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Audio and Acoustic Signal Processing

: Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels

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Authors:
Maha ALODEH, Danilo SPANO, Symeon CHATZINOTAS, Bjorn OTTERSTEN
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1 March 2017 - 7:55am
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[1] Maha ALODEH, Danilo SPANO, Symeon CHATZINOTAS, Bjorn OTTERSTEN, ": Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1550. Accessed: Oct. 19, 2017.
@article{1550-17,
url = {http://sigport.org/1550},
author = {Maha ALODEH; Danilo SPANO; Symeon CHATZINOTAS; Bjorn OTTERSTEN },
publisher = {IEEE SigPort},
title = {: Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels},
year = {2017} }
TY - EJOUR
T1 - : Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels
AU - Maha ALODEH; Danilo SPANO; Symeon CHATZINOTAS; Bjorn OTTERSTEN
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1550
ER -
Maha ALODEH, Danilo SPANO, Symeon CHATZINOTAS, Bjorn OTTERSTEN. (2017). : Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels. IEEE SigPort. http://sigport.org/1550
Maha ALODEH, Danilo SPANO, Symeon CHATZINOTAS, Bjorn OTTERSTEN, 2017. : Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels. Available at: http://sigport.org/1550.
Maha ALODEH, Danilo SPANO, Symeon CHATZINOTAS, Bjorn OTTERSTEN. (2017). ": Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels." Web.
1. Maha ALODEH, Danilo SPANO, Symeon CHATZINOTAS, Bjorn OTTERSTEN. : Faster-than-Nyquist Spatiotemporal Symbol-level Precoding in the Downlink of Multiuser MISO Channels [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1550

Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification


This paper presents supervised feature learning approaches for speaker identification that rely on nonnegative matrix factorisation. Recent studies have shown that group nonnegative matrix factorisation and task-driven supervised dictionary learning can help performing effective feature learning for audio classification problems.

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Authors:
victor bisot, slim essid, gaël richard
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1 March 2017 - 4:34am
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[1] victor bisot, slim essid, gaël richard, "Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1539. Accessed: Oct. 19, 2017.
@article{1539-17,
url = {http://sigport.org/1539},
author = {victor bisot; slim essid; gaël richard },
publisher = {IEEE SigPort},
title = {Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification},
year = {2017} }
TY - EJOUR
T1 - Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification
AU - victor bisot; slim essid; gaël richard
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1539
ER -
victor bisot, slim essid, gaël richard. (2017). Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification. IEEE SigPort. http://sigport.org/1539
victor bisot, slim essid, gaël richard, 2017. Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification. Available at: http://sigport.org/1539.
victor bisot, slim essid, gaël richard. (2017). "Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification." Web.
1. victor bisot, slim essid, gaël richard. Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1539

CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP)


High Resolution Envelope Processing (HREP) is a new tool for improved perceptual coding of audio signals that predominantly consist of many dense transient events, such as applause, rain drop sounds, etc. These signals have traditionally been very difficult to code for perceptual audio codecs, particularly at low bit rates. Based on the gain control principle, HREP acts as a pre-/post-processor pair to perceptual audio codecs and preserves the temporal fine structure and subjective quality of applause-like signals.

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Authors:
Florin Ghido, Sascha Disch, Jürgen Herre, Franz Reutelhuber, Alexander Adami
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1 March 2017 - 4:15am
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ICASSP 2017 HREP Poster

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[1] Florin Ghido, Sascha Disch, Jürgen Herre, Franz Reutelhuber, Alexander Adami, "CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1538. Accessed: Oct. 19, 2017.
@article{1538-17,
url = {http://sigport.org/1538},
author = {Florin Ghido; Sascha Disch; Jürgen Herre; Franz Reutelhuber; Alexander Adami },
publisher = {IEEE SigPort},
title = {CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP)},
year = {2017} }
TY - EJOUR
T1 - CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP)
AU - Florin Ghido; Sascha Disch; Jürgen Herre; Franz Reutelhuber; Alexander Adami
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1538
ER -
Florin Ghido, Sascha Disch, Jürgen Herre, Franz Reutelhuber, Alexander Adami. (2017). CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP). IEEE SigPort. http://sigport.org/1538
Florin Ghido, Sascha Disch, Jürgen Herre, Franz Reutelhuber, Alexander Adami, 2017. CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP). Available at: http://sigport.org/1538.
Florin Ghido, Sascha Disch, Jürgen Herre, Franz Reutelhuber, Alexander Adami. (2017). "CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP)." Web.
1. Florin Ghido, Sascha Disch, Jürgen Herre, Franz Reutelhuber, Alexander Adami. CODING OF FINE GRANULAR AUDIO SIGNALS USING HIGH RESOLUTION ENVELOPE PROCESSING (HREP) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1538

MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY


A sensor network wishes to transmit information to a fusion center to allow it to detect a public hypothesis, but at the same time prevent it from inferring a private hypothesis. We propose a multilayer sensor network structure, where each sensor first applies a nonlinear fusion function on the information it receives from sensors in a previous layer, and then a linear weighting matrix to distort the information it sends to sensors in the next layer.

