Unsupervised Segmentation and Classification of Orca ...

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Motivation• Apply technologies from Automatic Speech Recognition (ASR)to animal communication research• Pitch (i. e. perceived fundamental frequency)• Important carrier of information• Difficult to process (octave ambiguity, half-blown, noise)• Standard approach: Extract estimate of pitch contour• Pitch estimates inherently error-prone→ explicit pitch estimation may be suboptimal!• Try a recently introduced implicit representation of pitchon orca vocalizations:Fundamental Frequency Variation (FFV) SpectrumJune 12th, 2013 | F. Hönig et al. | Pattern Recognition Lab | Segmentation and Classification using Fundamental Frequency Variation 2
Outline• Architecture of a classical ASR system• Acoustic frontend• Data• Mel frequency cepstral coefficients (MFCC)• Fundamental frequency variation (FFV) spectrum• Experiments• Unsupervised Segmentation• Supervised ClassificationJune 12th, 2013 | F. Hönig et al. | Pattern Recognition Lab | Segmentation and Classification using Fundamental Frequency Variation 3
Page 1 and 2: Unsupervised Segmentation andClassi
Page 3: Motivation• Apply technologies fr
Page 8 and 9: What Kind of Problem is Speech Reco
Page 10 and 11: What Kind of Problem is Speech Reco
Page 12 and 13: Divide-and-Conquer Approach (1)•
Page 24 and 25: Parameter Estimation• Ingredients
Page 32 and 33: Classical Acoustic Frontent:Mel Fre
Page 34 and 35: frequency (kHz)7.06.05.04.03.02.01.
Page 36 and 37: frequency (kHz)7.06.05.04.03.02.01.
Page 38 and 39: delta pitch [octaves/sec]220.0140.0
Page 40 and 41: Data: Orcalab• Resident orca reco
Page 42 and 43: Annotation: Orchive• Web system f
Page 44 and 45: Unsupervised Segmentation• Can we
Page 46 and 47: Unsupervised Segmentation: Results
Page 48 and 49: Can we do more with FFV?• First t
Page 50 and 51: Classification Experiments• Repor
Page 52 and 53: Classification Experiments• Repor
Page 54:
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