1aNS5 – Noise, vibration, and harshness (NVH) of smartphones

Inman Jang – kgpbjim@yonsei.ac.kr
Tae-Young Park – pty0948@yonsei.ac.kr
Won-Suk Ohm – ohm@yonsei.ac.kr
Yonsei University
50, Yonsei-ro, Seodaemun-gu
Seoul 03722
Korea

Heungkil Park – heungkil.park@samsung.com
Samsung Electro Mechanics Co., Ltd.
150, Maeyeong-ro, Yeongtong-gu
Suwon-si, Gyeonggi-do 16674
Korea

Popular version of paper 1aNS5, “Controlling smartphone vibration and noise”
Presented Monday morning, November 28, 2016
172nd ASA Meeting, Honolulu

Noise, vibration, and harshness, also known as NVH, refers to the comprehensive engineering of noise and vibration of a device through stages of their production, transmission, and human perception. NVH is a primary concern in car and home appliance industries because many consumers take into account the quality of noise when making buying decisions. For example, a car that sounds too quiet (unsafe) or too loud (uncomfortable) is a definite turnoff. That said, a smartphone may strike you as an acoustically innocuous device (unless you are not a big fan of Metallica ringtones), for which the application of NVH seems unwarranted. After all, who would expect the roar of a Harley from a smartphone? But think again. Albeit small in amplitude (less than 30 dB), smartphones emit an audible buzz that, because of the close proximity to the ear, can degrade the call quality and cause annoyance.

smartphone-noise

Figure 1: Smartphone noise caused by MLCCs

The major culprit for the smartphone noise is the collective vibration of tiny electronics components, known as multi-layered ceramic capacitors (MLCCs). An MLCC is basically a condenser made of piezoelectric ceramics, which expands and contracts upon the application of voltage (hence piezoelectric). A typical smartphone has a few hundred MLCCs soldered to the circuit board inside. The almost simultaneous pulsations of these MLCCs are transmitted to and amplified by the circuit board, the vibration of which eventually produces the distinct buzzing noise as shown in Fig. 1. (Imagine a couple hundred rambunctious little kids jumping up and down on a floor almost in unison!) The problem has been even more exacerbated by the recent trend in which the name of the game is “The slimmer the better”; because a slimmer circuit board is much easier to flex it transmits and produces more vibration and noise.

Recently, Yonsei University and Samsung Electromechanics in South Korea joined forces to address this problem. Their comprehensive NVH regime includes the visualization of smartphone noise and vibration (transmission), the identification and replacement of the most problematic MLCCs (production), and the evaluation of harshness of the smartphone noise (human perception). For visualization of smartphone noise, a technique known as the nearfield acoustic holography is used to produce a sound map as shown in Fig. 2, in which the spatial distribution of sound pressure, acoustic intensity or surface velocity can be overlapped on the snapshot of the smartphone. Such sound maps help smartphone designers draw a detailed mental picture of what is going on acoustically and proceed to rectify the problem by identifying the groups of MLCCs most responsible for producing the vibration of the circuit board. Then, engineers can take corrective actions by replacing the (cheap) problematic MLCCs with (expensive) low-vibration MLCCs. Lastly, the outcome of the noise/vibration engineering is measured not only in terms of physical attributes such as sound pressure level, but also in their psychological correlates such as loudness and the overall psychoacoustic annoyance. This three-pronged strategy (addressing production, transmission, and human perception) is proven to be highly effective, and currently Samsung Electromechanics is offering the NVH service to a number of major smartphone vendors around the world.

sound-map - smartphone

Figure 2: Sound map of a smartphone surface

 

1aSC31 – Shape changing artificial ear inspired by bats enriches speech signals

Anupam K Gupta1,2, Jin-Ping Han ,2, Philip Caspers1, Xiaodong Cui2, Rolf Müller1

  1. Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA
  2. IBM T. J. Watson Research Center, Yorktown, NY, USA

Contact: Jin-Ping Han – hanjp@us.ibm.com

Popular version of paper 1aSC31, “Horseshoe bat inspired reception dynamics embed dynamic features into speech signals.”
Presented Monday morning, Novemeber 28, 2016
172nd ASA Meeting, Honolulu

Have you ever had difficulty understanding what someone was saying to you while walking down a busy big city street, or in a crowded restaurant? Even if that person was right next to you? Words can become difficult to make out when they get jumbled with the ambient noise – cars honking, other voices – making it hard for our ears to pick up what we want to hear. But this is not so for bats. Their ears can move and change shape to precisely pick out specific sounds in their environment.

