Intelligibility of ideal binary masked noisy speech was measured on a group of normal hearing individuals across mixture signal to noise ratio (SNR) levels, masker types, and local criteria for forming the binary mask. The binary mask is computed from time-frequency decompositions of target and masker signals using two different schemes: an ideal binary mask computed by thresholding the local S...

The ideal binary mask is often seen as a goal for time-frequency masking algorithms trying to increase speech intelligibility, but the required availability of the unmixed signals makes it difficult to calculate the ideal binary mask in any real-life applications. In this paper we derive the theory and the requirements to enable calculations of the ideal binary mask using a directional system w...

This paper provides a comparison of binary mask estimation techniques, based on different ways of estimating the instantaneous SNR. The effect of six different gain functions and three noise estimation algorithms on estimating the SNR, and subsequently the binary mask was assessed. New criteria are proposed for classifying time-frequency bins as belonging to the target or masker signals. Senten...

Separation of competing speech is a key challenge in signal processing and a feat routinely performed by the human auditory brain. A long standing benchmark of the spectrogram approach to source separation is known as the ideal binary mask. Here, we train a convolutional deep neural network, on a twospeaker cocktail party problem, to make probabilistic predictions about binary masks. Our result...

Additive noise has long been an issue for robust automatic speech recognition (ASR) systems. One approach to noise robustness is the removal of noise information through segregation by binary time-frequency masks; each time-frequency unit in a spectro-temporal representation of the speech signal is labeled either noise-dominant or signal-dominant. The noise-dominant units are masked and their e...

In this paper, we attempt to generalize the ideal binary mask (IBM) estimation to the ideal ratio mask (IRM) estimation. Under binary masking, the error in IBM estimation may greatly distort the original speech spectrum. The main purpose of this paper is using ratio mask to smooth this negative impact. Since the key issue is the noise tracking, we firstly use exponential distributions to model ...

For a given mixture of speech and noise, an ideal binary time-frequency mask is constructed by comparing speech energy and noise energy within local time-frequency units. It is observed that listeners achieve nearly perfect speech recognition from gated noise with binary gains prescribed by the ideal binary mask. Only 16 filter channels and a frame rate of 100 Hz are sufficient for high intelli...

What is the computational goal of auditory scene analysis? This is a key issue to address in the Marrian information-processing framework. It is also an important question for researchers in computational auditory scene analysis (CASA) because it bears directly on how a CASA system should be evaluated. In this chapter I discuss different objectives used in CASA. I suggest as a main CASA goal th...

The application of the ideal binary mask to an auditory mixture has been shown to yield substantial improvements in intelligibility. This mask is commonly applied to the time-frequency (T-F) representation of a mixture signal and eliminates portions of a signal below a signal-to-noise-ratio (SNR) threshold while allowing others to pass through intact. The factors influencing intelligibility of ...