Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
SlideShare a Scribd company logo

Project Presentation (1)

•
•
A
Abhishek Singh

This document summarizes work on classifying audio clips as containing sounds from North Atlantic right whales or not. Spectrograms of audio clips were generated and template matching and other feature engineering was used to extract over 450 features from the spectrograms. Random forests and gradient boosting models were trained on the features and achieved over 99% accuracy on test data using all features, with template matching features performing nearly as well. The goal is to help identify right whale calls to aid in collision avoidance for shipping lanes.

1 of 9
Download to read offline
March 11th, 2016 Jason Helgren, Jaime Pastor, Abhishek Singh Have ye seen the Right Whale? A Classification Problem
Problem: Identifying the “Right Whale” Calls Goal Identify audio clips containing sounds from the North Atlantic right whale. Type Binary classification Data 30,000 2-second audio clips Application Collision avoidance in shipping lanes.
Audio Classification to Image Classification Fourier Transformation Frequency domain representation of a time varying signal Spectrogram Frequency characteristics of a signal over time Audio Recording Pressure variation in time. We perceive these pressure variations as sound time time frequency frequencydensitypressure Idealized example - a tuning fork
Whale Call Spectrogram Frequency Time
Feature Engineering (I) Processing images: Contrast-enhancement Original spectrogram Processed image Time and frequency metrics (300 features)
Feature Engineering (II) Template matching (150 features) Search for template over spectrogram and find the area of the image that best matches the template Original Image and matchWhale call template example Best match (Correlation, x, y)
Modeling and results (I) Model selection ❏ Random Forests ❏ Gradient Boosting Hyper-parameter tuning ❏ Learning rate ❏ Sampling rate ❏ Number of trees Scoring on the test set ❏ Using all variables (450 features) ❏ Using only template matching variables (150 features) ❏ Using non-template matching variables (300 features)
Modeling and results (II) FEATURE SELECTION TRAIN AUC (24k obs.) TEST AUC (6k obs.) All features (450 features) .9992 .9965 Only template matching features (150 features) .9982 .9951 Only non-template matching features (300 features) .9796 .9499 AUC Summary
Jason Helgren jmhelgren@usfca.edu in/jasonhelgren “Have ye seen the Right Whale? A Classification Problem” Kaggle 2013 Marinexplore Whale Detection Challenge Project available at Github Jaime Pastor jpastorsamper@usfca.edu in/jaimepastor2 Abhishek Singh aasingh4@usfca.edu in/abhishek19895 Thank you

More Related Content

Project Presentation (1)

  • 1. March 11th, 2016 Jason Helgren, Jaime Pastor, Abhishek Singh Have ye seen the Right Whale? A Classification Problem
  • 2. Problem: Identifying the “Right Whale” Calls Goal Identify audio clips containing sounds from the North Atlantic right whale. Type Binary classification Data 30,000 2-second audio clips Application Collision avoidance in shipping lanes.
  • 3. Audio Classification to Image Classification Fourier Transformation Frequency domain representation of a time varying signal Spectrogram Frequency characteristics of a signal over time Audio Recording Pressure variation in time. We perceive these pressure variations as sound time time frequency frequencydensitypressure Idealized example - a tuning fork
  • 5. Feature Engineering (I) Processing images: Contrast-enhancement Original spectrogram Processed image Time and frequency metrics (300 features)
  • 6. Feature Engineering (II) Template matching (150 features) Search for template over spectrogram and find the area of the image that best matches the template Original Image and matchWhale call template example Best match (Correlation, x, y)
  • 7. Modeling and results (I) Model selection ❏ Random Forests ❏ Gradient Boosting Hyper-parameter tuning ❏ Learning rate ❏ Sampling rate ❏ Number of trees Scoring on the test set ❏ Using all variables (450 features) ❏ Using only template matching variables (150 features) ❏ Using non-template matching variables (300 features)
  • 8. Modeling and results (II) FEATURE SELECTION TRAIN AUC (24k obs.) TEST AUC (6k obs.) All features (450 features) .9992 .9965 Only template matching features (150 features) .9982 .9951 Only non-template matching features (300 features) .9796 .9499 AUC Summary
  • 9. Jason Helgren jmhelgren@usfca.edu in/jasonhelgren “Have ye seen the Right Whale? A Classification Problem” Kaggle 2013 Marinexplore Whale Detection Challenge Project available at Github Jaime Pastor jpastorsamper@usfca.edu in/jaimepastor2 Abhishek Singh aasingh4@usfca.edu in/abhishek19895 Thank you