Deep recurrent neural network-based autoencoders for acoustic novelty detection
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: Computational Intelligence and Neuroscience, 2017, S. 4694860-
- Event
-
Veröffentlichung
- (where)
-
Augsburg
- (who)
-
Universität Augsburg
- (when)
-
2017
- Event
-
Veröffentlichung
- (where)
-
New York, NY
- (who)
-
Hindawi Limited
- (when)
-
2017
- Creator
- DOI
-
10.1155/2017/4694860
- URN
-
urn:nbn:de:bvb:384-opus4-719098
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:02 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Marchi, Erik
- Vesperini, Fabio
- Squartini, Stefano
- Schuller, Björn
- Universität Augsburg
- Hindawi Limited
Time of origin
- 2017
Other Objects (12)
Existence and L‐estimates for elliptic equations involving convolution
LSTM-based traffic load balancing and resource allocation for an edge system
Multipotent mesenchymal stromal cells: possible culprits in solid tumors?
Diabetic and Metabolic Programming: Mechanisms Altering the Intrauterine Milieu
Emotion-Aware Natural Interaction
Smiling in pain: explorations of its social motives
The Influence of Executive Functioning on Facial and Subjective Pain Responses in Older Adults
A Privacy Preserving Approach to Collaborative Systemic Risk Identification : The Use-Case of Supply Chain Networks
Successful treatment of a patient with COVID-19-induced severe ARDS, pneumothorax, and pneumomediastinum with awake vv-ECMO implantation
Softwarization of mobile network functions towards agile and energy efficient 5G architectures: a survey
Metacognitive strategies and test performance: an experience sampling analysis of students' learning behavior
Efficacy and interindividual variability in motor-cortex plasticity following anodal tDCS and paired-associative stimulation
Existence and L‐estimates for elliptic equations involving convolution
LSTM-based traffic load balancing and resource allocation for an edge system
Multipotent mesenchymal stromal cells: possible culprits in solid tumors?
Diabetic and Metabolic Programming: Mechanisms Altering the Intrauterine Milieu
Emotion-Aware Natural Interaction
Smiling in pain: explorations of its social motives
The Influence of Executive Functioning on Facial and Subjective Pain Responses in Older Adults
A Privacy Preserving Approach to Collaborative Systemic Risk Identification : The Use-Case of Supply Chain Networks
Successful treatment of a patient with COVID-19-induced severe ARDS, pneumothorax, and pneumomediastinum with awake vv-ECMO implantation
Softwarization of mobile network functions towards agile and energy efficient 5G architectures: a survey
Metacognitive strategies and test performance: an experience sampling analysis of students' learning behavior
Efficacy and interindividual variability in motor-cortex plasticity following anodal tDCS and paired-associative stimulation
Existence and L‐estimates for elliptic equations involving convolution
LSTM-based traffic load balancing and resource allocation for an edge system
Multipotent mesenchymal stromal cells: possible culprits in solid tumors?
Diabetic and Metabolic Programming: Mechanisms Altering the Intrauterine Milieu
Emotion-Aware Natural Interaction
Smiling in pain: explorations of its social motives
The Influence of Executive Functioning on Facial and Subjective Pain Responses in Older Adults
A Privacy Preserving Approach to Collaborative Systemic Risk Identification : The Use-Case of Supply Chain Networks
Successful treatment of a patient with COVID-19-induced severe ARDS, pneumothorax, and pneumomediastinum with awake vv-ECMO implantation
Softwarization of mobile network functions towards agile and energy efficient 5G architectures: a survey
Metacognitive strategies and test performance: an experience sampling analysis of students' learning behavior