1 research outputs found
Directional Embedding Based Semi-supervised Framework For Bird Vocalization Segmentation
This paper proposes a data-efficient, semi-supervised, two-pass framework for
segmenting bird vocalizations. The framework utilizes a binary classification
model to categorize frames of an input audio recording into the background or
bird vocalization. The first pass of the framework automatically generates
training labels from the input recording itself, while model training and
classification is done during the second pass. The proposed framework utilizes
a reference directional model for obtaining a feature representation called
directional embeddings (DE). This reference directional model acts as an
acoustic model for bird vocalizations and is obtained using the mixtures of
Von-Mises Fisher distribution (moVMF). The proposed DE space only contains
information about bird vocalizations, while no information about the background
disturbances is reflected. The framework employs supervised information only
for obtaining the reference directional model and avoids the background
modeling. Hence, it can be regarded as semi-supervised in nature. The proposed
framework is tested on approximately 79000 vocalizations of seven different
bird species. The performance of the framework is also analyzed in the presence
of noise at different SNRs. Experimental results convey that the proposed
framework performs better than the existing bird vocalization segmentation
methods.Comment: Accepted for publication in Applied Acoustic