Animal Bioacoustics | Acoustics.org

3pAB5 – How Canaries Listen to Their Song

Adam R. Fishbein – afishbei@terpmail.umd.edu
Shelby L. Lawson
Gregory F. Ball
Robert J. Dooling

University of Maryland
4123 Biology-Psychology Building
College Park, MD 20742

Popular version of paper 3pAB5
Presented Tuesday afternoon, June 27, 2017
173^rd ASA Meeting, Boston

The melodic, rolling songs of canaries have entertained humans for centuries. But for canaries, these songs play an important role in courtship. The song, produced exclusively by males, can last for minutes and consists of various syllables repeated in flexibly sequenced phrases.

Earlier behavioral observations have shown that females are especially attracted to so-called “sexy” syllables or “sexy” phrases. These are characterized by a fast tempo, wide-bandwidth (meaning that they extend from low to high pitch), and a two-note structure. Researchers have argued that females have evolved to prefer these syllables because they are difficult to produce and thus provide an honest signal of the male’s quality [1][2]. That is, sexy syllables indicate a strong, healthy male with good genes.

Figure 1 Recording of canary song (top) and spectrogram of “sexy” phrase (bottom). The two red lines indicate the two notes of the sexy syllable. (Credit: Fishbein)

We explored how canaries in a non-breeding state (i.e. short days) listen to their song by testing their auditory perception using the equivalent of a human hearing test. Since the birds can’t tell us “yes” or “no” when asked if two sounds are different, we train them to listen to a repeating sound and peck a key when the sound changes. If they respond correctly, this tells us they can hear the difference between the sounds and they are then rewarded with brief access to food.

Figure 2 Canary in testing chamber. (Credit: Fishbein)

Some of the questions we posed are: Do sexy phrases sound different to canaries than other phrases? Do they listen more to the fine details of every syllable or to the overall flow of the song? Are females more sensitive to “sexy” qualities than males? Do other birds hear canary song differently than canaries?

In one experiment, we asked canaries to distinguish between eight different song phrases: four “sexy” ones and four “non-sexy” ones. We analyzed the birds’ responses and created a “perceptual map” that visually represents how distinct the phrases sound to the canaries.

Our results show that canaries perceive a bird’s sexy phrases as more similar to each other than other phrases, confirming that canaries find these sexy syllable vocalizations particularly salient.

Figure 3 “Perceptual map” for canaries. Circles indicate phrases taken from recordings of bird A. Diamonds indicate phrases taken from recordings of bird B. Blue labels are non-sexy phrases and red ones are sexy. Axis labels indicate the acoustic features that each dimension correlates with. (Credit: Fishbein)

Other experiments in this study provided further evidence that sexy song syllables sound distinctive to canaries. Canaries could hear synthesized reversals of sexy syllables, but performed better at reversals of non-sexy ones. They were also better at hearing increases in the tempo of sexy syllables than decreases in tempo. These results suggested that canaries may be attuned to perceiving the fast tempo and coordinated notes of the sexy syllables. Importantly, these findings were the case for both female and male canaries, perhaps because male canaries need to assess competitors and maintain their own song, just as females need to find the highest quality mate.

Canaries are not exceptional in being able to hear the fine details of their song. Other species tested with these song manipulations are similarly sensitive to small temporal differences between notes in sexy syllables.

Taken together, these results suggest that canaries listen to chunk by chunk, phrase by phrase changes in their song, keying in to details about sexiness when those particular syllables occur. In the future, it will be interesting to compare these perceptual results from canaries in a non-breeding state to canaries that are on long days, with elevated hormone levels, preparing to breed.

In a way, canaries seem to listen to song like we listen to an orchestral symphony, hearing the melody and rhythm of the whole piece, integrating the contributions of each instrument, and not zooming in on the performance of a single instrument except during an especially impressive solo.

References

Vallet, E., Kreutzer, M., 1995. “Female canaries are sexually responsive to special song phrases.” Animal Behavior. 49, 1603-1610.
Suthers, R., Vallet, E., Kreutzer, M., 2012. “Bilateral coordination and the motor basis of female preference for sexual signals in canary song.” Journal of Experimental Biology. 215, 2950-2959.

