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Talk given in MICAI 2013: Emotion based features of bird singing for Turdus migratorius identification
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Emotion based features of bird singing for Turdus migratorius identification
Toaki Villarreal, Caleb Rascón and Ivan MezaUniversidad Nacional Autónoma de México
http://golem.iimas.unam.mx/
GrupoGolem
Motivation
● Ecosystems change constantly and with this its inhabitants
● In the case of birds, to understand such changes specialists monitor populations
● Bioacoustics monitors by means of the singing of birds
Challenges
● It is time consuming
● It requires a specialist
● Recording área limited
On the other hand
Automatic emotion recognition in speech has the goal:
With a recording of speech recognise the emotional state of the speaker
Our goal
With a recording of bird singing recognise the bird species
*We are interested in the bird species, not the emotional state of the bird
Proposal
Develop a monitoring system for birds based on what we know for emotion recognition
At this point:
● We focus on Turdus migratorius● It has to be live and had a good performance● Module of Acoustic Identification of Birds
Bird songs
Emotion based: procedure
● Identify a candidate composed by multiple frames (usually turns in a conversation)
● Extract a representation per frame
● Use additive functions to represent the segment as a vector
● Use a classification technique
For the birds
● Energy based sound activity, syllabifier
● Standard MFCC (13 valores), 1st derivation
● Mean, Std. var, 1st, 2nd and 3rd , min, max, skewness, kurtosis
● Support Vector Machine
The system
MIAA
Evaluation performance
Segment based evaluation, recordings with only Turdus migratorius, GS labellings available
Precision 87.49%
Recall 75.15%
F1-score 78.30%
Precision 73.94%
Recall 64.64%
F1-score 67.34%
Syllabifier Classifier
What about other species?
Segment based evaluation, no GSSix species: Turdus rufopalliatus, Myadestes occidentalis, Thryomanes bewickii, Cardinalis cardinalis and Toxostoma curvirostre
Precision 83.23%
Recall 83.23%
F1-score 83.23%
Experiments
Which features are more helpful?
Conclusions
● We were able to identify the Turdus migratorius
● Our approach is inspired by emotion based
identification systems
● We showed that MFCC are good enough
● The module is functional and works live on
the MIAA module
● … lots of future work
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