3aSPb5 – Improving Headphone Spatialization: Fixing a problem you’ve learned to accept

Muhammad Haris Usmani – usmani@cmu.edu
Ramón Cepeda Jr. – rcepeda@andrew.cmu.edu
Thomas M. Sullivan – tms@ece.cmu.edu
Bhiksha Raj – bhiksha@cs.cmu.edu
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213

Popular version of paper 3aSPb5, “Improving headphone spatialization for stereo music”
Presented Wednesday morning, May 20, 2015, 10:15 AM, Brigade room
169th ASA Meeting, Pittsburgh

The days of grabbing a drink, brushing dust from your favorite record and playing it in the listening room of the house are long gone. Today, with the portability technology has enabled, almost everybody listens to music on their headphones. However, most commercially produced stereo music is mixed and mastered for playback on loudspeakers– this presents a problem for the growing number of headphone listeners. When a legacy stereo mix is played on headphones, all instruments or voices in that piece get placed in between the listener’s ears, inside of their head. This not only is unnatural and fatiguing for the listener, but is detrimental toward the original placement of the instruments in that musical piece. It disturbs the spatialization of the music and makes the sound image appear as three isolated lobes inside of the listener’s head [1], see Figure 1.

usmani_1

Hard-panned instruments separate into the left and right lobes, while instruments placed at center stage are heard in the center of the head. However, as hearing is a dynamic process that adapts and settles with the perceived sound, we have accepted headphones to sound this way [2].

In order to improve the spatialization of headphones, the listener’s ears must be deceived into thinking that they are listening to the music inside of a listening room. When playing music in a room, the sound travels through the air, reverberates inside the room, and interacts with the listener’s head and torso before reaching the ears [3]. These interactions add the necessary psychoacoustic cues for perception of an externalized stereo soundstage presented in front of the listener. If this listening room is a typical music studio, the soundstage perceived is close to what the artist intended. Our work tries to place the headphone listener into the sound engineer’s seat inside a music studio to improve the spatialization of music. For the sake of compatibility across different headphones, we try to make minimal changes to the mastering equalization curve of the music.

Since there is a compromise between sound quality and the spatialization that can be presented, we developed three different systems that present different levels of such compromise. We label these as Type-I, Type-II, and Type-0. Type-I focuses on improving spatialization but at the cost of losing some sound quality, Type-II improves spatialization while taking into account that the sound quality is not degraded too much, and Type-0 focuses on refining conventional listening by making the sound image more homogeneous. Since the sound quality is key in music, we will skip over Type-I and focus on the other two systems.

Type-II, consists of a head related transfer function (HRTF) model [4], room reverberation (synthesized reverb [5]), and a spectral correction block. HRTFs embody all the complex spatialization cues that exist due to the relative positions of the listener and the source [6]. In our case, a general HRTF model is used which is configured to place the listener at the “sweet spot” in the studio (right and left speakers placed at an angle of 30° from the listener’s head). The spectral correction attempts to keep the original mastering equalization curve as intact as possible.

Type-0, is made up of a side-content crossfeed block and a spectral correction block. Some headphone amps allow crossfeed between the left and right channels to model the fact that when listening to music through loudspeakers, each ear can hear the music from each speaker with a delay attached to the sound originating from the speaker that is furthest away. A shortcoming of conventional crossfeed is that the delay we can apply is limited (to avoid comb filtering) [7]. Side-content crossfeed resolves this by only crossfeeding unique content between the two channels, allowing us to use larger delays. In this system, the side-content is extracted by using a stereo-to-3 upmixer, which is implemented as a novel extension to Nikunen et al.’s upmixer [8].

These systems were put to the test by conducting a subjective evaluation with 28 participants, all between 18 to 29 years of age. The participants were introduced to the metrics that were being measured in the beginning of the evaluation. Since the first part of the evaluation included specific spatial metrics which are a bit complicated to grasp for untrained listeners, we used a collection of descriptions, diagrams, and/or music excerpts that represented each metric to provide in-evaluation training for the listeners. The results of the first part of the evaluation suggest that this method worked well.
We were able to conclude from the results that Type-II externalized the sounds while performing at a level analogous to the original source in the other metrics and Type-0 was able to improve sound quality and comfort by compromising stereo width when compared to the original source, which is what we expected. Also, there was strong content-dependence observed in the results suggesting that a different setting of improving spatialization must be used with music that’s been produced differently. Overall, two of the three proposed systems in this work are preferred in equal or greater amounts to the legacy stereo mix.