3pAB1 – A Welcoming Whinny

David G. Browning decibeldb@aol.com
Peter D. Herstein – netsailor.ph@cox.net
BROWNING BIOTECH
139 Old North Road
Kingston, RI 02881
Popular Version of paper 3pAB1
Presented Tuesday afternoon, June 27, 2017
173rd ASA Meeting, Boston

Are you greeted with a welcoming whinny when you enter the barn? When doing research on horse whinnys (as part of the Equinne Vocalization Project) we realized we were hearing more whinnys when horses were inside the barn than out. This led us to investigate further and we came to realize it was vocalization adaptation. Horses have remarkable eyesight, with almost a 360° field of view, which they primarily rely on to observe and communicate when out in the open. In a barn, confined to a stall, their line of sight is often blocked. Quite remarkably, they learn to compensate by recognizing the sounds that are of interest — like that of the feed-cart or even their owner’s footsteps — which they often salute with a whinny.

We were curious as to how universal vocalization adaptation occurred in the animal world and in searching the literature we found numerous interesting examples. Asian Wild Dogs (Dholes), for example, hunt prey in packs, usually out in the open where they can visually keep track of the prey and their pack mates. When they encounter some sight-limiting vegetation, however, they have developed a short, flat whistle to keep track of each other but not interfere with their listening for the prey.

Jungles, presenting further examples, are uniquely challenging to animals for three reasons: visibility is limited, moving is difficult, and the vocalization has to be heard despite many others’ sounds. African rhinos out on the plain can make do with a simple bellow, as it would be easy to trot over and check them out. In contrast, a Sumartran rhino, always in the jungle, has a complex vocalization. Often compared to that of a whale, the vocalization is complex in order to be heard among the competing calls while providing enough information so to entice another to slog over to check it out (or not).

The military use a term “situational awareness,” that also refers the awareness that is crucial to animals, and this work provides some examples of their acoustic compensations when visibility is limited for some reason.

2pABa1 – Snap chat: listening in on the peculiar acoustic patterns of snapping shrimp, the noisiest animals on the reef

Ashlee Lillis – ashlee@whoi.edu
T. Aran Mooney – amooney@whoi.edu

Marine Research Facility
Woods Hole Oceanographic Institution
266 Woods Hole Road
Woods Hole, MA 02543

Popular version of paper 2pABa1
Presented Tuesday afternoon, November 29, 2016
172nd ASA Meeting, Honolulu

Characteristic soundscape recorded on a coral reef in St. John, US Virgin Islands. The conspicuous crackle is produced by many tiny snapping shrimp.

Put your head underwater in almost any tropical or sub-tropical coastal area and you will hear a continuous, static-like noise filling the water. The source of this ubiquitous sizzling sound found in shallow-water marine environments around the world was long considered a mystery of the sea. It wasn’t until WWII investigations of this underwater sound, considered troublesome, that hidden colonies of a type of small shrimp were discovered as the cause of the pervasive crackling sounds (Johnson et al., 1947).

Individual snapping shrimp (Figure 1), sometimes referred to as pistol shrimp, measure smaller than a few centimeters, but produce one of the loudest of all sounds in nature using a specialized snapping claw. The high intensity sound is actually the result of a bubble popping when the claw is closed at incredibly high speed, creating not only the characteristic “snap” sound but also a flash of light and extremely high temperature, all in a fraction of a millisecond (Versluis et al., 2000). Because these shrimp form large, dense aggregations, living unseen within reefs and rocky habitats, the combination of individual snaps creates the consistent crackling sound familiar to mariners. Snapping is used by shrimp for defense and territorial interactions, but likely serves other unknown functions based on our recent studies.

Figure 1. Images of the species of snapping shrimp, Alpheus heterochaelis, we are using to test hypotheses in the lab. This is the dominant species of snapping shrimp found coastally in the Southeast United States, but there are hundreds of different species worldwide, easily identified by their relatively large snapping claw.

Since snapping shrimp produce the dominant sound in many marine regions, changes in their activity or population substantially alters ambient sound levels at a given location or time. This means that the behavior of snapping shrimp exerts an outsized influence on the sensory environment for a variety of marine animals, and has implications for the use of underwater sound by humans (e.g., harbor defense, submarine detection). Despite this fundamental contribution to the acoustic environment of temperate and coral reefs, relatively little is known about snapping shrimp sound patterns, and the underlying behaviors or environmental influences. So essentially, we ask the question: what is all the snapping about?

Figure 2 (missing). Photo showing an underwater acoustic recorder deployed in a coral reef setting. Recorders can be left to record sound samples at scheduled times (e.g. every 10 minutes) so that we can examine the long-term temporal trends in snapping shrimp acoustic activity on the reef.

