A scalable feature learning and tag prediction framework for natural environment sounds

P. Sattigeri; J. J. Thiagarajan; M. Shah; K. N. Ramamurthy; Andreas Spanias

doi:10.1109/ACSSC.2014.7094773

A scalable feature learning and tag prediction framework for natural environment sounds

P. Sattigeri, J. J. Thiagarajan, M. Shah, K. N. Ramamurthy, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Building feature extraction approaches that can effectively characterize natural environment sounds is challenging due to the dynamic nature. In this paper, we develop a framework for feature extraction and obtaining semantic inferences from such data. In particular, we propose a new pooling strategy for deep architectures, that can preserve the temporal dynamics in the resulting representation. By constructing an ensemble of semantic embeddings, we employ an l₁-reconstruction based prediction algorithm for estimating the relevant tags. We evaluate our approach on challenging environmental sound recognition datasets, and show that the proposed features outperform traditional spectral features.

Original language	English (US)
Title of host publication	Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1779-1783
Number of pages	5
ISBN (Electronic)	9781479982974
DOIs	https://doi.org/10.1109/ACSSC.2014.7094773
State	Published - Apr 24 2015
Event	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 2 2014 → Nov 5 2014

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2015-April
ISSN (Print)	1058-6393

Other

Other	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/2/14 → 11/5/14

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2014.7094773

Cite this

Sattigeri, P., Thiagarajan, J. J., Shah, M., Ramamurthy, K. N., & Spanias, A. (2015). A scalable feature learning and tag prediction framework for natural environment sounds. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 1779-1783). [7094773] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094773

A scalable feature learning and tag prediction framework for natural environment sounds. / Sattigeri, P.; Thiagarajan, J. J.; Shah, M. et al.
Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. ed. / Michael B. Matthews. IEEE Computer Society, 2015. p. 1779-1783 7094773 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sattigeri, P, Thiagarajan, JJ, Shah, M, Ramamurthy, KN & Spanias, A 2015, A scalable feature learning and tag prediction framework for natural environment sounds. in MB Matthews (ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers., 7094773, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2015-April, IEEE Computer Society, pp. 1779-1783, 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/2/14. https://doi.org/10.1109/ACSSC.2014.7094773

Sattigeri P, Thiagarajan JJ, Shah M, Ramamurthy KN, Spanias A. A scalable feature learning and tag prediction framework for natural environment sounds. In Matthews MB, editor, Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society. 2015. p. 1779-1783. 7094773. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2014.7094773

Sattigeri, P. ; Thiagarajan, J. J. ; Shah, M. et al. / A scalable feature learning and tag prediction framework for natural environment sounds. Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. editor / Michael B. Matthews. IEEE Computer Society, 2015. pp. 1779-1783 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

@inproceedings{19767103795a48c4b73d7697c704b233,

title = "A scalable feature learning and tag prediction framework for natural environment sounds",

abstract = "Building feature extraction approaches that can effectively characterize natural environment sounds is challenging due to the dynamic nature. In this paper, we develop a framework for feature extraction and obtaining semantic inferences from such data. In particular, we propose a new pooling strategy for deep architectures, that can preserve the temporal dynamics in the resulting representation. By constructing an ensemble of semantic embeddings, we employ an l1-reconstruction based prediction algorithm for estimating the relevant tags. We evaluate our approach on challenging environmental sound recognition datasets, and show that the proposed features outperform traditional spectral features.",

author = "P. Sattigeri and Thiagarajan, {J. J.} and M. Shah and Ramamurthy, {K. N.} and Andreas Spanias",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 02-11-2014 Through 05-11-2014",

year = "2015",

month = apr,

day = "24",

doi = "10.1109/ACSSC.2014.7094773",

language = "English (US)",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "1779--1783",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers",

}

TY - GEN

T1 - A scalable feature learning and tag prediction framework for natural environment sounds

AU - Sattigeri, P.

AU - Thiagarajan, J. J.

AU - Shah, M.

AU - Ramamurthy, K. N.

AU - Spanias, Andreas

PY - 2015/4/24

Y1 - 2015/4/24

N2 - Building feature extraction approaches that can effectively characterize natural environment sounds is challenging due to the dynamic nature. In this paper, we develop a framework for feature extraction and obtaining semantic inferences from such data. In particular, we propose a new pooling strategy for deep architectures, that can preserve the temporal dynamics in the resulting representation. By constructing an ensemble of semantic embeddings, we employ an l1-reconstruction based prediction algorithm for estimating the relevant tags. We evaluate our approach on challenging environmental sound recognition datasets, and show that the proposed features outperform traditional spectral features.

AB - Building feature extraction approaches that can effectively characterize natural environment sounds is challenging due to the dynamic nature. In this paper, we develop a framework for feature extraction and obtaining semantic inferences from such data. In particular, we propose a new pooling strategy for deep architectures, that can preserve the temporal dynamics in the resulting representation. By constructing an ensemble of semantic embeddings, we employ an l1-reconstruction based prediction algorithm for estimating the relevant tags. We evaluate our approach on challenging environmental sound recognition datasets, and show that the proposed features outperform traditional spectral features.

UR - http://www.scopus.com/inward/record.url?scp=84940510433&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84940510433&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2014.7094773

DO - 10.1109/ACSSC.2014.7094773

M3 - Conference contribution

AN - SCOPUS:84940510433

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1779

EP - 1783

BT - Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015

Y2 - 2 November 2014 through 5 November 2014

ER -

A scalable feature learning and tag prediction framework for natural environment sounds

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this