, the throat. The didgeridoo does not have a cup, and the rim is the edge of the resonator with a ring of bee wax. While the narrow throat of western mouthpiece mutes additional sounds produced with the voice, didgeridoos are very open from end to end and carry the voice much better.
The room, a musical instrument is performed in acts as a third resonator, which also affect the timbre of the instrument. In our case, the room was simulated using a computer model with early reflections and late reverberation.
Tone generators for soprano saxophone from left to right: Chinese Bawu, soprano saxophone, Bassoon reed, cornetto.
In general, it is difficult to assess the effect of a mouthpiece and resonator individually, because both vary across instruments. The trumpet for example has a narrow cylindrical bore with a brass mouthpiece, the saxophone has a wide conical bore with reed-based mouthpiece. To mitigate this effect, several tone generators were adapted for a soprano saxophone, including a brass mouthpiece from a cornetto, a bassoon mouthpiece and a didgeridoo adapter made from a 140 cm folded PCV pipe that can be attached to the saxophone as well. It turns out that the exchange of tone generators change the timbre of the saxophone significantly. The cornetto mouthpiece gives the instrument a much mellower tone. Similar to the baroque cornetto, the instruments sounds better in a bright room with lot of high frequencies, while the saxophone is at home at a 19th-century concert hall with a steeper roll off at high frequencies.
When Motorola’s vice president, Martin Cooper, made his first call from a mobile phone device, which priced about four thousand dollars back in 1983, one could not have imagined then that in just a few decades mobile phones would become a crucial and ubiquitous part of everyday life. Not surprisingly this technology is also being increasingly misused by the criminal fraternity to coordinate their activities, which range from threatening calls, to ransoms and even bank frauds and robberies.
Recordings of mobile phone conversations can sometimes be presented as major pieces of evidence in a court of law. However, identifying a criminal by their voice is not a straight forward task and poses many challenges. Unlike DNA and finger prints, an individual’s voice is far from constant and exhibits changes as a result of a wide range of factors. For example, the health condition of a person can substantially change his/her voice, and as a result the same words spoken on one occasion would sound different on another.
The process of comparing voice samples and then presenting the outcome to a court of law is technically known as forensic voice comparison. This process begins by extracting a set of features from the available speech recordings of an offender, whose identity obviously is unknown, in order to capture information that is unique to their voice. These features are then compared using various procedures with those of the suspect charged with the offence.
One approach that is becoming widely accepted nowadays amongst forensic scientists for undertaking forensic voice comparison is known as the likelihood ratio framework. The likelihood ratio addresses two different hypotheses and estimates their associated probabilities. First is the prosecution hypothesis which states that suspect and offender voice samples have the same origin (i.e., suspect committed the crime). Second is the defense hypothesis that states that the compared voice samples were spoken by different people who just happen to sound similar.
When undertaking this task of comparing voice samples, forensic practitioners might erroneously assume that mobile phone recordings can all be treated in the same way, irrespective of which mobile phone network they originated from. But this is not the case. There are two major mobile phone technologies currently in use today: the Global System for Mobile Communications (GSM) and Code Division Multiple Access (CDMA), and these two technologies are fundamentally different in the way they process speech. One difference, for example, is that the CDMA network incorporates a procedure for reducing the effect of background noise picked up by the sending-end mobile microphone, whereas the GSM network does not. Therefore, the impact of these networks on voice samples is going to be different, which in turn will impact the accuracy of any forensic analysis undertaken.
Having two mobile phone recordings, one for the suspect and another for the offender that originate from different networks represent a typical scenario in forensic case work. This situation is normally referred to as a mismatched condition (see Figure 1). Researchers at the University of Auckland, New Zealand, have conducted a number of experiments to investigate in what ways and to what extent such mismatch conditions can impact the accuracy and precision of a forensic voice comparison. This study used speech samples from 130 speakers, where the voice of each speaker had been recorded on three occasions, separated by one month intervals. This was important in order to account for the variability in a person’s voice which naturally occurs from one occasion to another. In these experiments the suspect and offender speech samples were processed using the same speech codecs as used in the GSM and CDMA networks. Mobile phone networks use these codecs to compress speech in order to minimize the amount of data required for each call. Not only this, the speech codec dynamically interacts with the network and changes its operation in response to changes occurring in the network. The codecs in these experiments were set to operate in a manner similar to what happens in a real, dynamically changing, mobile phone network.
The results suggest that the degradation in the accuracy of a forensic analysis under mismatch conditions can be very significant (as high as 150%). Surprisingly, though, these results also suggest that the precision of a forensic analysis might actually improve. Nonetheless, precise but inaccurate results are clearly undesirable. The researchers have proposed a strategy for lessening the impact of mismatch by passing the suspect’s speech samples through the same speech codec as the offender’s (i.e., either GSM or CDMA) prior to forensic analysis. This strategy has been shown to improve the accuracy of a forensic analysis by about 70%, but performance is still not as good as analysis under matched conditions.
Balamurali B. T. Nair – email@example.com
Esam A. Alzqhoul – firstname.lastname@example.org
Bernard J. Guillemin – email@example.com
Dept. of Electrical & Computer Engineering,
Faculty of Engineering,
The University of Auckland,
Private Bag 92019, Auckland Mail Centre,
Auckland 1142, New Zealand.