Relevance-based quantization of scattering features for unsupervised mining of environmental audio

Relevance-based quantization of scattering features for unsupervised mining of environmental audio

Abstract The emerging field of computational acoustic monitoring aims at retrieving high-level information from acoustic scenes recorded by some network of sensors. These networks gather large amounts of data requiring analysis. To decide which parts to inspect further, we need tools that automatica...

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Bibliographic Details
Main Authors: Vincent Lostanlen, Grégoire Lafay, Joakim Andén, Mathieu Lagrange
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Unsupervised learning
Data mining
Acoustic signal processing
Wavelet transforms
Audio databases
Content-based retrieval
Online Access:http://link.springer.com/article/10.1186/s13636-018-0138-4

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http://link.springer.com/article/10.1186/s13636-018-0138-4

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