  In this work, we propose a supervised, convex representation based audio
hashing framework for bird species classification. The proposed framework
utilizes archetypal analysis, a matrix factorization technique, to obtain
convex-sparse representations of a bird vocalization. These convex
representations are hashed using Bloom filters with non-cryptographic hash
functions to obtain compact binary codes, designated as conv-codes. The
conv-codes extracted from the training examples are clustered using
class-specific k-medoids clustering with Jaccard coefficient as the similarity
metric. A hash table is populated using the cluster centers as keys while hash
values/slots are pointers to the species identification information. During
testing, the hash table is searched to find the species information
corresponding to a cluster center that exhibits maximum similarity with the
test conv-code. Hence, the proposed framework classifies a bird vocalization in
the conv-code space and requires no explicit classifier or reconstruction error
calculations. Apart from that, based on min-hash and direct addressing, we also
propose a variant of the proposed framework that provides faster and effective
classification. The performances of both these frameworks are compared with
existing bird species classification frameworks on the audio recordings of 50
different bird species.
