Methods and Dataset¶

Methods¶

Feature Extraction:
- Linear Predictive Coding
- Clustering and Vector Quantization
Classification:
- VQ Distortion
- Naive Bayes
- Markov Chain Model
- Discrete Hidden Markov Model

Sound recordings acquired through the Monterey Accelerated Research System (MARS) cabled observatory [1]
A 4.5-hour continuous and clear recording of one song session from 21 December 2016
One singing whale, variable noise background due to changes in wind speed
256 kHz recording decimated to 16KHz sampling rate
5470 song units manually identified, segmented, and labelled in 22 different classes
Constrained to classes having at least 200 instances
This resulted in a total of 4539 instances across eight classes as follows:
- A : 512
- Bm : 608
- C : 550
- E : 713
- F : 340
- G2 : 307
- I3 : 324
- II : 1185
Each class instance set was randomly partitioned into:
- 50% for training
- 50% for testing

A DFT of window size 1024 and offset 32 was used in each of the spectrograms below. Time axis is in seconds.