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The Xeno-canto collection and its relation to
sound recognition and classification
Willem-Pier Vellinga1and Robert Planqu´e1
Stichting Xeno-canto voor natuurgeluiden (Xeno-canto Foundation), The Netherlands
{wp,bob}@xeno-canto.org
Abstract. This paper discusses distinguishing characteristics of the Xeno-
canto bird sound collection. The main aim is to indicate the relation
between automated recognition of bird sounds (or feature recognition in
digital recordings more generally) and curating large bioacoustics col-
lections. Not only do large collections make it easier to design robust
algorithmic approaches to automated species classifiers, those same al-
gorithms should also become useful in determining the actual content of
the collections.
Keywords: LifeCLEF2015, BirdCLEF2015, Xeno-canto, bird sounds, automated
recognition, citizen science, data mining.
1 Introduction
For the past two years the BirdCLEF challenge [1,2], part of the LifeCLEF
workshops [3,4], has been based on sounds from Xeno-canto. Xeno-canto (XC)
aims to popularise bird sound recording, to improve accessibility of bird sounds,
and to increase knowledge of bird sounds. It tries to achieve these aims by
facilitating and curating a collaborative, shared, global bird sound collection on
www.xeno-canto.org. The collection was initiated by the authors in 2005 [5].
When XC started out it was mainly a project to aid identification of small
collections of bird sounds made by the authors in tropical forests in Peru and
Ecuador. Identifying species by sound using the means available at the time,
mostly commercial cassette tapes or CDs with up to a hundred recordings, was
cumbersome and many sounds were simply not available. (For a discussion see
[6]).
Sjoerd Mayer’s “Birds of Bolivia” CD-ROM’s [7,8] were an inspiration. They
increased the number of sounds available and species represented by an order of
magnitude, made navigation of the sounds much easier, mapped locations, and
identified background species on a recording. Mayer also engaged the birding
community by welcoming and crediting contributions of sounds by birders and
published corrections of errors on his website.
The authors essentially took these concepts a step further, and designed and
constructed an interface to a non-commercial, open database situated on the
world wide web. A number of guiding principles were formulated that distin-
guished XC from other sound collections at the time:
–Anyone with web access is invited to upload sounds. XC does not refuse
recordings. Contributors can share any bird sound they find interesting, pro-
vided they are below a fixed maximum size (initially 1 MB, now 10 MB) and
provided a required minimum set of metadata is given: species, recordist
name, location name, country, recording date, time of day, elevation, and
sound type(s). This system certainly has drawbacks: a considerable fraction
of the recordings is short, of dodgy quality, or both. Still, such recordings
may be useful. They may represent poorly known locations or vocalisations,
or may simply contribute to the sample size of individual species. Also, in
the context of automated species identification algorithms, it is clear that
any real-life deployment of such an algorithm would have to deal with poor
quality recordings as well.
–The recordings uploaded to XC are shared. Re-use of the recordings is in-
tended, for purposes that are in line with the aims of XC, such as download-
ing to personal collections, embedding sounds in educational or personal web
sites, use for scientific research, etcetera. The Creative Commons licenses
(http://creativecommons.org/) offer a useful framework. After consulta-
tion with the community it was decided to settle for CC-BY-ND-NC (attri-
bution, no-derivatives, non-commercial) licenses. Since this is in fact a rather
restrictive license, nowadays one can also choose CC-BY-NC-SA (SA stands
for share-alike) and CC-BY-SA licenses that allow more liberal re-use. In all
cases attribution of the author/contributor on republication is mandatory.
For discussion of the limits of the other terms, see the Creative Commons
website. The XC website code is written in free, open source software. It
is based on a standard LAMP (Linux, Apache, MySQL, PHP) set-up, with
some additional software written to show sonograms, implement mapping,
and so on.
–Anyone can contribute to the collection in some way. Apart from sharing
recordings, people may contribute expertise on identification, set identifica-
tion challenges, offer experience with equipment, write articles on-site, or
just comment on recording achievements.
–Anyone can challenge an identification (ID) on the site. The vast majority of
recordings have been identified correctly to species by the recordist, but er-
rors are inevitable. When challenged, the recording is set aside and does not
appear in search results until the ID is resolved by the community. This is
usually done in an open discussion on the forum. If the ID is agreed upon, the
recording is put back into the collection by the administrators. The admin-
istrators therefore have the role of arbiters, rather than authorities, and in
fact there are no designated authorities that decide on species identification.
