Optimized neural networks for modeling of loudspeaker directivity diagrams

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschung

Authors

For the electro-acoustical simulation of sound reinforcement systems, calculation and simulation of the sound field distribution requires measurement and storage of the frequency dependent directivity characteristics (level and phase) of the used loudspeaker models. In modern simulation programs, the spatial resolution can be less than five degrees in third - or even twelfth - octave frequency bands. Therefore, modeling of the directivity diagram of loudspeakers can reduce storage place and simulation time and may even increase the accuracy of the simulation. Modeling - in the sense of mapping the resulting enormous amount of measured data - can be realized very efficiently and with small approximation error using second order neural networks. To reduce the model development time, we in addition created a new adaptation rule for feedforward neural networks with improved convergence behavior. This is achieved only by using the training data and the output error to analytically determine values for the learning parameters momentum and learning rate in each learning step. We will show the advantages of using neural networks with optimized learning parameters by the example of modeling measured directional response patterns of two real loudspeakers. For measurement we used maximum length sequences (MLSSA).

OriginalspracheEnglisch
Titel2001 IEEE International Conference on Acoustics, Speech, and Signal Processing : Proceedings
Anzahl der Seiten4
Band2
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum05.2001
Seiten1285-1288
ISBN (Print)0-7803-7041-4 , 0-7803-7042-2
DOIs
PublikationsstatusErschienen - 05.2001
VeranstaltungIEEE International Conference on Acoustics, Speech, and Signal Processing 2001 - Salt Lake City, USA / Vereinigte Staaten
Dauer: 07.05.200111.05.2001

Bibliographische Notiz

Titel d. Bandes: Speech processing 2, industry technology track, design & implementation of signal processing systems, neural networks for signal processing. ISSN: 1520-6149

DOI