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Bioacoustics and Medical Acoustics

QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL


The performance of voice-based systems for remote monitoring of Parkinson’s disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy.

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Authors:
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little
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12 May 2019 - 6:13pm
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Infinite hidden Markov model for quality control

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[1] Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little, "QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4471. Accessed: Aug. 17, 2019.
@article{4471-19,
url = {http://sigport.org/4471},
author = {Amir Hossein Poorjam; Yordan P. Raykov; Reham Badawy; Jesper Rindom Jensen; Mads Græsbøll Christensen; Max A. Little },
publisher = {IEEE SigPort},
title = {QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL},
year = {2019} }
TY - EJOUR
T1 - QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL
AU - Amir Hossein Poorjam; Yordan P. Raykov; Reham Badawy; Jesper Rindom Jensen; Mads Græsbøll Christensen; Max A. Little
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4471
ER -
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little. (2019). QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL. IEEE SigPort. http://sigport.org/4471
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little, 2019. QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL. Available at: http://sigport.org/4471.
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little. (2019). "QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL." Web.
1. Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little. QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4471

Towards disease-specific speech markers for differential diagnosis in Parkinsonism


Parkinsonism refers to Parkinson’s Disease (PD) and Atypical Parkinsonian Syndromes (APS), such as Progressive
Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Discrimination between PD and APS and within
APS groups in early disease stages is a very challenging task. Interestingly, speech disorder is frequently an early and
prominent clinical feature of both PD and APS. This renders speech/voice analysis a promising tool for the development of

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11 May 2019 - 9:42am
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[1] , "Towards disease-specific speech markers for differential diagnosis in Parkinsonism", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4442. Accessed: Aug. 17, 2019.
@article{4442-19,
url = {http://sigport.org/4442},
author = { },
publisher = {IEEE SigPort},
title = {Towards disease-specific speech markers for differential diagnosis in Parkinsonism},
year = {2019} }
TY - EJOUR
T1 - Towards disease-specific speech markers for differential diagnosis in Parkinsonism
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4442
ER -
. (2019). Towards disease-specific speech markers for differential diagnosis in Parkinsonism. IEEE SigPort. http://sigport.org/4442
, 2019. Towards disease-specific speech markers for differential diagnosis in Parkinsonism. Available at: http://sigport.org/4442.
. (2019). "Towards disease-specific speech markers for differential diagnosis in Parkinsonism." Web.
1. . Towards disease-specific speech markers for differential diagnosis in Parkinsonism [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4442

Segmentation, Classification, and Visualization of Orca Calls using Deep Learning


Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater hydrophones and made it publicly available on the Orchive. There exist over 15 000 manually extracted orca/noise annotations and about 20 000 h unseen audio data. To analyze the behavior and communication of killer whales we need to interpret the different call types.

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Hendrik Schröter, Elmar Nöth, Andreas Maier, Rachael Cheng, Volker Barth, Christian Bergler
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10 May 2019 - 9:46am
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[1] Hendrik Schröter, Elmar Nöth, Andreas Maier, Rachael Cheng, Volker Barth, Christian Bergler, "Segmentation, Classification, and Visualization of Orca Calls using Deep Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4326. Accessed: Aug. 17, 2019.
@article{4326-19,
url = {http://sigport.org/4326},
author = {Hendrik Schröter; Elmar Nöth; Andreas Maier; Rachael Cheng; Volker Barth; Christian Bergler },
publisher = {IEEE SigPort},
title = {Segmentation, Classification, and Visualization of Orca Calls using Deep Learning},
year = {2019} }
TY - EJOUR
T1 - Segmentation, Classification, and Visualization of Orca Calls using Deep Learning
AU - Hendrik Schröter; Elmar Nöth; Andreas Maier; Rachael Cheng; Volker Barth; Christian Bergler
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4326
ER -
Hendrik Schröter, Elmar Nöth, Andreas Maier, Rachael Cheng, Volker Barth, Christian Bergler. (2019). Segmentation, Classification, and Visualization of Orca Calls using Deep Learning. IEEE SigPort. http://sigport.org/4326
Hendrik Schröter, Elmar Nöth, Andreas Maier, Rachael Cheng, Volker Barth, Christian Bergler, 2019. Segmentation, Classification, and Visualization of Orca Calls using Deep Learning. Available at: http://sigport.org/4326.
Hendrik Schröter, Elmar Nöth, Andreas Maier, Rachael Cheng, Volker Barth, Christian Bergler. (2019). "Segmentation, Classification, and Visualization of Orca Calls using Deep Learning." Web.
1. Hendrik Schröter, Elmar Nöth, Andreas Maier, Rachael Cheng, Volker Barth, Christian Bergler. Segmentation, Classification, and Visualization of Orca Calls using Deep Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4326

Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing


Sleep-disordered breathing (SDB) is a serious and prevalent condition, and acoustic analysis via consumer devices (e.g. smartphones) offers a low-cost solution to screening for it. We present a novel approach for the acoustic identification of SDB sounds, such as snoring, using bottleneck features learned from a corpus of whole-night sound recordings. Two types of bottleneck features are described, obtained by applying a deep autoencoder to the output of an auditory model or a short-term autocorrelation analysis.

