Deep neural networks for acoustic emotion recognition: raising the benchmarks

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing: (ICASSP), 22-27 May 2011, Prague, Czech Republic, S. 5688-5691

Event: Veröffentlichung

(where): Augsburg, Piscataway, NJ

(who): Universität Augsburg, IEEE

(when): 2011

Creator: Stuhlsatz, André
Meyer, Christine
Eyben, Florian
Zielke, Thomas
Meier, Günter
Schuller, Björn

Contributor: Tichavský, Petr
Černocký, Jan
Procházka, Aleš
Jan, Jiří
Vích, Robert

DOI: 10.1109/ICASSP.2011.5947651

URN: urn:nbn:de:bvb:384-opus4-726940

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:53 PM CET

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Associated

Stuhlsatz, André
Meyer, Christine
Eyben, Florian
Zielke, Thomas
Meier, Günter
Schuller, Björn
Tichavský, Petr
Černocký, Jan
Procházka, Aleš
Jan, Jiří
Vích, Robert
Universität Augsburg, IEEE

Time of origin

2011

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Deep neural networks for acoustic emotion recognition: raising the benchmarks

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Other Objects (12)

Acoustic emotion recognition: a benchmark comparison of performances

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The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

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Acoustic emotion recognition: a benchmark comparison of performances

CCA based feature selection with application to continuous depression recognition from acoustic speech features

On-line continuous-time music mood regression with deep recurrent neural networks

Single-channel speech separation with memory-enhanced recurrent neural networks

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection

Likability of human voices: a feature analysis and a neural network regression approach to automatic likability estimation

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Automatic multi-lingual arousal detection from voice applied to real product testing applications

Combining long short-term memory and dynamic Bayesian networks for incremental emotion-sensitive artificial listening

Combining monaural source separation with long short-term memory for increased robustness in vocalist gender recognition

Acoustic emotion recognition: a benchmark comparison of performances

CCA based feature selection with application to continuous depression recognition from acoustic speech features

On-line continuous-time music mood regression with deep recurrent neural networks

Single-channel speech separation with memory-enhanced recurrent neural networks

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

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Combining monaural source separation with long short-term memory for increased robustness in vocalist gender recognition

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