Recent advances in underwater recording technology and interest in passive acoustic monitoring have aided our efforts to sample marine soundscapes more thoroughly (Figure 2), and we are discovering complex dynamics in snapping shrimp sound production. We collected long-term underwater recordings in several Caribbean coral reef systems and analyzed the snapping shrimp snap rates. Our soundscape data show that snap rates generally exhibit daily rhythms (Figure 3), but that these rhythms can vary over short spatial scales (e.g., opposite patterns between nearby reefs) and shift substantially over time (e.g., daytime versus nighttime snapping during different seasons). These acoustic patterns relate to environmental variables such as temperature, light, and dissolved oxygen, as well as individual shrimp behaviors themselves.

Figure 3. Time-series of snap rates detected on two nearby USVI coral reefs for a week-long recording period. Snapping shrimp were previously thought to consistently snap more during the night, but we found in this study location that shrimp were more active during the day, with strong dawn and dusk peaks at one of the sites. This pattern conflicts with what little is known about snapping behaviors and is motivating further studies of why they snap.

The relationships between environment, behaviors, and sound production by snapping shrimp are really only beginning to be explored. By listening in on coral reefs, our work is uncovering intriguing patterns that suggest a far more complex picture of the role of snapping shrimp in these ecosystems, as well as the role of snapping for the shrimp themselves. Learning more about the diverse habits and lifestyles of snapping shrimp species is critical to better predicting and understanding variation in this dominant sound source, and has far-reaching implications for marine ecosystems and human applications of underwater sound.

References

Johnson, M. W., F. Alton Everest, and Young, R. W. (1947). “The role of snapping shrimp (Crangon and Synalpheus) in the production of underwater noise in the sea,” Biol. Bull. 93, 122–138.

Versluis, M., Schmitz, B., von der Heydt, A., and Lohse, D. (2000). “How snapping shrimp snap: through cavitating bubbles,” Science, 289, 2114–2117. doi:10.1126/science.289.5487.2114

2aABa3 – Indris’ melodies are individually distinctive and genetically driven

Marco Gamba – marco.gamba@unito.it
Cristina Giacoma – cristina.giacoma@unito.it

University of Torino
Department of Life Sciences and Systems Biology
Via Accademia Albertina 13
10123 Torino, Italy

Popular version of paper 2aABa3 “Melody in my head, melody in my genes? Acoustic similarity, individuality and genetic relatedness in the indris of Eastern Madagascar”
Presented Tuesday morning, November 29, 2016
172nd ASA Meeting, Honolulu

Human hearing ablities are exceptional at identifying the voices of friends and relatives [1]. The potential for this identification lies in the acoustic structures of our words, which not only convey verbal information (the meaning of our words) but also non-verbal cues (such as sex and identity of the speakers).

In animal communication, the recognizing a member of the same species can also be important. Birds and mammals may adjust their signals that function for neighbor recognition, and the discrimination between a known neighbor and a stranger would result in strikingly different responses in term of territorial defense [2].

Indris (Indri indri) are the only lemurs that produce group songs and among the few primate species that communicate using articulated singing displays. The most distinctive portions of the indris’ song are called descending phrases, consisting of between two and five units or notes. We recorded 21 groups of indris in the Eastern rainforests of Madagascar from 2005 to 2015. In each recording, we identified individuals using natural markings. We noticed that group encounters were rare, and hypothesized that song might play a role in providing members of the same species with information about the sex and identity of an individual singer and the emitting group.

Figure 1. A female indri with offspring in the Maromizaha Forest, Madagascar. Maromizaha is a New Protected Area located in the Region Alaotra-Mangoro, east of Madagascar. It is managed by GERP (Primate Studies and Research Group). At least 13 species of lemurs have been observed in the area.

We found we could effectively discriminate between the descending phrases of an individual indris, showing they have the potential for advertising about sex and individual identity. This strengthened the hypothesis that song may play a role in processes like kinship and mate recognition. Finding that there is was degree of group specificity in the song also supports the idea that neighbor-stranger recognition is also important in the indris and that the song may function announcing territorial occupation and spacing.

Figure 2. Spectrograms of an indri song showing a typical sequence of different units. In the enlarged area, the pitch contour in red shows a typical “descending phrase” of 4 units. The indris also emit phrases of 2, 3 and more rarely 5 or 6 units.

Traditionally, primate songs are considered an example of a genetically determined display. Thus the following step in our research was to examine whether the structure of the phrases could relate to the genetic relatedness of the indris. We found a significant correlation between the genetic relatedness of the studied individuals and the acoustic similarity of their song phrases. This suggested that genetic relatedness may play a role in determining song similarity.