This biosonar capability inspired our artificial ear research and improving the accuracy of automatic speech recognition (ASR) systems and speaker localization. We asked if could we enrich a speech signal with direction-dependent, dynamic features by using bat-inspired reception dynamics?

Horseshoe bats, for example, are found throughout Africa, Europe and Asia, and so-named for the shape of their noses, can change the shape of their outer ears to help extract additional information about the environment from incoming ultrasonic echoes. Their sophisticated biosonar systems emit ultrasonic pulses and listen to the incoming echoes that reflect back after hitting surrounding objects by changing their ear shape (something other mammals cannot do). This allows them to learn about the environment, helping them navigate and hunt in their home of dense forests.

While probing the environment, horseshoe bats change their ear shape to modulate the incoming echoes, increasing the information content embedded in the echoes. We believe that this shape change is one of the reasons bats’ sonar exhibit such high performance compared to technical sonar systems of similar size.

To test this, we first built a robotic bat head that mimics the ear shape changes we observed in horseshoe bats.

han1 - bats

Figure 1: Horseshoe bat inspired robotic set-up used to record speech signal

We then recorded speech signals to explore if using shape change, inspired by the bats, could embed direction-dependent dynamic features into speech signals. The potential applications of this could range from improving hearing aid accuracy to helping a machine more-accurately hear – and learn from – sounds in real-world environments.

We compiled a digital dataset of 11 US English speakers from open source speech collections provided by Carnegie Mellon University. The human acoustic utterances were shifted to the ultrasonic domain so our robot could understand and play back the sounds into microphones, while the biomimetic bat head actively moved its ears. The signals at the base of the ears were then translated back to the speech domain to extract the original signal.
This pilot study, performed at IBM Research in collaboration with Virginia Tech, showed that the ear shape change was, in fact, able to significantly modulate the signal and concluded that these changes, like in horseshoe bats, embed dynamic patterns into speech signals.

The dynamically enriched data we explored improved the accuracy of speech recognition. Compared to a traditional system for hearing and recognizing speech in noisy environments, adding structural movement to a complex outer shape surrounding a microphone, mimicking an ear, significantly improved its performance and access to directional information. In the future, this might improve performance in devices operating in difficult hearing scenarios like a busy street in a metropolitan center.

han2

Figure 2: Example of speech signal recorded without and with the dynamic ear. Top row: speech signal without the dynamic ear, Bottom row: speech signal with the dynamic ear

5aEA2 – What Does Your Signature Sound Like?

Daichi Asakura – asakura@pa.info.mie-u.ac.jp
Mie University
Tsu, Mie, Japan

Popular version of poster, 5aEA2. “Writer recognition with a sound in hand-writing”
172nd ASA Meeting, Honolulu

We can notice a car approaching by noise it makes on the road or can recognize a person by the sound of their footsteps. There are many studies analyzing and recognizing these noises. In the computer security industry, studies have even been proposed to estimate what is being typed from the sound of typing on the keyboard [1] and extracting RSA keys through noises made by a PC [2].

Of course, there is a relationship between a noise and its cause and that noise, therefore, contains information. The sound of a person writing, or “hand writing sound,” is one of the noises in our everyday environment. Previous studies have addressed the recognition of handwritten numeric characters by using the resulting sound, finding an average recognition of 88.4%. Based on this study, we seek the possibility of recognizing and identifying a writer by using the sound of their handwriting. If accurate identification is possible, it could become a method of signature verification without having to ever look at the signature.

