Published on March 1, 2014
Emotion based features of bird singing for Turdus migratorius identification Toaki Villarreal, Caleb Rascón and Ivan Meza Universidad 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
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
Evaluation performance Precision 87.49% Precision 73.94% Recall 75.15% Recall 64.64% F1-score 78.30% F1-score 67.34% Syllabifier Classifier Segment based evaluation, recordings with only Turdus migratorius, GS labellings available
What about other species? Precision 83.23% Recall 83.23% F1-score 83.23% Segment based evaluation, no GS Six species: Turdus rufopalliatus, Myadestes occidentalis, Thryomanes bewickii, Cardinalis cardinalis and Toxostoma curvirostre
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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