Refine
Document Type
- Doctoral thesis (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Language
- English (2)
Keywords
- Störsignal (2) (remove)
Institute
This dissertation presents a novel technique for enhancing the spurious-free dynamic range of radio monitoring receivers with a bandwidth of several gigahertz. The improvement is based on suppressing the discrete spurious signals that occur at higher drive levels in the output spectrum of the required broadband analog-to-digital converter. The achievable improvement of the spurious-free dynamic range is typically more than 20 dB, allowing better, gap-free monitoring of wide frequency ranges. The attenuation of the spurious signals is achieved by a unique combination of two separate analog-to-digital converters working in parallel and subsequent digital signal processing. The developed solution is not limited to broadband radio monitoring receivers but can generally suppress certain types of spurious signals generated during analog-to-digital conversion.
The first part of this thesis is devoted to the evaluation of approaches which exploit the inherent redundancy of the speech signal to improve the noise robustness. On the basis of this evaluation on the AURORA 2000 database, we further study in detail two of the evaluated approaches. The first of these approaches is the hybrid RBF/HMM approach, which is an attempt to combine the superior classification performance of radial basis functions (RBFs) with the ability of HMMs to model time variation. The second approach is using neural networks to non-linearly reduce the dimensionality of large feature vectors including context frames. We propose the use of different MLP topologies for that purpose. Experiments on the AURORA 2000 database reveal that the performance of the first approach is similar to the performance of systems based on SCHMMs. The second approach cannot outperform the performance of linear discriminant analysis (LDA) on a database recorded in real car environments, but it is on average significantly better than LDA on the AURORA 2000 database.