Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transfor...

The short-time Fourier transform (STFT) provides the foundation of binary-mask based audio source separation approaches. In computing a spectrogram, the STFT window size parameterizes the trade-off between time and frequency resolution. However, it is not yet known how this parameter affects the operation of the binary mask in terms of separation quality for real-world signals such as speech or...

In the field of nonstationary signal analysis and processing, short-time Fourier transform (STFT) is frequently used to convert signals into time-frequency domain. The instantaneous frequency (IF) STFT defined as time derivative phase plays an important role in reassignment method synchrosqueezing (SST). this paper, we propose a framework design window function for computing IF STFT. Computing ...

This paper presents an approach to the direction-of-arrival (DOA) estimation problem in acoustic environments using microphone arrays. It works in the short-time Fourier transform (STFT) domain. It first transforms the noisy speech signals received at the array into the STFT domain. A Householder transformation is then constructed and applied to the multichannel STFT coefficients in each subban...

This paper proposes a new estimator for evolutionary spectrum (ES) based on short time Fourier transform (STFT) and modified group delay function (GDFM). The STFT due to its built-in averaging suppresses the crossterms and the GDFM preserves the frequency resolution of the rectangular window as it reduces the Gibbs ripple without using any window function. The new estimator is applicable to ran...

The Short Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT) of accelerometer signals measured in patients with epilepsy are analyzed. Characteristics of the spectrogram and scalogram are studied in order to get more insight in how these transforms might be useful to derive suitable features for myoclonic seizure detection. First some artificial elementary patterns that re...

I consider the problem of decomposing a time series into its constituent components. In general, this is underdetermined, and assumptions must be made. Previous attempts include applying nonnegative matrix factorization to the spectrograms of isolated sources to learn a basis for each. A new time series composed of a sum of these sources is then projected onto these bases. For this project, I a...

We propose a learning-based postfilter to reconstruct the high-fidelity spectral texture in short-term Fourier transform (STFT) spectrograms. In speech-processing systems, such as speech synthesis, conversion, enhancement, separation, and coding, STFT spectrograms have been widely used as key acoustic representations. In these tasks, we normally need to precisely generate or predict the represe...

Deep learning based speech enhancement in the short-time Fourier transform (STFT) domain typically uses a large window length such as 32 ms. A larger can lead to higher frequency resolution and potentially better enhancement. This however incurs an algorithmic latency of ms online setup, because overlap-add algorithm used inverse STFT (iSTFT) is also performed using same size. To reduce this in...