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Authors:
Xin He, Wee Peng Tay
Submitted On:
1 March 2017 - 1:57am
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ICASSP17_xin.pdf

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[1] Xin He, Wee Peng Tay, "MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1521. Accessed: Oct. 19, 2017.
@article{1521-17,
url = {http://sigport.org/1521},
author = {Xin He; Wee Peng Tay },
publisher = {IEEE SigPort},
title = {MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY},
year = {2017} }
TY - EJOUR
T1 - MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY
AU - Xin He; Wee Peng Tay
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1521
ER -
Xin He, Wee Peng Tay. (2017). MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY. IEEE SigPort. http://sigport.org/1521
Xin He, Wee Peng Tay, 2017. MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY. Available at: http://sigport.org/1521.
Xin He, Wee Peng Tay. (2017). "MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY." Web.
1. Xin He, Wee Peng Tay. MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1521

BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION


Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been determined yet. This paper presents a method that operates directly on the speech signal, thus avoiding the problematic step of feature extraction.

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16 March 2017 - 10:05am
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ICASSP2017_Lotfidereshgi (poster) V2.pdf

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[1] , "BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1516. Accessed: Oct. 19, 2017.
@article{1516-17,
url = {http://sigport.org/1516},
author = { },
publisher = {IEEE SigPort},
title = {BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1516
ER -
. (2017). BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION. IEEE SigPort. http://sigport.org/1516
, 2017. BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION. Available at: http://sigport.org/1516.
. (2017). "BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION." Web.
1. . BIOLOGICALLY INSPIRED SPEECH EMOTION RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1516

ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC


This paper addresses the task of Automatic Speech Recognition (ASR) with music in the background, where the accuracy of recognition may deteriorate significantly.
To improve the robustness of ASR in this task, e.g. for broadcast news transcription or subtitles creation, we adopt two approaches:
1) multi-condition training of the acoustic models and 2) denoising autoencoders followed by acoustic model training on the preprocessed data.
In the latter case, two types of autoencoders are considered: the fully connected and the convolutional network.

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Authors:
Jiri Malek, Jindrich Zdansky, Petr Cerva
Submitted On:
28 February 2017 - 9:22am
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posterICASSP2017_MalekZdanskyCerva.pdf

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[1] Jiri Malek, Jindrich Zdansky, Petr Cerva, "ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1511. Accessed: Oct. 19, 2017.
@article{1511-17,
url = {http://sigport.org/1511},
author = {Jiri Malek; Jindrich Zdansky; Petr Cerva },
publisher = {IEEE SigPort},
title = {ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC},
year = {2017} }
TY - EJOUR
T1 - ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC
AU - Jiri Malek; Jindrich Zdansky; Petr Cerva
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1511
ER -
Jiri Malek, Jindrich Zdansky, Petr Cerva. (2017). ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC. IEEE SigPort. http://sigport.org/1511
Jiri Malek, Jindrich Zdansky, Petr Cerva, 2017. ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC. Available at: http://sigport.org/1511.
Jiri Malek, Jindrich Zdansky, Petr Cerva. (2017). "ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC." Web.
1. Jiri Malek, Jindrich Zdansky, Petr Cerva. ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1511

Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings


Many approaches have been used in bird species classification from their sound in order to provide labels for the whole of a recording. However, a more precise classification of each bird vocalization would be of great importance to the use and management of sound archives and bird monitoring. In this work, we introduce a technique that using a two step process can first automatically detect all bird vocalizations and then, with the use of ‘weakly’ labelled recordings, classify them.