We used the handwriting sounds of nine participants, conducting recognition experiments. We asked them to write the same text, which were names in Kanji, the Chinese characters, under several different conditions, such as writing slowly or writing on a different day. Figure 1 shows an example of a spectrogram of the hand-writing sound we analyzed. The bottom axis represents time and the vertical axis shows frequency. Colors represent the magnitude – or intensity – of the frequencies, where red indicates high intensity and blue is low.
handwriting

The spectrogram showed features corresponding to the number of strokes in the Kanji. We used a recognition system based on a hidden Markov model (HMM) – typically used for speech recognition –, which represents transitions of spectral patterns as they evolve in time. The results showed an average identification rate of 66.3%, indicating that writer identification is possible in this manner. However, the identification rate decreased under certain conditions, especially a slow writing speed.

To improve performances, we need to increase the number of hand writing samples and include various written texts as well as participants. We also intend to include writing of English characters and numbers. We expect that Deep Learning, which is attracting increasing attention around the world, will also help us achieve a higher recognition rate in future experiments.

 

  1. Zhuang, L., Zhou, F., and Tygar, J. D., Keyboard Acoustic Emanations Revisited, ACM Transactions on Information and Systems Security, 2009, vol.13, no.1, article 3, pp.1-26.
  2. Genkin, D., Shamir, A., and Tromer, E., RSA Key Extraction via Low-Bandwidth Acoustic Cryptanalysis, Proceedings of CRYPTO 2014, 2014, pp.444-461.
  3. Kitano, S., Nishino, T. and Naruse, H., Handwritten digit recognition from writing sound using HMM, 2013, Technical Report of the Institute of Electronics, Information and Communication Engineers, vol.113, no.346, pp.121-125.

5aNS6 – The Perceived Annoyance of Urban Soundscapes

Adam Craig – Adam.Craig@gcu.ac.uk
Don Knox – D.Knox@gcu.ac.uk
David Moore – J.D.Moore@gcu.ac.uk

Glasgow Caledonian University
School of Engineering and Built Environment
70 Cowcaddens Road
Glasgow
United Kingdom
G4 0BA

Popular version of paper 5aNS6
Presented Friday morning, October 31st 2014
168th ASA Meeting, Indianapolis

The term ‘soundscape’ is widely used to describe the sonic landscape and can be considered the auditory equivalent of a visual landscape. Current soundscape research looks into the view of sound assessment in terms of perception and has been the subject of large scale projects such as the Positive Soundscapes Project (Davies et al. 2009) i.e. the emotional attributes associated with particular sounds. This research addresses the limitations of current noise assessment methods by taking into account the relationship between the acoustic environment and the emotional responses and behavioural characteristics of people living within it. Related research suggests that a variety of objective and subjective factors influence the effects of exposure to noise, including age, locale, cross-cultural differences (Guyot at el. 2005) and the time of year (Yang and Kang, 2005). A key aspect of this research area is the subjective effect of the soundscape on the listener. This paradigm emphasises the subjective perception of sound in an environment – and whether it is perceived as being positive or negative. This approach dovetails with advancing sound and music classification research which aims to categorise sounds in terms of their emotional impact on the listener.

Annoyance is one of the main factors which contribute to a negative view of environmental noise, and can lead to stress-related health conditions. Subjective perception of environmental sounds is dependent upon a variety of factors related to the sound, the geographical location and the listener. Noise maps used to communicate information to the public about environmental noise in a given geographic location are based on simple noise level measurements, and do not include any information regarding how perceptually annoying or otherwise the noise might be.

Selected locations for recording - image courtesy of Scottish Noise Mapping
Figure 1 Selected locations for recording – image courtesy of Scottish Noise Mapping

This study involved subjective assessment by a large panel of listeners (N=167) of a corpus of sixty pre-recorded urban soundscapes collected from a variety of locations around Glasgow City Centre (see figure 1). Binaural recordings were taken at three points during each 24 hour period in order to capture urban noise during day, evening and night. Perceived annoyance was measured using Likert and numerical scales and each soundscape measured in terms of arousal and positive/negative valence (see figure 2).

craig_figure2
Figure 2 Arousal/Valance Circumplex Model Presented in Listening Tests

Coding of each of the soundscapes would be essential process in order to test the effects of the location on the variables provided by the online survey namely annoyance score (verbal), annoyance score (numeric), quadrant score, arousal score, and valence score. The coding was based on the environment i.e. urban (U), semi-open (S), or open (O); the density of traffic i.e. high (H), mid (M), low (L); and the distance form the main noise source (road traffic) using two criteria >10m (10+) and <10m (10-). The coding resulted in eight different location types; UH10-, UH10+, UM10+, UL10-, SM10+, SL10-, SL10+, and OL10+.

