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Bird task



The general public as well as professionals like park rangers, ecology consultants, fishers or, of course, the ornithologists themselves might actually be users of an automated bird identifying system, typically in the context of wider initiatives related to ecological surveillance or biodiversity conservation. Using audio records rather than bird pictures is justified by current practices. Birds are actually not easy to photograph as they are most of the time hidden, perched high in a tree or frightened by human presence, whereas audio calls and songs have proved to be easier to collect and much more discriminant. The organization of this task is supported by Xeno-Canto foundation for nature sounds and the French projects Pl@ntNet (INRIA, CIRAD, Tela Botanica) and SABIOD Mastodons.

Chorus      PlantNet     SABIOD Mastodons

Task overview

The task will be focused on bird identification based on different types of audio records over 501 species from South America centered on Brazil. Additional information includes contextual meta-data (author, date, locality name, comment, quality rates). The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Xeno-canto, an international social network of amateur and expert ornithologists. This makes the task closer to the conditions of a real-world application: (i) audio records of the same species are coming from distinct birds living in distinct areas (ii) audio records by different users that might not used the same combination of microphones and portable recorders (iii) audio records are taken at different periods in the year and different hours of a day involving different background noise (other bird species, insect chirping, etc).


The dataset will be built from the outstanding Xeno-canto collaborative database ( involving at the time of writing more than 140k audio records covering 8700 bird species observed all around the world thanks to the active work of more than 1400 contributors.

The subset of Xeno-canto data that will be used for the first year of the task will contain 14027 audio recordings belonging to 501 bird species in Brazil area, i.e. the ones having the more recordings in Xeno-canto database. The amount of 501 classes will clearly go one step further current benchmarks (80 species max) and foster brave new techniques. On the other side, the task will remain feasible with current approaches in terms of the number of records per species and the required hardware to process that data. Detailed statistics are the following:
- minimally 15 recordings per species (maximum 91)
- minimally 10 different recordists, maximally 42, per species.

Audio records of the Xeno-canto dataset centered on Brazil (13/11/2013).

Before we release this BirdCLEF2014 dataset, sample records can be easily found on Xeno-canto website:

Please DO NOT download the whole Xeno-canto data yourself by using their API. Their system is actually not calibrated to support such massive access and they will provide us specific links to download the training data. Please also notice that, to allow a fair evaluation, it will be strictly forbidden to use the online resources of Xeno-canto as training data because some of them might be used as queries in the official test set of the task. More generally, it will be forbidden to use any external training data to enrich the provided one. Many Xeno-canto contents are actually circulating freely on the web and we could not guaranty that the data crawled by some participants does not include some of the records we will use in the test set.

Audio records are associated to various metadata such as the type of sound (call, song, alarm, flight, etc.), the date and localization of the observations (from which rich statistics on species distribution can be derived), some textual comments of the authors, multilingual common names and collaborative quality ratings. The available metadata for each recording includes:

- MediaId: the audio record ID
- FileName: the (normalized) audio record filename
- ClassId: the class ID that must be used as ground-truth
- Species the species name
- Genus: the name of the Genus, one level above the Species in the taxonomical hierarchy used by Xeno-canto
- Family: the name of the Family, two levels above the Species in the taxonomical hierarchy used by Xeno-canto
- Sub-species: (if available) the name of the sub-species, one level under the Species in the taxonomical hierarchy used by Xeno-canto
- VernacularNames: (if available) english common name(s)
- BackgroundSpecies: latin (Genus species) names of other audible species eventually mentioned by the recordist
- Date: (if available) the date when the bird was observed
- Time: (if available) the time when the bird was observed
- Quality: the (round up) average of the user ratings on audio record quality
- Locality: (if available) the locality name, most of the time a town
- Latitude & Longitude
- Elevation (altitude in meters)
- Author: name of the author of the record,
- AuthorID: id of the author of the record,
- Audio Content: comma-separated list of sound types such as 'call' or 'song', free-form
- Comments: free comments from the recordists

Audio recordings pre-processing and features extraction

In order to avoid any bias in the evaluation related to the used recording devices, the whole audio data has been normalized by Univ. Toulon Dyni team: normalization of the bandwith / frequency sample to 44.1 kHz, .wav format (16 bits). They also provide audio features for both training and test recordings that could be used by any participant, based on Mel Filter Cepstral Coefficients features optimized for bird calls. They are similar than in the previous bird classification challenges ICML4B 2013 and NIPS4B 2013, allowing interesting comparisons. Therefore, 16 MFCC (first coeff = log energy) are computed on windows of 11.6 ms, each 3.9 ms. We derive their speed and acceleration, yielding to one line of 16*3 features per frame. Scripts are detailed at .