Tags: music, acoustics, design, technology

References

[1] G-Sonique, “Monitor MSX5 – Headphone monitoring system,” G-Sonique, 2011. [Online]. Available: http://www.g-sonique.com/msx5headphonemonitoring.html.
[2] S. Mushendwa, “Enhancing Headphone Music Sound Quality,” Aalborg University – Institute of Media Technology and Engineering Science, 2009.
[3] C. J. C. H. K. K. Y. J. L. Yong Guk Kim, “An Integrated Approach of 3D Sound Rendering,” Springer-Verlag Berlin Heidelberg, vol. II, no. PCM 2010, p. 682–693, 2010.
[4] D. Rocchesso, “3D with Headphones,” in DAFX: Digital Audio Effects, Chichester, John Wiley & Sons, 2002, pp. 154-157.
[5] P. E. Roos, “Samplicity’s Bricasti M7 Impulse Response Library v1.1,” Samplicity, [Online]. Available: http://www.samplicity.com/bricasti-m7-impulse-responses/.
[6] R. O. Duda, “3-D Audio for HCI,” Department of Electrical Engineering, San Jose State University, 2000. [Online]. Available: http://interface.cipic.ucdavis.edu/sound/tutorial/. [Accessed 15 4 2015].
[7] J. Meier, “A DIY Headphone Amplifier With Natural Crossfeed,” 2000. [Online]. Available: http://headwize.com/?page_id=654.
[8] J. Nikunen, T. Virtanen and M. Vilermo, “Multichannel Audio Upmixing by Time-Frequency Filtering Using Non-Negative Tensor Factorization,” Journal of the AES, vol. 60, no. 10, pp. 794-806, October 2012.

2aSC – Speech: An eye and ear affair!

Pamela Trudeau-Fisette – ptrudeaufisette@gmail.com
Lucie Ménard – menard.lucie@uqam.ca
Université du Quebec à Montréal
320 Ste-Catherine E.
Montréal, H3C 3P8

Popular version of poster session 2aSC, “Auditory feedback perturbation of vowel production: A comparative study of congenitally blind speakers and sighted speakers”
Presented Tuesday morning, May 19, 2015, Ballroom 2, 8:00 AM – 12:00 noon
169th ASA Meeting, Pittsburgh
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When learning to speak, young infants and toddlers use auditory and visual cues to correctly associate speech movements to a specific speech sound. In doing so, typically developing children compare their own speech and those of their ambient language to build and improve the relationship between what they hear, see and feel, and how to produce it.

In many day-to-day situations, we exploit the multimodal nature of speech: in noisy environments, for instance like in a cocktail party, we look at our interlocutor’s face and use lip reading to recover speech sounds. When speaking clearly, we open our mouth wider to make ourself sound more intelligible. Sometimes, just seeing someone’s face is enough to communicate!

What happens in cases of congenital blindness? Despite the fact that blind speakers learn to produce intelligible speech, they do not quite speak like sighted speakers do. Since they do not perceive others’ visual cues, blind speakers do not produce visible labial movements as much as their sighted peers do.

Production of the French vowel “ou” (similar as in cool) produced by a sighted adult speaker (on the left) and a congenitally blind adult speaker (on the right). We can clearly see that the articulatory movements of the lips are more explicit for the sighted speaker.

Therefore, blind speakers put more weight on what they hear (auditory feedback) than sighted speakers, because one sensory input is lacking. How does that affect the way blind individuals speak?

To answer this question, we conducted an experiment during which we asked congenitally blind adult speakers and sighted adult speakers to produce multiple repetitions of the French vowel “eu”. While they were producing the 130 utterances, we gradually altered their auditory feedback through headphones – without them knowing it- so that they were not hearing the exact sound they were saying. Consequently, they needed to modify the way they produced the vowel in order to compensate for the acoustic manipulation, so they could hear the vowel they were asked to produce (and the one they thought they were saying all along!).

What we were interested in is whether blind speakers and sighted speakers would react differently to this auditory manipulation. The blind speakers not being able to rely on visual feedback, we hypothesized that they would grant more importance on their auditory feedback and, therefore, compensate to a greater extent for the acoustic manipulation.