To capture quantitative information about the actual audio recordings themselves, the MIRToolkit for MATLAB was used to extract acoustical features from the dataset. Several functions were identified that could be meaningful for measuring the soundscapes in terms of loudness, spectral shape, but also rhythm, which could be thought of in not so musical terms but as the rate and distribution of events within a soundscape.

As expected, correlations between extracted features and locations suggest where there are many transient events, higher energy levels, and where the type of events include harsh and dissonant sounds i.e. heavy traffic, resulted in higher annoyance scores and higher arousal scores but perceived more negatively than quiet areas. In those locations where there are fewer transient events, lower energy levels, and there are less harsh and possibly more positive sounds i.e. birdsong, resulted in lower annoyance scores and lower arousal scores as well as being perceived more positively than busy urban areas. The results shed light on the subjective annoyance of environmental sound in a range of locations and provide the reader with an insight as to what psychoacoustic features may contribute to these views of urban soundscapes.

References

Davies, W., Adams, M., Bruce, N., Cain, R., Jennings, P., Carlyle, A., … Plack, C. (2009, October 26). A positive soundscape evaluation system. Retrieved from http://usir.salford.ac.uk/2468/1/Davies_et_al_soundscape_evaluation_euronoise_2009.pdf

Guyot, F., Nathanail, C., Montignies, F., & Masson, B. (2005). Urban sound environment quality through a physical and perceptive classification of sound sources : a cross-cultural study Methodology.

Scottish Noise Mapping (2014). Scottish Noise Mapping: Map Search [Online] http://gisapps.aecomgis.com/scottishnoisemapping_p2/default.aspx#/Main. [Accessed 20th June 2014]

Yang, W., & Kang, J. (2005). Soundscape and Sound Preferences in Urban Squares: A Case Study in Sheffield. Journal of Urban Design, 10(1), 61–80. doi:10.1080/13574800500062395

1aAB4 – What does a Greater Prairie-Chicken sound like? It’s not your typical cock-a-doodle-doo!

Cara Whalen – cara.whalen@huskers.unl.edu
Mary Bomberger Brown – mbrown9@unl.edu
Larkin Powell – lpowell3@unl.edu
Jennifer Smith – jsmith60@unl.edu
University of Nebraska – Lincoln

School of Natural Resources
3310 Holdrege Street
Lincoln, NE 68583

Edward J. Walsh – Edward.Walsh@boystown.org
JoAnn McGee – JoAnn.McGee@boystown.org
Boys Town National Research Hospital
555 N. 30th Street
Omaha, NE 68131

Popular version of 1aAB4 The acoustic characteristics of greater prairie-chicken vocalizations
Presented Monday morning, October 27th, 2014
168th ASA Meeting, Indianapolis
Click here to read the abstract

It is 5 o’clock in the morning and only a hint of sunlight is visible on the horizon. Besides the sound of a light breeze swirling through the grass, all is quiet on the Nebraska prairie. Everything seems to be asleep. Then, suddenly, “whhooo-doo-doooohh” breaks the silence. The prairie-chickens have arrived.

The Greater Prairie-Chicken is a medium-sized grouse that lives on the prairies of central North America (Figure 1a) (Schroeder and Robb 1993). Prairie-chickens are well-known for their breeding activities in which the males congregate in groups each spring and perform elaborate courtship displays to attract females (Figure 1b). The areas where the males gather, called “leks,” are distributed across the landscape. Female prairie-chickens visit leks every morning to observe and compare males until a suitable one is chosen. After mating, females leave the leks to nest and raise their broods on their own, while the males remain on the leks and continue to perform courtship displays. Click the link to watch a video clip of prairie chickens lekking.

