identification of microchiroptera from echolocation calls Lessons learned from human automatic speech recognition Mark D. Skowronski Computational Neuro-Engineering Lab Electrical and Computer Engineering University of Florida Gainesville, FL, USA December 1, 2004
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Mark D. Skowronski Computational Neuro-Engineering Lab Electrical and Computer Engineering
Statistical automatic identification of microchiroptera from echolocation calls Lessons learned from human automatic speech recognition. Mark D. Skowronski Computational Neuro-Engineering Lab Electrical and Computer Engineering University of Florida Gainesville, FL, USA December 1, 2004. - PowerPoint PPT Presentation
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Statistical automatic identification of microchiroptera from echolocation calls
Lessons learned from human automatic speech recognition
Mark D. SkowronskiComputational Neuro-Engineering LabElectrical and Computer Engineering
University of FloridaGainesville, FL, USADecember 1, 2004
Overview• Motivations for bat acoustic research• Review bat call classification methods• Contrast with 1970s human ASR
– Machine learning vs. expert knowledge• Experiments• Conclusions and future work
Bat research motivations• Bats are among:
– the most diverse (25% of all mammal species),– the most endangered,– and the least studied mammals.
• Close relationship with insects– agricultural impact– disease vectors
• Acoustical research– non-invasive (compared to netting)– significant domain (echolocation)
More motivations• Calls simple compared to human speech• Same goals as human ASR
– Narrowband spectrogram– 436 calls (2% of data) in 3 hours (80x real time)– Four classes, a priori: 34, 40, 20, 6%– All experiments on hand-labeled data only– No hand-labeled calls excluded from experiments
1 2 3 4
Methods• Baseline, from the literature
– Features• Duration• Zero crossing: Fmin, Fmax, Fmax_energy• MUSIC super resolution frequency estimator
– Classifier• Discriminant function analysis, quadratic boundaries
• DTW and HMM– Features
• Frequency (MUSIC), log energy, Δs (HMM only)– HMM
– Weakness: accuracy of class labels– No labeled calls excluded, realistic– HMM most accurate, but undertrained– MUSIC frequency estimate robust, but 1000x slower
than ZCA (20x real time)• Machine learning
– Expert information still necessary• Feature extraction (dimensionality reduction)• Model parameters
– DTW: fast training, slow classification– HMM: slow training, fast classification (real time)
Future work• Ultimate goal
– Real-time portable system for species ID– Commercial product possibilites
• Feature extraction– Robust
• Broadband noise• Echos• Unknown distance between bat and microphone
– Chirp model, echo model– Faster frequency estimates– Match assumptions of classifiers
More future work• Detection
– Replace energy-based method with principled statistical methods using frame-based features
• Classification– Accurate class labels for training
• Netting• Record from known bat roosts (preferred)
– Pseudo-sinusoidal input• Oscillator network• Echo state network