To explore this matter, we observed the acoustic (produced sounds) and articulatory (lips and tongue movements) differences between the two groups at three distinct time points of the experiment phases.

As predicted, congenitally blind speakers compensated for the altered auditory feedback in a greater extent than their sighted peers. More specifically, even though both speaker groups adapted their productions, the blind group compensated more than the control group did, as if they were integrating the auditory information more strongly. Also, we found that both speaker groups used different articulatory strategies to respond to the applied manipulation: blind participants used their tongue (which is not visible when you speak) more to compensate. This latter observation is not surprising considering the fact that blind speakers do not use their lips (which is visible when you speak) as much as their sighted peers do.

Tags: speech, language, learning, vision, blindness

1pSC2 – Deciding to go (or not to go) to the party may depend as much on your memory as on your hearing

Kathy Pichora-Fuller – k.pichora.fuller@utoronto.ca
Department of Psychology, University of Toronto,
3359 Mississauga Road,
Mississauga, Ontario, CANADA L5L 1C6

Sherri Smith – Sherri.Smith@va.gov
Audiologic Rehabilitation Laboratory, Veterans Affairs Medical Center,
Mountain Home, Tennessee, UNITED STATES 37684

Popular version of paper 1pSC2 Effects of age, hearing loss and linguistic complexity on listening effort as measured by working memory span
Presented Monday afternoon, May 18, 2015 (Session: Listening Effort II)
169th ASA Meeting, Pittsburgh

Understanding conversation in noisy everyday situations can be a challenge for listeners, especially individuals who are older and/or hard-of-hearing. Listening in some everyday situations (e.g., at dinner parties) can be so challenging that people might even decide that they would rather stay home than go out. Eventually, avoiding these situations can damage relationships with family and friends and reduce enjoyment of and participation in activities. What are the reasons for these difficulties and why are some people affected more than other people?

How easy or challenging it is to listen may vary from person to person because some people have better hearing abilities and/or cognitive abilities compared to other people. The hearing abilities of some people may be affected by the degree or type of their hearing loss. The cognitive abilities of some people, for example how well they can attend to and remember what they have heard, can also affect how easy it is for them to follow conversation in challenging listening situations. In addition to hearing abilities, cognitive abilities seem to be particularly relevant because in many everyday listening situations people need to listen to more than one person talking at the same time and/or they may need to listen while doing something else such as driving a car or crossing a busy street. The auditory demands that a listener faces in a situation increase as background noise becomes louder or as more interfering sounds combine with each other. The cognitive demands in a situation increase when listeners need to keep track of more people talking or to divide their attention as they try to do more tasks at the same time. Both auditory and cognitive demands could result in the situation becoming very challenging and these demands may even totally overload a listener.

One way to measure information overload is to see how much a person remembers after they have completed a set of tasks. For several decades, cognitive psychologists have been interested in ‘working memory’, or a person’s limited capacity to process information while doing tasks and to remember information after the tasks have been completed. Like a bank account, the more cognitive capacity is spent on processing information while doing tasks, the less cognitive capacity will remain available for remembering and using the information later. Importantly, some people have bigger working memories than other people and people who have a bigger working memory are usually better at understanding written and spoken language. Indeed, many researchers have measured working memory span for reading (i.e., a task involving the processing and recall of visual information) to minimize ‘contamination’ from the effects of hearing loss that might be a problem if they measured working memory span for listening. However, variations in difficulty due to hearing loss may be critically important in assessing how the demands of listening affect different individuals when they are trying to understand speech in noise. Some researchers have studied the effects of the acoustical properties of speech and interfering noises on listening, but less is known about how variations in the type of language materials (words, sentences, stories) might alter listening demands for people who have hearing loss. Therefore, to learn more about why some people cope better when listening to conversation in noise, we need to discover how both their auditory and their cognitive abilities come into play during everyday listening for a range of spoken materials.

We predicted that speech understanding would be more highly associated with working memory span for listening than with listening span for reading, especially when more realistic language materials are used to measure speech understanding. To test these predictions, we conducted listening and reading tests of working memory and we also measured memory abilities using five other measures (three auditory memory tests and two visual memory tests). Speech understanding was measured with six tests (two tests with words, one in quiet and one in noise; three tests with sentences, one in quiet and two in noise; one test with stories in quiet). The tests of speech understanding using words and sentences were selected from typical clinical tests and involved simple immediate repetition of the words or sentences that were heard. The test using stories has been used in laboratory research and involved comprehension questions after the end of the story. Three groups with 24 people in each group were tested: one group of younger adults (mean age = 23.5 years) with normal hearing and two groups of older adults with hearing loss (one group with mean age = 66.3 years and the other group with mean age 74.3 years).