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Authors:
Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan
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8 May 2019 - 9:03am
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[1] Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan, "Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4089. Accessed: Aug. 17, 2019.
@article{4089-19,
url = {http://sigport.org/4089},
author = {Hector E. Romero; Ning Ma; Guy J. Brown; Amy V. Beeston; Madina Hasan },
publisher = {IEEE SigPort},
title = {Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing},
year = {2019} }
TY - EJOUR
T1 - Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing
AU - Hector E. Romero; Ning Ma; Guy J. Brown; Amy V. Beeston; Madina Hasan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4089
ER -
Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan. (2019). Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing. IEEE SigPort. http://sigport.org/4089
Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan, 2019. Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing. Available at: http://sigport.org/4089.
Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan. (2019). "Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing." Web.
1. Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan. Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4089

A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE


Mitotic event detection is a fundamental step in investigating of cell behaviors. The event can be used to analyze various diseases, but most mitotic event detections performed previously focused only on two-dimensional (2D) images with time information. Owing to the complex background (normal cells) and mitotic event orientations, the 2D detection methods yield many false positive and false negative results. To solve this problem, we proposed a 2.5 dimensional (2.5D) cascaded end-to-end network combined with 3D anchors for accurate detection of mitotic events in 4D microscopic images.

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Titinunt Kitrungrotsakul, Yutaro Iwamoto, Xian-Hau Han, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen
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7 May 2019 - 11:14pm
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[1] Titinunt Kitrungrotsakul, Yutaro Iwamoto, Xian-Hau Han, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen, "A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3999. Accessed: Aug. 17, 2019.
@article{3999-19,
url = {http://sigport.org/3999},
author = {Titinunt Kitrungrotsakul; Yutaro Iwamoto; Xian-Hau Han; Satoko Takemoto; Hideo Yokota; Sari Ipponjima; Tomomi Nemoto; Xiong Wei; Yen-Wei Chen },
publisher = {IEEE SigPort},
title = {A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE},
year = {2019} }
TY - EJOUR
T1 - A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE
AU - Titinunt Kitrungrotsakul; Yutaro Iwamoto; Xian-Hau Han; Satoko Takemoto; Hideo Yokota; Sari Ipponjima; Tomomi Nemoto; Xiong Wei; Yen-Wei Chen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3999
ER -
Titinunt Kitrungrotsakul, Yutaro Iwamoto, Xian-Hau Han, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen. (2019). A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE. IEEE SigPort. http://sigport.org/3999
Titinunt Kitrungrotsakul, Yutaro Iwamoto, Xian-Hau Han, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen, 2019. A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE. Available at: http://sigport.org/3999.
Titinunt Kitrungrotsakul, Yutaro Iwamoto, Xian-Hau Han, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen. (2019). "A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE." Web.
1. Titinunt Kitrungrotsakul, Yutaro Iwamoto, Xian-Hau Han, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen. A CASCADE OF CNN AND LSTM NETWORK WITH 3D ANCHORS FOR MITOTIC CELL DETECTION IN 4D MICROSCOPIC IMAGE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3999

A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES

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Authors:
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen
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20 April 2018 - 3:14am
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[1] Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, "A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3090. Accessed: Aug. 17, 2019.
@article{3090-18,
url = {http://sigport.org/3090},
author = {Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES},
year = {2018} }
TY - EJOUR
T1 - A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES
AU - Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3090
ER -
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES. IEEE SigPort. http://sigport.org/3090
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, 2018. A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES. Available at: http://sigport.org/3090.
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). "A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES." Web.
1. Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. A SUPERVISED APPROACH TO GLOBAL SIGNAL-TO-NOISE RATIO ESTIMATION FOR WHISPERED AND PATHOLOGICAL VOICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3090

A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES

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Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen
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20 April 2018 - 3:11am
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[1] Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, "A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3089. Accessed: Aug. 17, 2019.
@article{3089-18,
url = {http://sigport.org/3089},
author = {Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES},
year = {2018} }
TY - EJOUR
T1 - A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES
AU - Amir Hossein Poorjam; Max A. Little; Jesper Rindom Jensen; Mads Græsbøll Christensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3089
ER -
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES. IEEE SigPort. http://sigport.org/3089
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen, 2018. A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES. Available at: http://sigport.org/3089.
Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. (2018). "A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES." Web.
1. Amir Hossein Poorjam, Max A. Little, Jesper Rindom Jensen, Mads Græsbøll Christensen. A PARAMETRIC APPROACH FOR CLASSIFICATION OF DISTORTIONS IN PATHOLOGICAL VOICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3089

BirdVox-full-night: a dataset and website for avian flight call detection.


