6 accepted papers
Artificial bandwidth extension (ABE) algorithms have been developed to estimate missing highband frequency components (4-8kHz) to improve quality of narrowband (0-4kHz) telephone calls. Most ABE solutions employ deep neural networks (DNNs) due to their well-known ability to model highly complex, non…
Artificial bandwidth extension (ABE) algorithms can improve speech quality when wideband devices are used with narrowband devices or infrastructure. Most ABE solutions employ some form of memory, implying high-dimensional feature representations that increase both latency and complexity. Dimensional…
Many smart devices now support high-quality speech communication services at super-wide bandwidths. Often, however, speech quality is degraded when they are used with networks or devices which lack super-wideband support. Artificial bandwidth extension can then be used to improve speech quality. Whi…
Artificial bandwidth extension (ABE) algorithms have been developed to improve speech quality when wideband devices are used in conjunction with narrowband devices or infrastructure. While past work points to the benefit of using contextual information or memory for ABE, an understanding of the rela…
Most artificial bandwidth extension (ABE) algorithms are based on the classical source-filter model of speech production. This approach generally requires the dual extension of each component through independent processing. Alternative approaches reported recently operate on the spectrum. With human…
In this paper, we propose a novel algorithm which uses simple lowpass filtering as pre-processing for detection of epochs. Lowpass filtering with an appropriate cut-off frequency removes the effect of vocal tract characteristics as formants lie in relatively higher frequency regions. The method is e…