Hochschulschrift
Signal reconstruction from multiresolution magnitude spectrograms for audio signal processing
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISBN
-
9783844025002
- Maße
-
21 cm, 275 g
- Umfang
-
IX, 171 S.
- Sprache
-
Englisch
- Anmerkungen
-
graph. Darst.
Zugl.: Aachen, Techn. Hochsch., Diss., 2013
- Erschienen in
-
Aachen series on multimedia and communications engineering ; Vol. 13
- Klassifikation
-
Elektrotechnik, Elektronik
- Schlagwort
-
Tonsignal
Akustische Signalverarbeitung
Signalregenerierung
Akustisches Spektrum
Kurzzeit-Fourier-Transformation
Fensterfunktion
- Inhaltsverzeichnis
- Rechteinformation
-
Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.
- Letzte Aktualisierung
- 11.06.2025, 12:00 UTC
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Hochschulschrift
Beteiligte
- Gnann, Volker
- Shaker
Entstanden
- 2014
Ähnliche Objekte (12)
Automatic General Audio Signal Classification
Parameter estimation and signal reconstruction
Fast recursive data-driven multi-resolution feature extraction for physiological signal classification
Underdetermined blind source separation for audio signals
Algorithms for audio signal segmentation and separation
Processing Music Signals Using Audio Decomposition Techniques
Quantum-assisted distortion-free audio signal sensing
MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
Signal enhancement and signal reconstruction for diffusion imaging using deep learning
Objective Quality Assessment of Perceptually Coded Audio Signals
Deep Representation Learning Techniques for Audio Signal Processing
Script-Signal, Signal, Block-Signal
Automatic General Audio Signal Classification
Parameter estimation and signal reconstruction
Fast recursive data-driven multi-resolution feature extraction for physiological signal classification
Underdetermined blind source separation for audio signals
Algorithms for audio signal segmentation and separation
Processing Music Signals Using Audio Decomposition Techniques
Quantum-assisted distortion-free audio signal sensing
MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
Signal enhancement and signal reconstruction for diffusion imaging using deep learning
Objective Quality Assessment of Perceptually Coded Audio Signals
Deep Representation Learning Techniques for Audio Signal Processing
Script-Signal, Signal, Block-Signal
Automatic General Audio Signal Classification
Parameter estimation and signal reconstruction
Fast recursive data-driven multi-resolution feature extraction for physiological signal classification
Underdetermined blind source separation for audio signals
Algorithms for audio signal segmentation and separation
Processing Music Signals Using Audio Decomposition Techniques
Quantum-assisted distortion-free audio signal sensing
MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
Signal enhancement and signal reconstruction for diffusion imaging using deep learning
Objective Quality Assessment of Perceptually Coded Audio Signals
Deep Representation Learning Techniques for Audio Signal Processing