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Authors:
Veronica Morfi, Dan Stowell
Submitted On:
28 February 2017 - 7:05am
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[1] Veronica Morfi, Dan Stowell, "Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1505. Accessed: Oct. 19, 2017.
@article{1505-17,
url = {http://sigport.org/1505},
author = {Veronica Morfi; Dan Stowell },
publisher = {IEEE SigPort},
title = {Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings},
year = {2017} }
TY - EJOUR
T1 - Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings
AU - Veronica Morfi; Dan Stowell
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1505
ER -
Veronica Morfi, Dan Stowell. (2017). Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings. IEEE SigPort. http://sigport.org/1505
Veronica Morfi, Dan Stowell, 2017. Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings. Available at: http://sigport.org/1505.
Veronica Morfi, Dan Stowell. (2017). "Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings." Web.
1. Veronica Morfi, Dan Stowell. Deductive Refinement of Species Labelling in Weakly Labelled Birdsong Recordings [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1505

Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks

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Authors:
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
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28 February 2017 - 2:59am
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[1] Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed, "Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1487. Accessed: Oct. 19, 2017.
@article{1487-17,
url = {http://sigport.org/1487},
author = {Michael M. Abdel-Sayed; Ahmed Khattab; Mohamed F. Abu-Elyazeed },
publisher = {IEEE SigPort},
title = {Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks},
year = {2017} }
TY - EJOUR
T1 - Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks
AU - Michael M. Abdel-Sayed; Ahmed Khattab; Mohamed F. Abu-Elyazeed
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1487
ER -
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed. (2017). Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks. IEEE SigPort. http://sigport.org/1487
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed, 2017. Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks. Available at: http://sigport.org/1487.
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed. (2017). "Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks." Web.
1. Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed. Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1487

FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS


Speaker diarization in noisy conditions is addressed in this paper. The regression-based DNN is first adopted to map the noisy acoustic features to the clean features, and then consensus clustering of the original and mapped features is used to fuse the diarization results. The experiments are conducted on the IFLY-DIAR-II database, which is a Chinese talk show database with various noise types, such as music, applause and laughter. Compared to the baseline system using PLP features, a 21.26% relative DER improvement can be achieved using the proposed algorithm.

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Authors:
Weixin Zhu, Wu Guo, Guoping Hu
Submitted On:
11 March 2017 - 8:49pm
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[1] Weixin Zhu, Wu Guo, Guoping Hu, "FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1460. Accessed: Oct. 19, 2017.
@article{1460-17,
url = {http://sigport.org/1460},
author = {Weixin Zhu; Wu Guo; Guoping Hu },
publisher = {IEEE SigPort},
title = {FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS},
year = {2017} }
TY - EJOUR
T1 - FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS
AU - Weixin Zhu; Wu Guo; Guoping Hu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1460
ER -
Weixin Zhu, Wu Guo, Guoping Hu. (2017). FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS. IEEE SigPort. http://sigport.org/1460
Weixin Zhu, Wu Guo, Guoping Hu, 2017. FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS. Available at: http://sigport.org/1460.
Weixin Zhu, Wu Guo, Guoping Hu. (2017). "FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS." Web.
1. Weixin Zhu, Wu Guo, Guoping Hu. FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1460

FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS


Speaker diarization in noisy conditions is addressed in this paper. The regression-based DNN is first adopted to map the noisy acoustic features to the clean features, and then consensus clustering of the original and mapped features is used to fuse the diarization results. The experiments are conducted on the IFLY-DIAR-II database, which is a Chinese talk show database with various noise types, such as music, applause and laughter. Compared to the baseline system using PLP features, a 21.26% relative DER improvement can be achieved using the proposed algorithm.

Paper Details

Authors:
Weixin Zhu, Wu Guo, Guoping Hu
Submitted On:
27 February 2017 - 8:37pm
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poster_wxzhu_v2.pptx

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[1] Weixin Zhu, Wu Guo, Guoping Hu, "FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1459. Accessed: Oct. 19, 2017.
@article{1459-17,
url = {http://sigport.org/1459},
author = {Weixin Zhu; Wu Guo; Guoping Hu },
publisher = {IEEE SigPort},
title = {FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS},
year = {2017} }
TY - EJOUR
T1 - FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS
AU - Weixin Zhu; Wu Guo; Guoping Hu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1459
ER -
Weixin Zhu, Wu Guo, Guoping Hu. (2017). FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS. IEEE SigPort. http://sigport.org/1459
Weixin Zhu, Wu Guo, Guoping Hu, 2017. FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS. Available at: http://sigport.org/1459.
Weixin Zhu, Wu Guo, Guoping Hu. (2017). "FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS." Web.
1. Weixin Zhu, Wu Guo, Guoping Hu. FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1459

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