This is one of the more uncommon features of Xeno-canto, and in this sense
it differs from other well-known community projects on natural history, such
as eBird (ebird.org), Observado (waarneming.nl / observado.org).
At present, May 2015, the XC collection contains some 243,000 recordings
from over 9,300 bird species, shared by more than 2400 contributors from all
over the world. In the rest of this paper, the development and current status of
the XC collection are illustrated and a few points relevant to its relation with
automatic sound classification and recognition are discussed.
Fig. 1. Cumulative number of contributors over time.
2 Characteristics of the collection
The growth of XC is illustrated in Figures 1 and 2, by plotting the number of
recordings and the number of contributors over time. Two things are noteworthy.
Firstly, the data for the initial period is incomplete, since the uploading dates
were initially not recorded. Secondly, there are pronounced seasonal effects, most
obvious in the number of contributors. These points are remedied to some extent
in subsequent figures by plotting versus the number of recordings instead of
versus time.
2.1 Contributors
Both the number of recordings and the number of contributors grow at increas-
ing rates. Remarkably, plotting the number of recordings versus the number of
contributors shows that they have consistently increased at approximately the
same rate. See Figure 3. This leads to a more or less constant average number of
recordings per contributor, which turns out to be about 100. However, it should
be noted that the distribution of recordings per contributor is very broad and
skewed. At this moment 298 contributors contribute more than 100 recordings,
and many more contributors, around 2100, contribute less than 100 recordings.
Fig. 2. Cumulative number of recordings over time.
The three largest contributions each comprise more than 10000 recordings, more
than 100 times the average; 729 contributors contributed 1 recording, 100 times
less than the average. The Zipf-like plots in Figure 4 serve to characterise the
distribution at various stages during the development of XC.
2.2 Species
When a sound is uploaded a set of metadata is required, among which the name
of the species. Specifying the subspecies is optional. The taxonomy of the site
was initially based on the taxonomy in Neotropical Birds [11]. Other regions
were added over time (North-America, Africa, Asia, Europe and Australasia)
using other local taxonomies, which lead to problems with species occurring in
several regions. In 2011 the global IOC (International Ornithological Council)
taxonomy was adopted for all recordings and XC currently uses version 4.1 [12].
The constant revision of taxonomy at the species level means that the species
assignment of the recordings needs to be updated frequently. This task falls to
the team of administrators. Splits can be problematic, since the subspecific taxon
to which a recording belongs may not be indicated (see below).
IOC 4.1 recognises 10,518 extant species and 150 extinct species; to this list
XC has added 16 additional recently described or as yet undescribed species.
About 9330 are represented in XC at this moment. To our best knowledge this
constitutes the largest number of species in any public collection of bird sounds.
(There is at least one private collection that has more species, but it includes
sounds of all species from XC.)
The growth of the number of species may provide a clue about the moment
of completion of the collection at the species level. Figure 5 shows the species
Fig. 3. Order of contributor versus number of first recording by that contributor. To
a reasonable approximation the increase is linear the slope indicating that every con-
tributor adds about 100 recordings on average.
Fig. 4. Distribution of the number of recordings per recordist plotted in a Zipf plot after
2000, 5000, 20000, 50000 and 200000 recordings. These plots show that the distribution
of the number of recordings per contributor is very wide.
Fig. 5. Species accumulation curve (blue dots) and randomised species acccumulation
curve (brown dots). The drawn line is an extrapolation shown further in figure 8.
accumulation curve up to may 2015, together with a randomised accumulation
curve and a fit used for extrapolation. The randomised version is based on a
random draw from all recordings present in XC. Clearly the two curves differ
significantly. This is caused by the fact that XC started out with only Neotropical
species, and that other world areas were added later. The randomised species
accumulation curve does not take that into account. The two curves are seen to
meet up after about 170000 recordings, well after XC went global.
For any number of reasons (abundance and size range, vocal (in)activity,
accessibility of the range of the species, accessibility of the site to name just
four) the recordings are not evenly distributed across the species. The current
expectation value for the number of recordings per species is around 20. However
some 20 species have attracted over 500 recordings, while around 1200 are still
waiting to be uploaded. The distribution plotted in Zipf-fashion is shown in
Figure 6, probability densities are shown in Figure 7.
The species abundance curves can be extrapolated into the future by making
assumptions on the probability that species that are not represented at this time
will be uploaded. A reasonable fit is achieved by assuming that the probability of
a new species being uploaded is 1/3 of that of a species with 1 recording in XC,
with the ratios between probabilities of species already represented remaining
equal. An extrapolation based on that assumption is shown in Figure 8. Of
course the extrapolation follows the randomised species abundance curve very
well. The extrapolation is shown up to 900,000 recordings, at which point it is
still about 600 species shy of the total number of species. The precise number
will depend on the assumptions made, but it seems reasonable to assume that
completion at the species level will take a multiple of the number of recordings
present at this moment.
