detection and segmentation of bird song in noisy environments

Detection and Segmentation of Bird Song in Noisy EnvironmentsLawrence Neal, UHC Honors Thesis

Bioacoustics ProjectBird Species

◦Identifiable by species◦Presence/Absence, activity data is

useful Bird activity may shift in response to

climate change, ecological factors

Bioacoustics Project

Automated RecordingSong Meter automated recordersCollected May-August beginning

2009

Audio Data Analysis

Involves several steps:◦Extracting Bird Sound from Audio◦Identifying Bird Species◦Mapping species data back to sites

Audio Data Analysis

Involves several steps:◦Extracting Bird Sound from Audio

(Segmentation)◦Identifying Bird Species◦Mapping species data back to sites

SegmentationTime-Domain Segmentation

◦Separates audio into multiple clips◦Energy Thresholding, Onset/Offset

Detection◦Has been applied to bird song

Harma 2003, Fagerlund 2004, Lee 2008

SegmentationTime-Domain Segmentation

SegmentationTime-Domain Segmentation

◦Cannot separate overlapping sounds

SegmentationTime-Frequency Segmentation

◦Segment regions of the 2D spectrogram

SegmentationSpectrogram Segmentation

◦Similar to image segmentation

SpectrogramsTwo-dimensional representation

of sound◦Audio amplitude at each (time,

frequency)◦Generated by short-time Fourier

Transform Male voice saying 'nineteenth century'.

Violin playing (note harmonics)

SpectrogramsTradeoffs in parameters

◦Larger STFT size◦Higher freq. resolution

SpectrogramsTradeoffs in parameters

◦Shorter step size◦Higher time resolution

Spectrogram SegmentsEach segment is a continuous

region◦Defined by a binary mask over the

spectrogram

Spectrogram SegmentsCan be converted back to audio

with inverse STFT, or left as 2D segments

Segmentation MethodsPer-Pixel Random Forest

◦Trains on one feature vector per pixel◦Outputs probability per-pixel

Superpixel Merger Method◦First splits spectrogram into

‘superpixels’◦Trains on one feature vector per

superpixel◦Second classifier trains per

superpixel pair◦Outputs connected sets of

superpixels

Random ForestSupervised Classifier

◦Trains on human-provided data with labels “Feature Vector” of values, each with

yes/no label

◦Learns to mimic the human’s labelsBased on decision trees:

◦Tree is traversed with feature vector X

◦Each interior node is a decision of the type: If (Xd < θ) go left; else go right

◦Each leaf node contains a class label In this case, two classes: ‘Bird Sound’ and

‘Negative’

Random ForestConstructed by recursive procedure

◦Check if all remaining examples are the same If so, finish with a leaf node

◦Select a random subset of features For each one, find the optimal split (highest Gini)

◦Choose the (feature, split) pair for maximum Gini coefficient and create new interior node

◦Split the examples and recursively create two child nodes

Classification is a vote among all trees

Per-Pixel TrainingHand-Drawn mask over

spectrogram◦Pixels are randomly sampled

Per-Pixel TrainingFeature vector includes:

◦Pixel Frequency◦Window Variance◦All window pixel values

Per-Pixel OutputProbability Mask over the

spectrogramThreshold is applied to extract

segments

Per-Pixel Output

Per-Pixel Output

Per-Pixel Output

Per-Pixel LimitationsScope is limited to window sizeHigh threshold causes

oversegmentationLow threshold causes

undersegmentationSlow- must classify for each pixel

Superpixel MethodBegins with an initial pre-

segmentation◦Modification of Simple Linear

Iterative Clustering (SLIC) image segmentation

◦Uses computed features that describe regions of the spectrogram

Segments are sets of superpixels

Superpixel ClusteringBased on SLIC method:

◦Each pixel is assigned a 5-valued vector (X,Y, L, a, b) for position and color

Locally-constrained K-Means Clustering◦Each centroid searches only a radius

of 2S S = sqrt(N/K)

Creates a set of regularly-sized regions◦Some regions’ boundaries follow the

edges of larger objects in the image

Superpixel ClusteringOver-segments an image

◦Edges of clusters arealong image edges

But, doesn’t workfor spectrograms

Superpixel ClusteringSpectrograms lack edges

◦Also, only one channel of colorInstead of (x,y,L,a,b), we use a

new vector:◦(x, y, B, V, Gx, Gy, Px, Py)

Superpixel ClusteringX,Y values

◦Time and frequency values in the spectrogram

B, V◦Pixel values after Gaussian blur, variance of

pixel valuesGx ,Gy

◦Horizontal/Vertical Sobel Gradient valuesPx, Py

◦Time and Frequency values of nearest peak (weighted by Gaussian kernel)

Superpixel Clustering

Foreground/Background ClassifierRandom Forest trained using the

same manual spectrogram labels as per-pixel◦Each superpixel is labeled positive

(foreground) if more than 10% of its area overlaps with a positive-labeled region

Feature vector describes superpixel:◦Mean and variance of pixel values,

blurred pixel values, peak frequencies◦Histogram of Oriented Gradients

Foreground/Background Classifier

Superpixel Merger ClassifierRandom Forest trained to classify

pairs of adjacent superpixels◦Positive classification: Merge

together◦Negative classification: Split apart

After background pixels are discarded, all remaining edges between superpixels are classified◦All edges above a threshold are

merged

Superpixel Merger Classifier

Superpixel Method Output

Superpixel Method Output

Superpixel Method Output

Superpixel Method Output

Superpixel Method Output

Superpixel Method Output

Evaluation DatasetsHJ Andrews dataset, 625

recordings◦Each 15 seconds long◦Drawn 2 each from 24 hours

“Set A” dataset, 166 recordings◦All from early and mid morning◦Paired by year, 2009/2010

Differences in Training Data

Results

Results

Results

Results

Future WorkSuperpixel Method is promising

◦Faster than per-pixel classification◦Could use more sophisticated

merger technique

Bibliography A. Harma, “Automatic identification of bird species based on sinusoidal

modeling of syllables,” in IEEE International Conference on Acoustics Speech and Signal Processing, April 2003, pp. 545–548.

Chang-Hsing Lee, Chin-Chuan Han, and Ching-Chien Chuang, “Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 8, pp. 1541 – 1550, 2008.

Leo Breiman, “Random forests,” Machine Learning, pp. 5–32, January 2001. Fagerlund, Seppo. Automatic Recognition of Bird Species by Their Sounds.

Master’s Thesis, HELSINKI UNIVERSITY OF TECHNOLOGY, Laboratory of Acoustics and Audio Signal Processing. Nov. 8, 2004