Task description

The task will be evaluated as a bird species retrieval task. A part of the collection will be delivered as a training set available a couple of months before the remaining data is delivered. The goal will be to retrieve the singing species among the top-k returned for each of the undetermined observation of the test set. Participants will be allowed to use any of the provided metadata complementary to the audio content.

Training and test data

As it was mentioned below above-said, the "Background" field in the Metadata may indicate if there are some other species identified in the background like for this observation or not even if Xeno-canto encourage to identify them. Some audio records may also not contain at all a dominant bird species like in this example.
The training dataset will contain only audio records with a dominant bird species, with or without other identified bird species in the background. Participants are free to use these background informations or not.
The test datatest will contain the same type of audio records but with purged background informations and comments which can potentially also include some species names. More precisely, the purged test xml files will only include:
- MediaId: the audio record ID
- FileName: the (normalized) audio record filename
- Date: (if available) the date when the bird was observed
- Time: (if available) the time when the bird was observed
- Locality: (if available) the locality name, most of the time a town
- Latitude & Longitude
- Elevation (altitude in meters)
- Author: name of the author of the record,
- AuthorID: id of the author of the record

The training data finally results in 9688 audio records with complete xml files associated to them.Download link of training data will be sent to participants on 05/02/2014.
The test data results in 4339 audio records with purged xml files.

Run format

The run file must be named as "" where X is the identifier of the run (i.e. 1, 2, 3 or 4). The run file has to contain as much lines as the total number of predictions, with at least one prediction and a maximum of 501 predictions per test audio record (501 being the total number of species). Each prediction item (i.e. each line of the file) has to respect the following format:
< MediaId;ClassId;rank;probability>

Here is a short fake run example respecting this format on only 8 test MediaId:

For each submitted run, please give in the submission system a description of the run. A combobox will specify wether the run was performed fully automatically or with a human assistance in the processing of the queries. Then, a textarea should contain a short description of the used method, for helping differentiating the different runs submitted by the same group, and where we ask to the participants to indicate if they used a method based on
- only AUDIO
For instance:
Only AUDIO, using provided MFFC features, multiple multi-class Support Vector Machines with probabilistic outputs

Optionally, you can add one or several bibtex reference(s) to publication(s) describing the method more in details.


The used metric is the mean Average Precision (mAP), considering each audio file of the test set as a query and computed as:
where Q is the number of test audio files and AveP(q) for a given test file q is computed as:
where k is the rank in the sequence of returned species, n is the total number of returned species, P(k) is the precision at cut-off k in the list and rel(k) is an indicator function equaling 1 if the item at rank k is a relevant species (i.e. one of the species in the ground truth).

How to register for the task

LifeCLEF will use the ImageCLEF registration interface. Here you can choose a user name and a password. This registration interface is for example used for the submission of runs. If you already have a login from the former ImageCLEF benchmarks you can migrate it to LifeCLEF 2014 here


  • 01.12.2013: Registration opens (register here)
  • 05.02.2014: training data release
  • 15.04.2014: test data release
  • 08.05.201415.05.2014: deadline for submission of runs
  • 15.05.201422.05.2014:release of results
  • 07.06.2014: deadline for submission of working notes
  • 09.2014: CLEF 2014 Conference (Sheffield, UK)

Frequently Asked Questions

Quality tag in XML
It ranges from 1-5, with 1 being best quality and 5 being worst quality, correct? 0 means unknown rating.

What is the maximum allowed number of submissions?
The number of submissions is 4.