Fig. 6. Distribution of the number of recordings per species plotted in a Zipf plot after
2000, 5000, 20000, 50000 and 200000 recordings.
Fig. 7. Probability densities of species after 2000, 5000, 20000, 50000 and 200000
recordings.
Fig. 8. Extrapolation of species accumulation curve assuming that as yet unrepresented
species have a probability of being uploaded that is 1/3 that of a species represented
with 1 recording. It is likely the collection needs to multiply in size before completion
at the species level is reached.
3 Linking bird song databases and automated species
recognition
The BirdCLEF workshop requires entrants to identify recordings from the XC
collection to species level based on the species level identification provided by
the XC community. It is worthwhile to have another look at the species level
IDs in XC. For a number of reasons the ID to species level, even if correct, may
be misleading.
– Presence of unnamed background species Although recordists are asked
to mention the background species present in the recordings, not all recordists
do so. On average 2 species are identified per recording, but it is certain that
many more could be identified. Interestingly, the presence of named back-
ground species helps humans to identify a sound of interest (as the authors
know from personal experience), but this does not seem to lead to a higher
rate of identification in the algorithmic identifications [1,2].
– Hidden diversity The IOC 4.1 species list not only recognises 10668 species,
but identifies another 20976 subspecies for 5093 of species, bringing the to-
tal number of taxa to 26551. On XC about 9330 species and 9140 additional
subspecies have been identified. This does not mean that 18470 taxa are
represented. It is likely that some recordings represent subspecies that have
not been named now. This means that the currently recognised number is an
underestimate. But it is also likely that in some cases the taxa represented
by recordings without subspecific ID are in fact already named, which would
lead to an overestimate. Of the 9300 species present on XC 4484 are mono-
typic. The 9100 subspecies therefore belong to about 4900 species adding
at least 4200 taxa. The maximum number of named taxa represented is
therefore 18400 and the minimum number 13500. An estimate based on the
number of species present (9300/10668)*26551 would lead to about 23000
taxa present at this time. Based on this estimate it seems likely that a con-
siderable number of taxa remains to be named on XC. At the same time this
also means that the species category may represent considerable taxonomic
diversity. It is to be expected that such diversity hidden within species on XC
is reflected in the sounds, since many subspecies are known to have distinct
vocalizations [9,10].
Other contributions to the diversity of sounds which do not necessarily align
with subspecies categories are geographical dialects, such as in Yellowhammer
(Emberiza citrinella), and the size of the vocabulary of a species, such as in
Common Nightingale (Luscinia megarhynchos). Little quantitative information
is available on the extent of dialect formation and the size of the vocabulary
across the range of the overwhelming majority of the 10518 species of birds.
Apparently the effect of such diversity at the species level on the results of auto-
matic recognition has not been quantified yet. Intuitively, given a set of training
data, one would expect a species that shows little diversity to be recognised more
faithfully than a species that shows a lot of variability.
In [1] it was concluded there that the recognition algorithms worked better
on average for species with more recordings in the training set. It would be
interesting to look for correlations with the number of subspecies recognised, or
the known size of vocabulary.
4 Conclusion
The results from the 2014 and 2015 BirdCLEF challenges offer an interesting
perspective on the use of automated algorithmic techniques on the one hand,
and large accessible public archives of sound data on the other.
At present, the focus in the challenges lies squarely in the field of automated
recognition, and understandably so. The large Xeno-canto database has been
the basis of the challenges, and give the first general insights in automated
feature extraction and classification to species level for general vocalizations.
The species set included in the latest 2015 edition spans 1000 species with a
huge range of different types of bird songs and calls. The BirdCLEF paper in
this volume contributes to our understanding which techniques excel at this type
of challenge.
We would welcome a second application of the algorithms, however, one that
would allow a deeper insight into the variety of vocalizations actually repre-
sented in archives such as Xeno-canto. There is great potential for collaborative
projects, in which estimates would be computed of a number of statistics. Ex-
ample include (a) estimates of repertoire sizes in song birds (or other taxa); (b)
discovery of subspecies with different vocal signatures; (c) the ability to extract
a small representative sample of different vocalizations for focal species, or fo-
cal localities. We hope that we may attract the computer science community to
work with us to start to address these types of challenges.
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