Neural network approach to the cocktail party effect
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Imagine you have 2 people at 2 different microphones but in the same room. Each microphone is going to pick up some sound from the other person. Is there a good neural network based approach to isolating the signals so that the sound from each microphone only captures 1 person?
I remember hearing a solution to this a few years back, but Im not sure if I remember that correctly
I ask because a similar problem was mentioned to me today. During EEG brain wave data collection, each electrode can pick up signal from multiple sources in the brain. In that world they try to isolate the sources and reduce the "noise" from other brain areas, and its common to use ICA for such a task. The problem with ICA is that the post-processing stage is very time consuming, so I'm wondering if theres a better ANN/DNN approach that could solve the problem more efficiently, or maybe with better accuracy
machine-learning neural-network deep-learning
asked Mar 2 '18 at 0:32
SimonSimon
491523
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bumped to the homepage by Community♦ 34 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Imagine you have 2 people at 2 different microphones but in the same room. Each microphone is going to pick up some sound from the other person. Is there a good neural network based approach to isolating the signals so that the sound from each microphone only captures 1 person?
I remember hearing a solution to this a few years back, but Im not sure if I remember that correctly
I ask because a similar problem was mentioned to me today. During EEG brain wave data collection, each electrode can pick up signal from multiple sources in the brain. In that world they try to isolate the sources and reduce the "noise" from other brain areas, and its common to use ICA for such a task. The problem with ICA is that the post-processing stage is very time consuming, so I'm wondering if theres a better ANN/DNN approach that could solve the problem more efficiently, or maybe with better accuracy
machine-learning neural-network deep-learning
asked Mar 2 '18 at 0:32
SimonSimon
491523
$endgroup$
bumped to the homepage by Community♦ 34 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
A quick search yielded, inter alia, github.com/MTG/DeepConvSep github.com/posenhuang/deeplearningsourceseparation Follow the citations and you may find others.
$endgroup$
– Emre
Mar 2 '18 at 1:00
add a comment |
$begingroup$
Imagine you have 2 people at 2 different microphones but in the same room. Each microphone is going to pick up some sound from the other person. Is there a good neural network based approach to isolating the signals so that the sound from each microphone only captures 1 person?
I remember hearing a solution to this a few years back, but Im not sure if I remember that correctly
I ask because a similar problem was mentioned to me today. During EEG brain wave data collection, each electrode can pick up signal from multiple sources in the brain. In that world they try to isolate the sources and reduce the "noise" from other brain areas, and its common to use ICA for such a task. The problem with ICA is that the post-processing stage is very time consuming, so I'm wondering if theres a better ANN/DNN approach that could solve the problem more efficiently, or maybe with better accuracy
machine-learning neural-network deep-learning
asked Mar 2 '18 at 0:32
SimonSimon
491523
$endgroup$
Imagine you have 2 people at 2 different microphones but in the same room. Each microphone is going to pick up some sound from the other person. Is there a good neural network based approach to isolating the signals so that the sound from each microphone only captures 1 person?
I remember hearing a solution to this a few years back, but Im not sure if I remember that correctly
I ask because a similar problem was mentioned to me today. During EEG brain wave data collection, each electrode can pick up signal from multiple sources in the brain. In that world they try to isolate the sources and reduce the "noise" from other brain areas, and its common to use ICA for such a task. The problem with ICA is that the post-processing stage is very time consuming, so I'm wondering if theres a better ANN/DNN approach that could solve the problem more efficiently, or maybe with better accuracy
machine-learning neural-network deep-learning
machine-learning neural-network deep-learning
asked Mar 2 '18 at 0:32
SimonSimon
491523
asked Mar 2 '18 at 0:32
SimonSimon
491523
asked Mar 2 '18 at 0:32
SimonSimon
491523
asked Mar 2 '18 at 0:32
SimonSimon
491523
asked Mar 2 '18 at 0:32
SimonSimon
491523
491523
bumped to the homepage by Community♦ 34 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 34 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
A quick search yielded, inter alia, github.com/MTG/DeepConvSep github.com/posenhuang/deeplearningsourceseparation Follow the citations and you may find others.
$endgroup$
– Emre
Mar 2 '18 at 1:00
add a comment |
$begingroup$
A quick search yielded, inter alia, github.com/MTG/DeepConvSep github.com/posenhuang/deeplearningsourceseparation Follow the citations and you may find others.
$endgroup$
– Emre
Mar 2 '18 at 1:00
$begingroup$
A quick search yielded, inter alia, github.com/MTG/DeepConvSep github.com/posenhuang/deeplearningsourceseparation Follow the citations and you may find others.
$endgroup$
– Emre
Mar 2 '18 at 1:00
$begingroup$
A quick search yielded, inter alia, github.com/MTG/DeepConvSep github.com/posenhuang/deeplearningsourceseparation Follow the citations and you may find others.
$endgroup$
– Emre
Mar 2 '18 at 1:00
add a comment |
1 Answer
1
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oldest
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$begingroup$
Take a look at this.
No DNN, but math, if different channels are available.
DNN were used for single channel input, but have to be trained to the signals you want to separate.
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
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add a comment |
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1 Answer
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$begingroup$
Take a look at this.
No DNN, but math, if different channels are available.
DNN were used for single channel input, but have to be trained to the signals you want to separate.
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
$endgroup$
add a comment |
$begingroup$
Take a look at this.
No DNN, but math, if different channels are available.
DNN were used for single channel input, but have to be trained to the signals you want to separate.
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
$endgroup$
add a comment |
$begingroup$
Take a look at this.
No DNN, but math, if different channels are available.
DNN were used for single channel input, but have to be trained to the signals you want to separate.
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
$endgroup$
Take a look at this.
No DNN, but math, if different channels are available.
DNN were used for single channel input, but have to be trained to the signals you want to separate.
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
edited May 28 '18 at 15:22
Stephen Rauch♦
1,52551330
1,52551330
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
answered May 28 '18 at 14:59
Simone GentaSimone Genta
1
1
add a comment |
add a comment |
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$begingroup$
A quick search yielded, inter alia, github.com/MTG/DeepConvSep github.com/posenhuang/deeplearningsourceseparation Follow the citations and you may find others.
$endgroup$
– Emre
Mar 2 '18 at 1:00