An adaptive time-frequency analysis scheme for improved real-time speech enhancement

In this paper, an adaptive time-frequency analysis scheme is proposed along with a synthesis scheme using an asymmetric window. The proposed scheme is suitable for audio noise reduction with a low delay in the range of 0 to 4 ms. The main novelty of the paper is the adaptive analysis scheme that can adapt to the incoming signal independently in both time and frequency by employing a complex filter on a DFT modulated filter bank. A number of adaptive time-frequency schemes are described that are suitable for low delay and low computational complexity. The adaptive time-frequency scheme is used for the computation of noise reduction gain factors, which are then adopted in a nonadaptive analysis/synthesis scheme. The synthesis scheme uses an asymmetric window to achieve a good tradeoff between low delay and a sharp frequency response. Examples are given of the adaptive analysis and measurements of the synthesis scheme are given to show that the filter bank has a gain dependent nonlinear phase response. Finally, a noise reduction task is performed that shows good performance compared to reference implementations in terms of segmental SNR and PESQ.

show abstract

“…The non-stationarity test is based on the criteria from [17] which we adapted to our purpose in [18]:…”

Section: Setting the Filter Coefficients For A Time-varying Filtermentioning

confidence: 99%

Adaptive Time-Frequency Analysis for Noise Reduction in an Audio Filter Bank With Low Delay

Andersen

Moonen

2016

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous work has focused on automatically finding optimal STFT parameters, e.g. in using dynamic programming to obtain the best window positions [3], or to detect signal non-stationarity to adjust the analysis parameters [4]. However, most of that work is based on heuristics and local search, and is not compatible with gradient descent-style optimization that can be used to jointly optimize entire end-to-end systems.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Short-Time Fourier Transform Parameters via Gradient Descent

Zhao

Subramani

Smaragdis

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

The Short-Time Fourier Transform (STFT) has been a staple of signal processing, often being the first step for many audio tasks. A very familiar process when using the STFT is the search for the best STFT parameters, as they often have significant side effects if chosen poorly. These parameters are often defined in terms of an integer number of samples, which makes their optimization non-trivial. In this paper we show an approach that allows us to obtain a gradient for STFT parameters with respect to arbitrary cost functions, and thus enable the ability to employ gradient descent optimization of quantities like the STFT window length, or the STFT hop size. We do so for parameter values that stay constant throughout an input, but also for cases where these parameters have to dynamically change over time to accommodate varying signal characteristics.

show abstract

Optimal Short-Time Fourier Transform's Parameters and Gaussian window for Enhancing Signal Separation

Aziez,

Khaleefah,

Abed

et al. 2022

2022 2nd International Conference on Advances in Engineering Science and Technology (AEST)

View full text Add to dashboard Cite

An adaptive time-frequency analysis scheme for improved real-time speech enhancement

Cited by 4 publications

References 7 publications

Adaptive Time-Frequency Analysis for Noise Reduction in an Audio Filter Bank With Low Delay

Adaptive Time-Frequency Analysis for Noise Reduction in an Audio Filter Bank With Low Delay

Optimizing Short-Time Fourier Transform Parameters via Gradient Descent

Optimal Short-Time Fourier Transform's Parameters and Gaussian window for Enhancing Signal Separation

Contact Info

Product

Resources

About