# Conv-codes: Audio Hashing For Bird Species Classification

## TL;DR

This paper introduces a novel audio hashing framework using convex-sparse representations and Bloom filters for efficient bird species classification, eliminating the need for explicit classifiers and improving speed and accuracy.

## Contribution

The work presents a new convex representation based audio hashing method for bird classification, utilizing archetypal analysis and Bloom filters, with a faster variant based on min-hash.

## Key findings

- Achieved accurate classification on 50 bird species datasets.
- Demonstrated faster classification with the min-hash variant.
- Outperformed existing bird classification frameworks.

## Abstract

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.