This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors.
Thus far, owing to the lack of annotated continuous recordings, existing methods had been benchmarked in a binary classification setting (presence vs. absence).
Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors.

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Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello
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17 April 2018 - 3:54pm
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[1] Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello, "BirdVox-full-night: a dataset and website for avian flight call detection.", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2941. Accessed: Aug. 17, 2019.
@article{2941-18,
url = {http://sigport.org/2941},
author = {Vincent Lostanlen; Justin Salamon; Andrew Farnsworth; Steve Kelling; and Juan Pablo Bello },
publisher = {IEEE SigPort},
title = {BirdVox-full-night: a dataset and website for avian flight call detection.},
year = {2018} }
TY - EJOUR
T1 - BirdVox-full-night: a dataset and website for avian flight call detection.
AU - Vincent Lostanlen; Justin Salamon; Andrew Farnsworth; Steve Kelling; and Juan Pablo Bello
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2941
ER -
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. (2018). BirdVox-full-night: a dataset and website for avian flight call detection.. IEEE SigPort. http://sigport.org/2941
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello, 2018. BirdVox-full-night: a dataset and website for avian flight call detection.. Available at: http://sigport.org/2941.
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. (2018). "BirdVox-full-night: a dataset and website for avian flight call detection.." Web.
1. Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. BirdVox-full-night: a dataset and website for avian flight call detection. [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2941

ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF


This paper proposes a novel approach for robustly detecting
multiply repeating audio events in monitoring recordings.
We consider the practically important case that
the sequence of inter onset intervals between subsequent events
is not constant but differs by some jitter. In such cases
classical approaches based on autocorrelation (ACF) are
of limited use. To overcome this problem we propose to use
ACF together with a variant of dynamic time warping. Combining
both techniques in an iterative algorithm, we obtain a

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Kevin Wilkinghoff
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14 April 2018 - 6:10am
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[1] Kevin Wilkinghoff, "ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2815. Accessed: Aug. 17, 2019.
@article{2815-18,
url = {http://sigport.org/2815},
author = {Kevin Wilkinghoff },
publisher = {IEEE SigPort},
title = {ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF},
year = {2018} }
TY - EJOUR
T1 - ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF
AU - Kevin Wilkinghoff
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2815
ER -
Kevin Wilkinghoff. (2018). ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF. IEEE SigPort. http://sigport.org/2815
Kevin Wilkinghoff, 2018. ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF. Available at: http://sigport.org/2815.
Kevin Wilkinghoff. (2018). "ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF." Web.
1. Kevin Wilkinghoff. ROBUST DETECTION OF JITTERED MULTIPLY REPEATING AUDIO EVENTS USING ITERATED TIME-WARPED ACF [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2815

Verbal Protest Recognition in Children with Autism


Real-time detection of verbal protest (sensory overload-induced crying) in children with autism is a first step towards understanding the precursors of challenging behaviors associated with autism. Detection of verbal protest is useful for both autism researchers interested in exploring just-in-time intervention techniques and researchers interested in audio event detection in routine living environments.In this paper, we examine, adapt, and improve upon two techniques for verbal protest recognition and tailor them for children with autism spectrum disorder (ASD).

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Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das
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13 April 2018 - 12:25pm
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[1] Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das, "Verbal Protest Recognition in Children with Autism", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2725. Accessed: Aug. 17, 2019.
@article{2725-18,
url = {http://sigport.org/2725},
author = {Jonah Casebeer; Hillol Sarker; Murtaza Dhuliawala; Nicholas Fay; Mary Pietrowicz; Amar Das },
publisher = {IEEE SigPort},
title = {Verbal Protest Recognition in Children with Autism},
year = {2018} }
TY - EJOUR
T1 - Verbal Protest Recognition in Children with Autism
AU - Jonah Casebeer; Hillol Sarker; Murtaza Dhuliawala; Nicholas Fay; Mary Pietrowicz; Amar Das
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2725
ER -
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das. (2018). Verbal Protest Recognition in Children with Autism. IEEE SigPort. http://sigport.org/2725
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das, 2018. Verbal Protest Recognition in Children with Autism. Available at: http://sigport.org/2725.
Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das. (2018). "Verbal Protest Recognition in Children with Autism." Web.
1. Jonah Casebeer, Hillol Sarker, Murtaza Dhuliawala, Nicholas Fay, Mary Pietrowicz, Amar Das. Verbal Protest Recognition in Children with Autism [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2725

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