DocumentCode :
1544050
Title :
Supervised Graph-Based Processing for Sequential Transient Interference Suppression
Author :
Talmon, Ronen ; Cohen, Israel ; Gannot, Sharon ; Coifman, Ronald R.
Author_Institution :
Dept. of Math., Yale Univ., New Haven, CT, USA
Volume :
20
Issue :
9
fYear :
2012
Firstpage :
2528
Lastpage :
2538
Abstract :
In this paper, we present a supervised graph-based framework for sequential processing and employ it to the problem of transient interference suppression. Transients typically consist of an initial peak followed by decaying short-duration oscillations. Such sounds, e.g., keyboard typing and door knocking, often arise as an interference in everyday applications: hearing aids, hands-free accessories, mobile phones, and conference-room devices. We describe a graph construction using a noisy speech signal and training recordings of typical transients. The main idea is to capture the transient interference structure, which may emerge from the construction of the graph. The graph parametrization is then viewed as a data-driven model of the transients and utilized to define a filter that extracts the transients from noisy speech measurements. Unlike previous transient interference suppression studies, in this work the graph is constructed in advance from training recordings. Then, the graph is extended to newly acquired measurements, providing a sequential filtering framework of noisy speech.
Keywords :
filtering theory; graph theory; interference suppression; speech enhancement; conference-room devices; data-driven model; door knocking; graph parametrization; hands-free accessories; hearing aids; keyboard typing; mobile phones; noisy speech measurements; sequential filtering framework; sequential transient interference suppression; short-duration oscillations; supervised graph-based processing; Interference; Noise; Noise measurement; Speech; Speech enhancement; Training; Transient analysis; Acoustic noise; graph filtering; speech enhancement; speech processing; transient noise;
fLanguage :
English
Journal_Title :
Audio, Speech, and Language Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1558-7916
Type :
jour
DOI :
10.1109/TASL.2012.2205243
Filename :
6220851
Link To Document :
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