How is the classID field created? Can we relate genus and species information with class ID?
There is no obvious rule for relating the genus and species information in the class ID.

Run format : What is the rank field in the submission format?
The field is the most important field since it will be used as the main key to sort species and compute the final metric which is based on the Mean Average Precision. The scenario expressed in this challenge is more related to a species retrieval system, where a user browse a result list of relevant species, than a "pure" classification problem where a system have to predict the correct species. Then the challenge for each query is to put as much as possible the correct species at the top of the result list.

Does every participant have to submit working notes for the conference?
We warmly encourage participants to submit working notes in order to make more credible the results obtained within the bioacoustic community.


A total of 10 participating groups submitted 29 runs. Thanks to all of you for your efforts and your constructive feedbacks regarding the organization.


Run name Run filename Type MAP 1 (with Background Species) MAP 2 (without Background Species)
MNB TSA Run 3 1400191117781__MarioTsaBerlin_run3 AUDIO & METADATA 0,453 0,511
MNB TSA Run 1 1400189639693__MarioTsaBerlin_run1 AUDIO & METADATA 0,451 0,509
MNB TSA Run 4 1400187879882__MarioTsaBerlin_run4 AUDIO & METADATA 0,449 0,504
MNB TSA Run 2 1400155761267__MarioTsaBerlin_run2 AUDIO & METADATA 0,437 0,492
QMUL Run 3 1398844676710__danstowell_run3 AUDIO 0,355 0,429
QMUL Run 4 1399057195244__danstowell_run4 AUDIO 0,345 0,414
QMUL Run 2 1398353349310__danstowell_run2 AUDIO 0,325 0,389
QMUL Run 1 1398351214959__danstowell_run1 AUDIO 0,308 0,369
INRIA Zenith Run 2 1398872637337__Run2-INRIA-Julien-Alexis-withMetadata AUDIO & METADATA 0,317 0,365
INRIA Zenith Run 1 1398427280302__Run1-INRIA-Julien-Alexis AUDIO 0,281 0,328
HLT Run 3 1399880572021__combined AUDIO & METADATA 0,289 0,272
HLT Run 2 1399880101403__run10 AUDIO & METADATA 0,284 0,267
HLT Run 1 1399429313760__jonfull AUDIO & METADATA 0,166 0,159
BirdSPec Run 2 1400164626383__BiRdSPec_run2 AUDIO 0,119 0,144
Utrecht Univ. Run 1 1400099635524__clef_240350350v AUDIO 0,123 0,14
Golem Run 1 1400173600537__golem_run1 AUDIO 0,105 0,129
Golem Run 2 1400200584201__golem_run2 AUDIO 0,104 0,128
BirdSPec Run 1 1400163565260__BiRdSPec_run1 AUDIO 0,08 0,092
BirdSPec Run 4 1400189292631__BiRdSPec_run4 AUDIO 0,074 0,089
Golem Run 3 1400200893582__golem_run3 AUDIO 0,074 0,089
BirdSPec Run 3 1400165270133__BiRdSPec_run3 AUDIO 0,062 0,075
Yellow Jackets Run 1 1399489750608__Aneesh_run_1 AUDIO 0,003 0,003
Randall Run 1 1400270035158__RandallDylan_run1 AUDIO 0,002 0,002
SCS Run 1 1398890068906__scs_run1 AUDIO 0 0
SCS Run 2 1399201734708__scs_run2 AUDIO 0 0
SCS Run 3 1399834505835__scs_run3 AUDIO 0 0
Perfect Main Species perfectRunMainLabel AUDIO 0,784 1
Perfect Main & Bacground Species perfectRunMultiLabel AUDIO 1 0,868
Random Main Species randomRunMonoLabel AUDIO 0,003 0,003



Main contact:

  • Hervé Glotin, University of Toulon, France, glotin(replace-by-an-arrobe)
  • Hervé Goëau, Inria, France, herve.goeau(replace-by-an-arrobe)
  • Andreas Rauber, TU Wien, Austria, rauber(replace-by-an-arrobe)
  • Willem-Pier Vellinga, Xeno-Canto foundation for nature sounds, The Netherlands, wp(replace-by-an-arrobe)