There was a wide range in performance on the listening test of working memory, but performance on the reading test of working memory was more limited and poorer. Overall, there was a significant correlation between the results on the reading and listening working memory measures. However, when correlations were conducted for each of the three groups separately, the correlation reached significance only for the oldest listeners with hearing loss; this group had lower mean scores on both tests. Surprisingly, for all three groups, there were no significant correlations among the working memory and speech understanding measures. To further investigate this surprising result, a factor analysis was conducted. The results of the factor analysis suggest that there was one factor including age, hearing test results and performance on speech understanding measures when the speech-understanding task was simply to repeat words or sentences – these seem to reflect auditory abilities. In addition, separate factors were found for performance on the speech understanding measures involving the comprehension of discourse or the use of semantic context in sentences – these seem to reflect linguistic abilities. Importantly, the majority of the memory measures were distinct from both kinds of speech understanding measures, and also a more basic and less cognitively demanding memory measure involving only the repetition of sets of numbers. Taken together, these findings suggest that working memory measures reflect differences between people in cognitive abilities that are distinct from those tapped by the sorts of simple measures of hearing and speech understanding that have been used in the clinic. Above and beyond current clinical tests, by testing working memory, especially listening working memory, useful information could be gained about why some people cope better than others in everyday challenging listening situations.

tags: age, hearing, memory, linguistics, speech

4pAB3 – Can a spider “sing”? If so, who might be listening?

Alexander L. Sweger – swegeral@mail.uc.edu
George W. Uetz – uetzgw@ucmail.uc.edu
University of Cincinnati
Department of Biological Sciences
2600 Clifton Ave, Cincinnati OH 45221

Popular version of paper 4pAB3, “the potential for acoustic communication in the ‘purring’ wolf spider’
Presented Thursday afternoon, May 21, 2015, 2:40 PM, Rivers room
169th ASA Meeting, Pittsburgh
Click here to read the abstract

While we are familiar with a wide variety of animals that use sound to communicate- birds, frogs, crickets, etc.- there are thousands of animal species that use vibration as their primary means of communication. Since sound and vibration are physically very similar, the two are inextricable connected, but biologically they are still somewhat separate modes of communication. Within the field of bioacoustics, we are beginning to fully realize how prevalent vibration is as a mode of animal communication, and how interconnected vibration and sound are for many species.

Wolf spiders are one group that heavily utilizes vibration as a means of communication, and they have very sensitive structures for “listening” to vibrations. However, despite the numerous vibrations that are involved in spider communication, they are not known for creating audible sounds. While a lot of species that use vibration will simultaneously use airborne sound, spiders do not possess structures for hearing sound, and it is generally assumed that they do not use acoustic communication in conjunction with vibration.

The “purring” wolf spider (Gladicosa gulosa) may be a unique exception to this assumption. Males create vibrations when they communicate with potential mates in a manner very similar to other wolf spider species, but unlike other wolf spider species, they also create airborne sounds during this communication. Both the vibrations and the sounds produced by this species are of higher amplitude than other wolf spider species, both larger and smaller, meaning this phenomenon is independent of species size. While other acoustically communicating species like crickets and katydids have evolved structures for producing sound, these spiders are vibrating structures in their environment (dead leaves) to create sound. Since we know spiders do not possess typical “ears” for hearing these sounds, we are interested in finding out if females or other males are able to use these sounds in communication. If they do, then this species could be used as an unusual model for the evolution of acoustic communication.

An image of a male "purring" wolf spider, Gladicosa gulosa, and the spectrogram of his accompanied vibration. Listen to a recording of the vibration here,

Figure 1: An image of a male “purring” wolf spider, Gladicosa gulosa, and the spectrogram of his accompanied vibration. Listen to a recording of the vibration here,

and the accompanying sound here.

Our work has shown that the leaves themselves are vital to the use of acoustic communication in this species. Males can only produce the sounds when they are on a surface that vibrates (like a leaf) and females will only respond to the sounds when they are on a similar surface. When we remove the vibration and only provide the acoustic signal, females still show a significant response and males do not, suggesting that the sounds produced by males may play a part in communicating specifically with females.

