Conv-codes: Audio Hashing For Bird Species Classification Article Swipe

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Anshul Thakur , Pulkit Sharma , Vinayak Abrol , P.K. Rajan ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1902.02498

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.

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Computer Science

Theoretical Computer Science

Cluster Analysis

Data Mining

Artificial Intelligence

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Hash function Hash table Computer science Double hashing Jaccard index Locality-sensitive hashing Universal hashing Dynamic perfect hashing Theoretical computer science Cluster analysis Pattern recognition (psychology) Data mining Artificial intelligence Computer security

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1902.02498
PDF: https://arxiv.org/pdf/1902.02498
OA Status: green
References: 19
Related Works: 20
OpenAlex ID: https://openalex.org/W2950636868

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DOI: https://doi.org/10.48550/arxiv.1902.02498

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Title: Conv-codes: Audio Hashing For Bird Species Classification

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-02-07

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Authors: Anshul Thakur, Pulkit Sharma, Vinayak Abrol, P.K. Rajan

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Landing page: https://arxiv.org/abs/1902.02498

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Concepts: Hash function, Hash table, Computer science, Double hashing, Jaccard index, Locality-sensitive hashing, Universal hashing, Dynamic perfect hashing, Theoretical computer science, Cluster analysis, Pattern recognition (psychology), Data mining, Artificial intelligence, Computer security

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abstract_inverted_index.faster	162
abstract_inverted_index.hashed	39
abstract_inverted_index.matrix	24
abstract_inverted_index.obtain	28, 48
abstract_inverted_index.Jaccard	69
abstract_inverted_index.centers	83
abstract_inverted_index.cluster	82, 111
abstract_inverted_index.compact	49
abstract_inverted_index.filters	42
abstract_inverted_index.hashing	11
abstract_inverted_index.maximum	115
abstract_inverted_index.metric.	74
abstract_inverted_index.propose	4, 153
abstract_inverted_index.species	15, 93, 106, 177
abstract_inverted_index.variant	155
abstract_inverted_index.compared	173
abstract_inverted_index.examples	61
abstract_inverted_index.exhibits	114
abstract_inverted_index.existing	175
abstract_inverted_index.explicit	136
abstract_inverted_index.min-hash	147
abstract_inverted_index.pointers	90
abstract_inverted_index.proposed	18, 123, 158
abstract_inverted_index.provides	161
abstract_inverted_index.requires	134
abstract_inverted_index.searched	102
abstract_inverted_index.species.	188
abstract_inverted_index.testing,	97
abstract_inverted_index.training	60
abstract_inverted_index.utilizes	20
abstract_inverted_index.analysis,	22
abstract_inverted_index.clustered	63
abstract_inverted_index.conv-code	131
abstract_inverted_index.different	186
abstract_inverted_index.effective	164
abstract_inverted_index.extracted	57
abstract_inverted_index.framework	12, 19, 124, 159
abstract_inverted_index.functions	46
abstract_inverted_index.k-medoids	66
abstract_inverted_index.populated	79
abstract_inverted_index.archetypal	21
abstract_inverted_index.classifier	137
abstract_inverted_index.classifies	125
abstract_inverted_index.clustering	67
abstract_inverted_index.conv-code.	120
abstract_inverted_index.conv-codes	56
abstract_inverted_index.designated	52
abstract_inverted_index.frameworks	171, 179
abstract_inverted_index.recordings	183
abstract_inverted_index.similarity	73, 116
abstract_inverted_index.technique,	26
abstract_inverted_index.addressing,	150
abstract_inverted_index.coefficient	70
abstract_inverted_index.conv-codes.	54
abstract_inverted_index.information	107
abstract_inverted_index.supervised,	6
abstract_inverted_index.information.	95
abstract_inverted_index.performances	167
abstract_inverted_index.values/slots	88
abstract_inverted_index.vocalization	128
abstract_inverted_index.calculations.	141
abstract_inverted_index.convex-sparse	29
abstract_inverted_index.corresponding	108
abstract_inverted_index.factorization	25
abstract_inverted_index.vocalization.	34
abstract_inverted_index.class-specific	65
abstract_inverted_index.classification	178
abstract_inverted_index.identification	94
abstract_inverted_index.reconstruction	139
abstract_inverted_index.representation	8
abstract_inverted_index.classification.	16, 165
abstract_inverted_index.representations	30, 37
abstract_inverted_index.non-cryptographic	44
cited_by_percentile_year
countries_distinct_count	3
institutions_distinct_count	4
sustainable_development_goals[0].id	https://metadata.un.org/sdg/15
sustainable_development_goals[0].score	0.699999988079071
sustainable_development_goals[0].display_name	Life in Land
citation_normalized_percentile