Tagging and Retrieval of Room Impulse Responses Using Semantic Word Vectors and Perceptual Measures of Reverberation
×
Cite This
Citation & Abstract
EM. TH. Chourdakis, and JO. D.. Reiss, "Tagging and Retrieval of Room Impulse Responses Using Semantic Word Vectors and Perceptual Measures of Reverberation," Paper 10198, (2019 March.). doi:
EM. TH. Chourdakis, and JO. D.. Reiss, "Tagging and Retrieval of Room Impulse Responses Using Semantic Word Vectors and Perceptual Measures of Reverberation," Paper 10198, (2019 March.). doi:
Abstract: This paper studies tagging and retrieval of room impulse responses from a labelled library. A similarity-based method is introduced that relies on perceptually relevant characteristics of reverberation. This method is developed using a publicly available dataset of algorithmic reverberation settings. Semantic word vectors are introduced to exploit semantic correlation among tags and allow for unseen words to be used for retrieval. Average precision is reported on a subset of the dataset as well as tagging of recorded room impulse responses. The developed approach manages to assign downloaded room impulse responses to tags that match their short descriptions. Furthermore, introducing semantic word vectors allows it to perform well even when large portions of the training data have been replaced by synonyms.
@article{chourdakis2019tagging,
author={chourdakis, emmanouil theofanis and reiss, joshua d.},
journal={journal of the audio engineering society},
title={tagging and retrieval of room impulse responses using semantic word vectors and perceptual measures of reverberation},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},}
@article{chourdakis2019tagging,
author={chourdakis, emmanouil theofanis and reiss, joshua d.},
journal={journal of the audio engineering society},
title={tagging and retrieval of room impulse responses using semantic word vectors and perceptual measures of reverberation},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},
abstract={this paper studies tagging and retrieval of room impulse responses from a labelled library. a similarity-based method is introduced that relies on perceptually relevant characteristics of reverberation. this method is developed using a publicly available dataset of algorithmic reverberation settings. semantic word vectors are introduced to exploit semantic correlation among tags and allow for unseen words to be used for retrieval. average precision is reported on a subset of the dataset as well as tagging of recorded room impulse responses. the developed approach manages to assign downloaded room impulse responses to tags that match their short descriptions. furthermore, introducing semantic word vectors allows it to perform well even when large portions of the training data have been replaced by synonyms.},}
TY - paper
TI - Tagging and Retrieval of Room Impulse Responses Using Semantic Word Vectors and Perceptual Measures of Reverberation
SP -
EP -
AU - Chourdakis, Emmanouil Theofanis
AU - Reiss, Joshua D.
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
TY - paper
TI - Tagging and Retrieval of Room Impulse Responses Using Semantic Word Vectors and Perceptual Measures of Reverberation
SP -
EP -
AU - Chourdakis, Emmanouil Theofanis
AU - Reiss, Joshua D.
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
AB - This paper studies tagging and retrieval of room impulse responses from a labelled library. A similarity-based method is introduced that relies on perceptually relevant characteristics of reverberation. This method is developed using a publicly available dataset of algorithmic reverberation settings. Semantic word vectors are introduced to exploit semantic correlation among tags and allow for unseen words to be used for retrieval. Average precision is reported on a subset of the dataset as well as tagging of recorded room impulse responses. The developed approach manages to assign downloaded room impulse responses to tags that match their short descriptions. Furthermore, introducing semantic word vectors allows it to perform well even when large portions of the training data have been replaced by synonyms.
This paper studies tagging and retrieval of room impulse responses from a labelled library. A similarity-based method is introduced that relies on perceptually relevant characteristics of reverberation. This method is developed using a publicly available dataset of algorithmic reverberation settings. Semantic word vectors are introduced to exploit semantic correlation among tags and allow for unseen words to be used for retrieval. Average precision is reported on a subset of the dataset as well as tagging of recorded room impulse responses. The developed approach manages to assign downloaded room impulse responses to tags that match their short descriptions. Furthermore, introducing semantic word vectors allows it to perform well even when large portions of the training data have been replaced by synonyms.
Authors:
Chourdakis, Emmanouil Theofanis; Reiss, Joshua D.
Affiliation:
Queen Mary University London, London, UK
AES Convention:
146 (March 2019)
Paper Number:
10198
Publication Date:
March 10, 2019Import into BibTeX
Subject:
MIR
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=20331