Analysis of non stationary signals like audio, speech and biomedical signals require good resolution both in time and frequency as their spectral components are not fixed. There are many applications of time-frequency analysis in non stationary signals like source separation, signal denoising etc. This paper presents an application of time frequency analysis using STFT, Short Time Fourier Trans...

Analysis of non stationary signals like audio, speech and biomedical signals require good resolution both in time and frequency as their spectral components are not fixed. There are many applications of time-frequency analysis in non stationary signals like source separation, signal denoising etc. This paper presents an application of time frequency analysis using STFT, Short Time Fourier Trans...

In this paper, a binary mask estimation algorithm is proposed based on modulations of speech. A multi-resolution spectrotemporal analytical auditory model is utilized to extract modulation features to estimate the binary mask, which is often used in speech segregation applications. The proposed method estimates noise from the beginning of each test sentence, a common approach seen in many conve...

Missing-data strategies have been developed to improve the noise-robustness of automatic speech recognition systems in adverse acoustic conditions. This is achieved by classifying time-frequency (T-F) units into reliable and unreliable components, as indicated by a so-called binary mask. Different approaches have been proposed to handle unreliable feature components, each with distinct advantag...

Land use and cover change (LUCC) is important for the global biogeochemical cycle and ecosystem. This paper introduced a change detection method based on a bi-band binary mask and an improved fuzzy c-means algorithm to research the LUCC. First, the bi-band binary mask approach with the core concept being the correlation coefficients between bands from different images are used to locate target ...

Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is domina...

This research focuses on the removal of the singing voice in polyphonic audio recordings under real-time constraints. It is based on time-frequency binary masks resulting from the combination of azimuth, phase difference and absolute frequency spectral bin classification and harmonic-derived masks. For the harmonic-derived masks, a pitch likelihood estimation technique based on Tikhonov regular...

Using the computational auditory scene analysis (CASA) as a framework, a novel speech separation approach based on dual-microphone energy difference (DMED) is proposed for close-talk system. The energy levels of the two microphones are calculated in time-frequency (T-F) units. The DMEDs are calculated as the energy level ratio between the two microphones, and used as a cue to estimate the signa...

Significant advances in speech separation have been made by formulating it as a classification problem, where the desired output is the ideal binary mask (IBM). Previous work does not explicitly model the correlation between neighboring time-frequency units and standard binary classifiers are used. As one of the most important characteristics of speech signal is its temporal dynamics, the IBM c...

We demonstrate an approach for fabricating photonic crystals with large three-dimensional photonic band gaps (PBG's) using single-exposure, single-beam, optical interference lithography based on diffraction of light through an optical phase mask. The optical phase mask (OPM) consists of two orthogonally oriented binary gratings joined by a thin, solid layer of homogeneous material. Illuminating...