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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Approaching Optimal Embedding in Audio Steganography with GAN


Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography.

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Authors:
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi
Submitted On:
28 May 2020 - 10:39pm
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[1] Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi, "Approaching Optimal Embedding in Audio Steganography with GAN", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5445. Accessed: Oct. 24, 2020.
@article{5445-20,
url = {http://sigport.org/5445},
author = {Jianhua Yang; Huilin Zheng; Xiangui Kang; Yun-Qing Shi },
publisher = {IEEE SigPort},
title = {Approaching Optimal Embedding in Audio Steganography with GAN},
year = {2020} }
TY - EJOUR
T1 - Approaching Optimal Embedding in Audio Steganography with GAN
AU - Jianhua Yang; Huilin Zheng; Xiangui Kang; Yun-Qing Shi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5445
ER -
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. (2020). Approaching Optimal Embedding in Audio Steganography with GAN. IEEE SigPort. http://sigport.org/5445
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi, 2020. Approaching Optimal Embedding in Audio Steganography with GAN. Available at: http://sigport.org/5445.
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. (2020). "Approaching Optimal Embedding in Audio Steganography with GAN." Web.
1. Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. Approaching Optimal Embedding in Audio Steganography with GAN [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5445

Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision

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Authors:
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous
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28 May 2020 - 1:01am
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[1] Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous, "Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5444. Accessed: Oct. 24, 2020.
@article{5444-20,
url = {http://sigport.org/5444},
author = {Aren Jansen; Daniel P. W. Ellis; Shawn Hershey; R. Channing Moore; Manoj Plakal; Ashok Popat; Rif A. Saurous },
publisher = {IEEE SigPort},
title = {Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision},
year = {2020} }
TY - EJOUR
T1 - Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
AU - Aren Jansen; Daniel P. W. Ellis; Shawn Hershey; R. Channing Moore; Manoj Plakal; Ashok Popat; Rif A. Saurous
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5444
ER -
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous. (2020). Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision. IEEE SigPort. http://sigport.org/5444
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous, 2020. Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision. Available at: http://sigport.org/5444.
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous. (2020). "Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision." Web.
1. Aren Jansen, Daniel P. W. Ellis, Shawn Hershey, R. Channing Moore, Manoj Plakal, Ashok Popat, Rif A. Saurous. Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5444

Source Coding of Audio Signals with a Generative Model


These are the slides from the video presentation at ICASSP 2020 of the paper "Source Coding of Audio Signals with a Generative Model".

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Authors:
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou
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27 May 2020 - 2:04pm
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[1] Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou, "Source Coding of Audio Signals with a Generative Model", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5443. Accessed: Oct. 24, 2020.
@article{5443-20,
url = {http://sigport.org/5443},
author = {Roy Fejgin; Janusz Klejsa; Lars Villemoes; Cong Zhou },
publisher = {IEEE SigPort},
title = {Source Coding of Audio Signals with a Generative Model},
year = {2020} }
TY - EJOUR
T1 - Source Coding of Audio Signals with a Generative Model
AU - Roy Fejgin; Janusz Klejsa; Lars Villemoes; Cong Zhou
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5443
ER -
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. (2020). Source Coding of Audio Signals with a Generative Model. IEEE SigPort. http://sigport.org/5443
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou, 2020. Source Coding of Audio Signals with a Generative Model. Available at: http://sigport.org/5443.
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. (2020). "Source Coding of Audio Signals with a Generative Model." Web.
1. Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. Source Coding of Audio Signals with a Generative Model [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5443

Audio-based Detection of Explicit Content in Music


We present a novel automatic system for performing explicit content detection directly on the audio signal. Our modular approach uses an audio-to-character recognition model, a keyword spotting model associated with a dictionary of carefully chosen keywords, and a Random Forest classification model for the final decision. To the best of our knowledge, this is the first explicit content detection system based on audio only. We demonstrate the individual relevance of our modules on a set of sub-tasks and compare our approach to a lyrics-informed oracle and an end-to-end naive architecture.

