Micai13 turdus migratorius (2)

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Information about Micai13 turdus migratorius (2)

Published on March 1, 2014

Author: GrupoGolem

Source: slideshare.net


Talk given in MICAI 2013: Emotion based features of bird singing for Turdus migratorius identification

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

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


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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