For the first time, we found evidence that the similarity of a primate vocal display changes within a population in a way that is strongly associated with kin. When examining differences between sexes we found that male offspring showed phrases that were more similar to their fathers, while daughters did not show similarity with any of their parents.

Figure 3. A 3d-plot of the dimensions (DF1, DF2, DF3) generated from a Discriminant model that successfully assigned descending phrases of four units (DP4) to the emitter. Colours denote individuals. The descending phrases of two (DP2) and three units (DP3) also showed a percentage of correct classification rate significantly above chance.

The potential for kin detection may play a vital role in determining relationships within a population, regulating dispersal, and avoiding inbreeding. Singing displays may advertise kin to signal against potential mating, information that females, and to a lesser degree males, can use when forming a new group. Unfortunately, we still do not know whether indris can perceptually decode this information or how they use it in their everyday life. But work like this sets the basis for understanding primates’ mating and social systems and lays the foundation for better conservation methods.

Belin, P. Voice processing in human and non-human primates. Philosophical Transactions of the Royal Society B: Biological Sciences, 2006. 361: p. 2091-2107.
Randall, J. A. Discrimination of foot drumming signatures by kangaroo rats, Dipodomys spectabilis. Animal Behaviour, 1994. 47: p. 45-54.
Gamba, M., Torti, V., Estienne, V., Randrianarison, R. M., Valente, D., Rovara, P., Giacoma, C. The Indris Have Got Rhythm! Timing and Pitch Variation of a Primate Song Examined between Sexes and Age Classes. Frontiers in Neuroscience, 2016. 10: p. 249.
Torti, V., Gamba, M., Rabemananjara, Z. H., Giacoma, C. The songs of the indris (Mammalia: Primates: Indridae): contextual variation in the long-distance calls of a lemur. Italian Journal of Zoology, 2013. 80, 4.
Barelli, C., Mundry, R., Heistermann, M., Hammerschmidt, K. Cues to androgen and quality in male gibbon songs. PLoS ONE, 2013. 8: e82748.

Tags:
-Animals
-Melody
-Biology

3pAB4 – Automatic classification of fish sounds for environmental purposes

Marielle MALFANTE – marielle.malfante@gipsa-lab.grenoble-inp.fr
Jérôme MARS – jerome.mars@gipsa-lab.grenoble-inp.fr
Mauro DALLA MURA – mauro.dalla-mura@gipsa-lab.grenoble-inp.fr
Cédric GERVAISE – cedric.gervaise@gipsa-lab.grenoble-inp.fr

GIPSA-Lab
Université Grenoble Alpes (UGA)
11 rue des Mathématiques
38402 Saint Martin d’Hères (GRENOBLE)
FRANCE

Popular version of paper 3pAB4 “Automatic fish sounds classification”
Presented Wednesday afternoon, May 25, 2016, 2:15 in Salon I
171^st ASA Meeting, Salt Lake City

In the current context of global warming and environmental concern, we need tools to evaluate and monitor the evolution of our environment. The evolution of animal populations is of a special concern in order to prevent changes of behaviour under environmental stress and to preserve biodiversity. Monitoring animal populations however, can be a complex and costly task. Experts can either (1) monitor animal populations directly on the field, or (2) use sensors to gather data on the field (audio or video recordings, trackers, etc.) and then process those data to retrieve knowledge about the animal population. In both cases the issue is the same: experts are needed and can only process limited quantity of data.

An alternative idea would be to keep using the field sensors but to build software tools in order to automatically process the data, thereby allowing monitoring animal populations on larger geographic areas and for extensive time periods.

The work we present is about automatically monitoring fish populations using audio recordings. Sounds have a better propagation underwater: by recording sounds under the sea we can gather loads of information about the environment and animal species it shelters. Here is an example of such recordings:

Legend: Raw recording of fish sounds, August 2014, Corsica, France.

Regarding fish populations, we distinguish four types of sounds that we call (1) Impulsions, (2) Roars, (3) Drums and (4) Quacks. We can hear them in the previous recording, but here are some extracts with isolated examples:

Legend: Filtered recording of fish sounds to hear Roar between 5s and 13s and Drums between 22s to 29s and 42s to 49s.

Legend: Filtered recording of fish sounds to hear Quacks and Impulsions. Both sounds are quite short (<0.5s) and are heard all along the recording.