Prairie-Chicken Prairie-Chicken

Figure 1a: A male Greater Prairie-Chicken. Figure 1b: A male prairie-chicken performs a courtship display for a female.

These complex courtship behaviors do not occur in silence. Vocalization plays an important role in the mate choice behavior of prairie-chickens. As part of a larger study addressing the effects of electricity producing wind turbine farms on prairie-chicken ecology, we wanted to learn more about the acoustic properties of prairie-chicken calls. We did this by recording the sound of prairie-chicken vocalizations at leks in the Nebraska Sandhills. We visited the leks in the very early morning and set up audio recorders, which were placed close enough to prairie-chickens on their leks to obtain high quality recordings (Figure 2a). Sitting in a blind at the edges of leks (Figure 2b), we observed prairie-chickens while they were lekking and collected the audio recordings.

Prairie-Chickenwhalen_figure_2b - Prairie-Chicken

Figure 2a: We used audio recorders to record male prairie-chicken vocalizations at the leks. Figure 2b: We observed lekking prairie-chickens and recorded vocalizations by sitting in a blind at the edge of a lek.

Male Greater Prairie-Chickens use four prominent vocalizations while on the leks: the “boom,” “cackle,” “whine” and “whoop.” The four vocalizations are distinct and serve different purposes.

The boom is used as part of the courtship display, so one function is to attract mates. Booms travel a long distance across the prairie, so another purpose of the call is to advertise lek location to other prairie-chickens (Sparling 1981, 1983). Click to listen to a boom sound clip

or to watch a boom video clip we recorded at the leks.

The “cackles” are short calls typically given in rapid succession. Prairie-chickens use the cackle as an aggressive or territorial call (Sparling 1981, 1983) or as a warning to alert other prairie-chickens of potential danger, such as an approaching prairie falcon, coyote or other predator. Click to listen to a cackle sound clip.

The “whine” is slightly longer in duration than the cackle; whines and cackles are often used together. The purpose of the whine is similar to that of the cackle. It serves as an aggressive and territorial call, although it is thought that whines are somewhat less aggressive than cackles (Sparling 1981, 1983). Click to listen to a whine sound clip

or to watch a video clip of cackles and whines (the cackles are the shorter notes and the whines are the longer notes).

The “whoop” is used for mate attraction. Males typically use the whoop when females are present on the lek (Sparling 1981, 1983). Click to listen to a whoop sound clip

or to watch a whoop video clip.

We measured acoustic characteristics of the vocalizations captured on the recordings so we could evaluate their features in detail. We are using this information about the vocalizations in a study of the effects of wind turbine sound on Greater Prairie-Chickens (Figure 3). We hope to determine whether the vocalizations produced by prairie-chickens near a wind farm are different in any way from those produced by prairie-chickens farther away. For example, do the prairie chickens near wind turbines call at a higher pitch in response to wind turbine sound? Also, do the prairie chickens near wind turbines vocalize louder? Ultimately we would like to know if components of the prairie-chickens’ vocalizations are masked by the sounds of the wind turbines.

Prairie-Chicken

Figure 3: We are conducting a study of the effects of wind turbine noise on Greater Prairie-Chickens.

The effect of anthropogenic noise is an issue not limited to Greater Prairie-Chickens and wind turbines. As humans create increasingly noisy landscapes through residential and industrial development, vehicle traffic, air traffic and urban sprawl, the threats posed to birds and other wildlife are likely to be significant. It is important to be aware of the potential effects of anthropogenic sound and find ways to mitigate those effects as landscapes become noisier.

 

References:

Schroeder, M. A., and L. A. Robb. 1993. Greater Prairie-Chicken (Tympanuchus cupido). In The Birds of North America, no. 36 (A. Poole, P. Stettenheim, and F. Gill, Eds.). Academy of Natural Sciences, Philadelphia, and American Ornithologists’ Union, Washington, D.C.

Sparling, D. W. 1981. Communication in prairie grouse. I. Information content and intraspecific functions of principal vocalizations. Behavioral and Neural Biology 32:463-486.

Sparling, D. W. 1983. Quantitative analysis of prairie grouse vocalizations. Condor 85:30-42.