@inproceedings{fadc03e254e64a928e733d28a1ff24b3,
title = "Relative transfer function estimation from speech keywords",
abstract = "Far-field speech capture systems rely on microphone arrays to spatially filter sound, attenuating unwanted interference and noise and enhancing a speech signal of interest. To design effective spatial filters, we must first estimate the acoustic transfer functions between the source and the microphones. It is difficult to estimate these transfer functions if the source signals are unknown. However, in systems that are activated by a particular speech phrase, we can use that phrase as a pilot signal to estimate the relative transfer functions. Here, we propose a method to estimate relative transfer functions from known speech phrases in the presence of background noise and interference using template matching and time-frequency masking. We find that the proposed method can outperform conventional estimation techniques, but its performance depends on the characteristics of the speech phrase.",
keywords = "Keyword spotting, Microphone array, Multichannel source separation, Relative transfer function",
author = "Corey, {Ryan M.} and Singer, {Andrew C.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018 ; Conference date: 02-07-2018 Through 05-07-2018",
year = "2018",
doi = "10.1007/978-3-319-93764-9_23",
language = "English (US)",
isbn = "9783319937632",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "238--247",
editor = "Sharon Gannot and Yannick Deville and Russell Mason and Plumbley, {Mark D.} and Dominic Ward",
booktitle = "Latent Variable Analysis and Signal Separation - 14th International Conference, LVA/ICA 2018, Proceedings",
address = "Germany",
}