  Researches on sequential vocalization often require analysis of vocalizations
in long continuous sounds. In such studies as developmental ones or studies
across generations in which days or months of vocalizations must be analyzed,
methods for automatic recognition would be strongly desired. Although methods
for automatic speech recognition for application purposes have been intensively
studied, blindly applying them for biological purposes may not be an optimal
solution. This is because, unlike human speech recognition, analysis of
sequential vocalizations often requires accurate extraction of timing
information. In the present study we propose automated systems suitable for
recognizing birdsong, one of the most intensively investigated sequential
vocalizations, focusing on the three properties of the birdsong. First, a song
is a sequence of vocal elements, called notes, which can be grouped into
categories. Second, temporal structure of birdsong is precisely controlled,
meaning that temporal information is important in song analysis. Finally, notes
are produced according to certain probabilistic rules, which may facilitate the
accurate song recognition. We divided the procedure of song recognition into
three sub-steps: local classification, boundary detection, and global
sequencing, each of which corresponds to each of the three properties of
birdsong. We compared the performances of several different ways to arrange
these three steps. As results, we demonstrated a hybrid model of a deep neural
network and a hidden Markov model is effective in recognizing birdsong with
variable note sequences. We propose suitable arrangements of methods according
to whether accurate boundary detection is needed. Also we designed the new
measure to jointly evaluate the accuracy of note classification and boundary
detection. Our methods should be applicable, with small modification and
tuning, to the songs in other species that hold the three properties of the
sequential vocalization.
