Name
Title | ||
|---|---|---|
A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams |
Abstract | ||
|---|---|---|
To overcome the computational complexity of the asynchronous hidden Markov model (AHMM), we present a novel multidimensional dynamic time warping (DTW) algorithm for hybrid fusion of asynchronous data. We show that our newly introduced multidimensional DTW concept requires significantly less decoding time while providing the same data fusion flexibility as the AHMM. Thus, it can be applied in a wide range of real-time multimodal classification tasks. Optimally exploiting mutual information during decoding even if the input streams are not synchronous, our algorithm outperforms late and early fusion techniques in a challenging bimodal speech and gesture fusion experiment. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neucom.2009.08.005 | Neurocomputing |
Keywords | Field | DocType |
markov model,hybrid fusion,data fusion flexibility,challenging bimodal speech,novel multidimensional dynamic time,decoding time,efficient multimodal fusion,multidimensional dtw concept,asynchronous data stream,asynchronous data,gesture fusion experiment,early fusion technique,computational complexity,mutual information,hidden markov model,data fusion,dynamic time warping,real time | Data stream mining,Dynamic time warping,Computer science,Artificial intelligence,Asynchronous communication,Pattern recognition,Algorithm,Speech recognition,Sensor fusion,Mutual information,Decoding methods,Hidden Markov model,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
73 | 1-3 | Neurocomputing |
Citations | PageRank | References |
33 | 1.56 | 51 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Wöllmer | 1 | 1359 | 81.78 |
| Marc Al-Hames | 2 | 116 | 8.75 |
| Florian Eyben | 3 | 2854 | 141.87 |
| Björn Schuller | 4 | 6749 | 463.50 |
| Gerhard Rigoll | 5 | 2788 | 268.87 |