Generalized Refinement of Short-Term Fourier Spectra in Time- and Frequency- Domain and its Combination with Polyphase Filterbanks

Krini, Mohammed; Madhu, Nilesh

doi:10.1109/isspit47144.2019.9001796

Cited by 1 publication

(2 citation statements)

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“…This approach was shown to have the following advantages over the straightforward alternative: (a) it is computationally significantly less expensive; (b) it can be configured to produce either the full, high resolution spectrum or only compute the refined spectrum for a subset of desired frequencies -leading to a further reduction in computational expense, without compromising on the featureextraction performance. This approach was generalised and further extended to the case of polyphase filterbanks in [12] and the benefit of this extension for the case of F 0 estimation was demonstrated in [13]. However, a shortcoming of using a limited-size data-window is the restriction imposed by the Heisenberg-Gabor limit (see e.g.…”

Section: Introductionmentioning

confidence: 99%

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Spectral refinement with adaptive window-size selection for voicing detection and fundamental frequency estimation

Madhu

Krini

2020

2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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Spectral refinement (SR) offers a computationally inexpensive means of generating a refined (higher resolution) signal spectrum by linearly combining the spectra of shorter, contiguous signal segments. The benefit of this method has previously been demonstrated on the problem of fundamental frequency (F0) estimation in speech processing -specifically for the improved estimation of very low F0. One drawback of SR is, however, the poorer detection of voicing onsets due to the Heisenberg-Gabor limit on time and frequency resolution. This may also lead to degraded performance in noisy conditions. Transitioning between long-and short-time windows for the spectral analysis may offer a good trade-off in these situations. This contribution presents a method to adaptively switch between short-and longtime windows (and, correspondingly, between the short-term and the refined spectrum) for voicing detection and F0 estimation. The improvements in voicing detection and F0 estimation due to this adaptive switching is conclusively demonstrated on audio signals in clean and corrupted conditions.

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Section: Introductionmentioning

confidence: 99%

“…Whereas we consider here the sub-band decomposition by the use of the discrete Fourier transform, the results may also be extended to the case of filterbanks (e.g. [12]).…”

Section: Introductionmentioning

confidence: 99%