However, to make a computer automatically classify a fish sound into one of those four groups is a very complex task. A simple or intuitive task for humans is often extremely complex for a computer, and vice versa. This is because humans and computers process information in different ways. For instance, a computer is very successful at solving complex calculations and at performing repetitive tasks, but it is very difficult to make a computer recognize a car in a picture. Humans however, tend to struggle with complex calculations but can very easily recognise objects in images. How do you explain a computer ‘this is a car’? It has four wheels. But then, how do you know this is a wheel? Well, it has a circular shape. Oh, so this ball is a wheel, isn’t it?

This easy task for a human is very complex for a machine. Scientists found a solution to make a computer understand what we call ‘high-level concepts’ (recognising objects in pictures, understanding speech, etc.). They designed algorithms called Machine Learning. The idea is to give a computer a lot of examples of each concept we want to teach it. For instance, to make a computer recognise a car in a picture, we feed it with many pictures of cars so that it can learn what a car is, and with many pictures without cars so that it can learn what a car is not. Many companies such as Facebook, Google, or Apple use those algorithms for face recognition, speech understanding, individualised advertisement, etc. It works very well.

In our work, we use the same technics to teach a computer to recognize and automatically classify fish sounds. Once those sounds have been classified, we can study their evolutions and see if fish populations behave differently from place to place, or if their behaviours evolve with time. It is also possible to study their density and see if their numbers vary through time.

This work is of a particular interest since to our knowledge, we present the first tool to automatically classify fish sounds. One of the main challenges is to make a sound understandable by a computer.that is to find and extract relevant information in the acoustic signal. By doing that, it gets easier for the computer to understand similarities and differences between all signals and in the end of the day, to be able to predict to which group a sound belongs.

Legend: How to build an automatic fish sounds classifier? Illustration.

3aAB7 – Construction Noise Impact on Wild Birds

Pasquale Bottalico, PhD. – pb@msu.edu

Voice Biomechanics and Acoustics Laboratory
Department of Communicative Sciences and Disorders
College of Communication Arts & Sciences
Michigan State University
1026 Red Cedar Road
East Lansing, MI 48824

Popular version of paper 3aAB7, “Construction noise impact on wild birds”
Presented Tuesday morning, May 25, 2016, 10:20, Salon I
171st ASA Meeting, Salt Lake City

Content
Almost all bird species use acoustic signals to communicate or recognize biological signals – to mate, to detect the sounds of predators and/or prey, to perform mate selection, to defend their territory, and to perform social activities. Noise generated from human activities (in particular by infrastructure and construction sites) has a strong impact on the physiology and behaviour of birds. In this work, a quantitative method for evaluating the impact of noise on wild birds is proposed. The method combines the results of previous studies that considered the effect of noise on birds and involved noise mapping evaluations. A forecast noise simulation was used to generate maps of (1) masking-annoyance areas and (2) potential density variation.

An example of application of the masking-annoyance areas method is shown in Figure 1. If a bird is in the Zone 1 (in purple), traffic noise and construction noise can potentially result in hearing loss and threshold shift. A temporary elevation of the bird’s hearing threshold and a masking of important communication signals can occur in the Zone 2 (in red). Zone 3 (in orange), 4 (in yellow) and 5 (in light green) are characterized by a high, medium and low level of signal masking, respectively. Once the level of noise generated by human activities falls below ambient noise levels in the critical frequencies for communication (2–8 kHz), masking of communication signals is no longer an issue. However, low-frequency noise, such as the rumble of a truck, may still potentially cause other behavioural and/or physiological effects (Zone 6, in green). No effects of any kind occur on the birds in Zone 7 (in dark green). The roles for Zone definition are based on the results of Dooling and Popper. [1]

Figure 1 Mapping of the interaction areas of noise effect on birds within the 7 zones for a project without (a) and with mitigations (b).

Waterman et al. [2] and Reijnem et al. [3-4-5] proposed a trend of the potential variation in birds density in relationship with the noise levels present in the area. This trend shows no effect on density when the noise levels are lower than 45 dB(A), while there is a rapid decrease (with a quadratic shape) for higher levels. An example of the potential decrease in bird density for a project with and without mitigations is shown in Figure 2. The blue areas are the areas where the birds’ density is not influenced by the noise, while the red ones are the areas from where the birds are leaving because the noise levels are too high.

This methodology permits a localization of the areas with greater impacts on birds. The mitigation interventions should be focused on these areas in order to balance bird habitat conservation and human use of land.

Figure 2 Potential decrease in bird density for a project without (a) and with mitigations (b).