So, the next question is- how are females responding to the airborne sound without ears? Despite the relatively low volume of the sounds produced, they can still create a vibration in a very thin surface like a leaf. This creates a complex method of communication- a male makes a vibration in a leaf that creates a sound, which then travels to another leaf and creates a new vibration, which a female can then hear. While relatively “primitive” compared to the highly-evolved acoustic communication in birds, frogs, insects, and other species, this unique usage of the environment may create opportunities for studying the evolution of sound as a mode of animal communication.

Monitoring deep ocean temperatures using low-frequency ambient noise

Katherine Woolfe, Karim G. Sabra
School of Mechanical Engineering, Georgia Institute of Technology
Atlanta, GA 30332-0405

In order to precisely quantify the ocean’s heat capacity and influence on climate change, it is important to accurately monitor ocean temperature variations, especially in the deep ocean (i.e. at depths ~1000m) which cannot be easily surveyed by satellite measurements. To date, deep ocean temperatures are most commonly measured using autonomous sensing floats (e.g. Argo floats). However, this approach is limited because, due to costs and logistics, the existing global network of floats cannot sample the entire ocean at the lower depths. On the other hand, acoustic thermometry (using the travel time of underwater sound to infer the temperature of the water the sound travels through) has already been demonstrated as one of the most precise methods for measuring ocean temperature and heat capacity over large distances (Munk et al., 1995; Dushaw et al., 2009; The ATOC Consortium, 1998). However, current implementations of acoustic thermometry require the use of active, man-made sound sources. Aside from the logistical issues of deploying such sources, there is also the ongoing issue of negative effects on marine animals such as whales.

An emerging alternative to measurements with active acoustic sources is the use of ambient noise correlation processing, which uses the background noise in an environment to extract useful information about that environment. For instance, ambient noise correlation processing has successfully been used to monitor seismically-active earth systems such as fault zones (Brenguier et al., 2008) and volcanic areas (Brenguier et al., 2014). In the context of ocean acoustics (Roux et al., 2004; Godin et al., 2010; Fried et al., 2013), previous studies have demonstrated that the noise correlation method requires excessively long averaging times to reliably extract most of the acoustic travel-paths that were used by previous active acoustic thermometry studies (Munk et al., 1995). Consequently, since this averaging time is typically too long compared to the timescale of ocean fluctuations (i.e., tides, surface waves, etc.), this would prevent the application of passive acoustic thermometry using most of these travel paths (Roux et al., 2004; Godin et al., 2010; Fried et al., 2013). However, for deep ocean propagation, there is an unusually stable acoustic travel path, where sound propagates nearly horizontally along the Sound Fixing and Ranging (SOFAR) channel. The SOFAR channel is centered on the minimum value of the sound speed over the ocean depth (located at ~1000 m depth near the equator) and thus acts as a natural pathway for sound to travel very large distances with little attenuation (Ewing and Worzel, 1948).

In this research, we have demonstrated the feasibility of a passive acoustic thermometry method use in the deep oceans, using only recordings of low-frequency (f~10 Hz) ambient noise propagating along the SOFAR channel. This study used continuous recordings of ocean noise from two existing hydroacoustic stations of the International Monitoring System, operated by the Comprehensive Nuclear-Test-Ban Treaty Organization, located respectively next to Ascension and Wake Islands (see Fig. 1(a)). Each hydroacoustic station is composed of two triangular-shaped horizontal hydrophone arrays (Fig. 1(b)), separated by L~130 km, which are referred to hereafter as the north and south triads. The sides of each triad are ~2 km long and the three hydrophones are located within the SOFAR channel at depth ~1000 m. From year to year, the acoustic waves that propagate between hydrophone pairs along the SOFAR channel build up from distant noise sources whose paths intersect the hydrophone pairs. In the low-frequency band used here (1-40 Hz) -with most of the energy of the arrivals being centered around 10 Hz- these arrivals are known to mainly originate from ice-breaking noise in the Polar regions (Chapp et al., 2005; Matsumoto et al., 2014; Gavrilov and Li, 2009; Prior et al., 2011). The angular beams shown in Fig. 1a illustrate a simple estimate of the geographical area from which ice-generated ambient noise is likely to emanate for each site (Woolfe et al., 2015).