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Authors:
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc
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27 May 2020 - 6:05am
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[1] Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc, "Audio-based Detection of Explicit Content in Music", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5441. Accessed: Oct. 24, 2020.
@article{5441-20,
url = {http://sigport.org/5441},
author = {Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gael Richard; Florence d’Alché-Buc },
publisher = {IEEE SigPort},
title = {Audio-based Detection of Explicit Content in Music},
year = {2020} }
TY - EJOUR
T1 - Audio-based Detection of Explicit Content in Music
AU - Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gael Richard; Florence d’Alché-Buc
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5441
ER -
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. (2020). Audio-based Detection of Explicit Content in Music. IEEE SigPort. http://sigport.org/5441
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc, 2020. Audio-based Detection of Explicit Content in Music. Available at: http://sigport.org/5441.
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. (2020). "Audio-based Detection of Explicit Content in Music." Web.
1. Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. Audio-based Detection of Explicit Content in Music [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5441

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection


Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes.

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Authors:
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak
Submitted On:
27 May 2020 - 8:49am
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[1] Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak, "Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5440. Accessed: Oct. 24, 2020.
@article{5440-20,
url = {http://sigport.org/5440},
author = {Flavio Martinelli; Giorgia Dellaferrera; Pablo Mainar; Milos Cernak },
publisher = {IEEE SigPort},
title = {Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection},
year = {2020} }
TY - EJOUR
T1 - Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
AU - Flavio Martinelli; Giorgia Dellaferrera; Pablo Mainar; Milos Cernak
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5440
ER -
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. (2020). Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection. IEEE SigPort. http://sigport.org/5440
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak, 2020. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection. Available at: http://sigport.org/5440.
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. (2020). "Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection." Web.
1. Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5440

I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION

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Authors:
Miquel India, Javier Hernando
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26 May 2020 - 4:46am
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[1] Miquel India, Javier Hernando, "I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5438. Accessed: Oct. 24, 2020.
@article{5438-20,
url = {http://sigport.org/5438},
author = {Miquel India; Javier Hernando },
publisher = {IEEE SigPort},
title = {I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION},
year = {2020} }
TY - EJOUR
T1 - I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION
AU - Miquel India; Javier Hernando
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5438
ER -
Miquel India, Javier Hernando. (2020). I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/5438
Miquel India, Javier Hernando, 2020. I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION. Available at: http://sigport.org/5438.
Miquel India, Javier Hernando. (2020). "I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION." Web.
1. Miquel India, Javier Hernando. I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5438

VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING


In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotropic Lateral Sclerosis (ALS), Parkinson’s Disease (PD) and Healthy Controls (HC) using a CNN-LSTM network. Classification performance is examined for three different tasks, namely, Spontaneous speech (SPON), Diadochoki-netic rate (DIDK) and Sustained Phonation (PHON). Experiments are conducted using speech data recorded from 60 ALS, 60 PD and60 HC subjects. Classification using SVM and DNN are considered baseline schemes.

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Authors:
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh
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26 May 2020 - 1:39am
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[1] Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh, "VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5437. Accessed: Oct. 24, 2020.
@article{5437-20,
url = {http://sigport.org/5437},
author = {Jhansi Mallela; Aravind Illa; Suhas B N; Sathvik Udupa; Yamini Belur; Nalini Atchayaram; Ravi Yadav; Pradeep Reddy; Dipanjan Gope; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING},
year = {2020} }
TY - EJOUR
T1 - VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING
AU - Jhansi Mallela; Aravind Illa; Suhas B N; Sathvik Udupa; Yamini Belur; Nalini Atchayaram; Ravi Yadav; Pradeep Reddy; Dipanjan Gope; Prasanta Kumar Ghosh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5437
ER -
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh. (2020). VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING. IEEE SigPort. http://sigport.org/5437
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh, 2020. VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING. Available at: http://sigport.org/5437.
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh. (2020). "VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING." Web.
1. Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh. VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5437

Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors

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Authors:
Yilin Liu, Fengyang Jiang, Mahanth Gowda
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25 May 2020 - 11:40pm
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[1] Yilin Liu, Fengyang Jiang, Mahanth Gowda, "Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5435. Accessed: Oct. 24, 2020.
@article{5435-20,
url = {http://sigport.org/5435},
author = {Yilin Liu; Fengyang Jiang; Mahanth Gowda },
publisher = {IEEE SigPort},
title = {Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors},
year = {2020} }
TY - EJOUR
T1 - Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors
AU - Yilin Liu; Fengyang Jiang; Mahanth Gowda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5435
ER -
Yilin Liu, Fengyang Jiang, Mahanth Gowda. (2020). Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors. IEEE SigPort. http://sigport.org/5435
Yilin Liu, Fengyang Jiang, Mahanth Gowda, 2020. Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors. Available at: http://sigport.org/5435.
Yilin Liu, Fengyang Jiang, Mahanth Gowda. (2020). "Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors." Web.
1. Yilin Liu, Fengyang Jiang, Mahanth Gowda. Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5435

SPEECH RECOGNITION MODEL COMPRESSION


Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-intensive. In this paper, we propose Bin & Quant (B&Q), a compression technique using which we were able to reduce the Deep Speech 2 speech recognition model size by 7 times for a negligible loss in accuracy.

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Authors:
Ahmed Tewfik, Raj Pawate
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25 May 2020 - 2:17pm
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[1] Ahmed Tewfik, Raj Pawate, "SPEECH RECOGNITION MODEL COMPRESSION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5434. Accessed: Oct. 24, 2020.
@article{5434-20,
url = {http://sigport.org/5434},
author = {Ahmed Tewfik; Raj Pawate },
publisher = {IEEE SigPort},
title = {SPEECH RECOGNITION MODEL COMPRESSION},
year = {2020} }
TY - EJOUR
T1 - SPEECH RECOGNITION MODEL COMPRESSION
AU - Ahmed Tewfik; Raj Pawate
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5434
ER -
Ahmed Tewfik, Raj Pawate. (2020). SPEECH RECOGNITION MODEL COMPRESSION. IEEE SigPort. http://sigport.org/5434
Ahmed Tewfik, Raj Pawate, 2020. SPEECH RECOGNITION MODEL COMPRESSION. Available at: http://sigport.org/5434.
Ahmed Tewfik, Raj Pawate. (2020). "SPEECH RECOGNITION MODEL COMPRESSION." Web.
1. Ahmed Tewfik, Raj Pawate. SPEECH RECOGNITION MODEL COMPRESSION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5434

Acoustic Matching by Embedding Impulse Responses


The goal of acoustic matching is to transform an audio recording made in one acoustic environment to sound as if it had been recorded in a different environment, based on reference audio from the target environment. This paper introduces a deep learning solution for two parts of the acoustic matching problem. First, we characterize acoustic environments by mapping audio into a low-dimensional embedding invariant to speech content and speaker identity.

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Authors:
Adam Finkelstein
Submitted On:
23 May 2020 - 11:34am
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[1] Adam Finkelstein, "Acoustic Matching by Embedding Impulse Responses", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5433. Accessed: Oct. 24, 2020.
@article{5433-20,
url = {http://sigport.org/5433},
author = {Adam Finkelstein },
publisher = {IEEE SigPort},
title = {Acoustic Matching by Embedding Impulse Responses},
year = {2020} }
TY - EJOUR
T1 - Acoustic Matching by Embedding Impulse Responses
AU - Adam Finkelstein
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5433
ER -
Adam Finkelstein. (2020). Acoustic Matching by Embedding Impulse Responses. IEEE SigPort. http://sigport.org/5433
Adam Finkelstein, 2020. Acoustic Matching by Embedding Impulse Responses. Available at: http://sigport.org/5433.
Adam Finkelstein. (2020). "Acoustic Matching by Embedding Impulse Responses." Web.
1. Adam Finkelstein. Acoustic Matching by Embedding Impulse Responses [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5433

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