Sabra1 - deep ocean

FIG. 1. (a) Locations of the two hydroacoustic stations (red dots) near Ascension and Wake Islands. (b) Zoomed-in schematic of the hydrophone array configurations for the Ascension and Wake Island sites. Each hydroacoustic station consists of a northern and southern triangle array of three hydrophones (or triad), with each triangle side having a length ~ 2 km. The distance L between triad centers is equal to 126 km and 132 km for the Ascension Island and Wake Island hydroacoustic stations, respectively.

Acoustic thermometry estimates ocean temperature fluctuations averaged over the entire acoustic travel path (in this case, the entire depth and length of the SOFAR channel between north and south hydrophone triads) by leveraging the nearly linear dependence between sound speed in water and temperature (Munk et al., 1995). Here the SOFAR channel extends approximately from 390 m to 1350 m deep at the Ascension Island site and 460 m to 1600 m deep at the Wake Island site, as determined from the local sound speed profiles and the center frequency (~10 Hz) of the SOFAR arrivals. We use passive acoustic thermometry is used to monitor the small variations in the travel time of the SOFAR arrivals over several years (8 years at Ascension Island, and 5 years at Wake Island). To do so, coherent arrivals are extracted by averaging cross-correlations of ambient noise recordings over 1 week at the Wake and Ascension Island sites. The small fluctuations in acoustic travel time are converted to deep ocean temperature fluctuations by leveraging the linear relationship between change in sound speed and change in temperature in the water (Woolfe et al., 2015). These calculated temperature fluctuations are shown in Fig. 2, and are consistent with Argo float measurements. At the Wake Island site, where data are measured only over 5 years, the Argo and thermometry data are found to be 54% correlated. Both data indicate a very small upward (i.e. warming) trend. The Argo data shows a trend of 0.003 °C /year ± 0.001 °C/ year, for 95% confidence interval, and the thermometry data shows a trend of 0.007 °C /year ± 0.002 °C/ year, for 95% confidence interval (Fig. 2(a)). On the other hand, for the Ascension site, the SOFAR channel temperature variations measured over a longer duration of eight years from passive thermometry and Argo data are found to be significantly correlated, with a 0.8 correlation coefficient. Furthermore, Fig. 2(b) indicates a warming of the SOFAR channel in the Ascension area, as inferred from the similar upward trend of both passive thermometry (0.013 °C /year ± 0.001 °C/ year, for 95% confidence interval) and Argo (0.013 °C/ year ± 0.004 °C/ year, for 95% confidence interval) temperature variation estimates Hence, our approach provides a simple and totally passive means for measuring deep ocean temperature variations, which could ultimately significantly improve our understanding of the role of oceans in climate change.

sabra2 - deep ocean

FIG. 2. (a) Comparison of the deep ocean temperature variations at the Wake Island site estimated from passive thermometry (blue line) with Argo float measurements (grey dots), along with corresponding error bars (Woolfe et al., 2015). (b) Same as (a), but for the Ascension Island site. Each ΔT data series is normalized so that a linear fit on the data would have a y-intercept at zero.

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Brenguier, F., Campillo, M., Takeda, T., Aoki, Y., Shapiro, N.M., Briand, X., Emoto, K., and Miyake, H. (2014). “Mapping Pressurized Volcanic Fluids from Induced Crustal Seismic Velocity Drops”, Science. 345, 80-82.
Brenguier, F., Campillo, M., Hadziioannou, C., Shapiro, N.M., Nadeau, R.M., and Larose, E. (2008). “Postseismic Relazation Along the San Andreas Fault at Parkfield from Continuous Seismological Observations.” Science. 321, 1478-1481.
Chapp, E., Bohnenstiehl, D., and Tolstoy, M. (2005). “Sound-channel observations of ice-generated tremor in the Indian Ocean”, Geochem. Geophys. Geosyst., 6, Q06003.
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Ewing, M., and Worzel, J.L., (1948). “Long-Range Sound Transmission”, GSA Memoirs. 27, 1-32.
Fried, S., Walker, S.C. , Hodgkiss, W.S. , and Kuperman, W.A. (2013). “Measuring the effect of ambient noise directionality and split-beam processing on the convergence of the cross-correlation function”, J. Acoust. Soc. Am., 134, 1824-1832.
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