Cees Taal

SKF · research and technology department

In the process industry, condition monitoring systems with automated fault diagnosis methods assisthuman experts and thereby improve maintenance efficiency, process sustainability, and workplace safety.Improving the automated fault diagnosis methods using data and machine learning-based models is a centralaspect of intelligent fault diagnosis (IFD)...

In recent years, data-driven techniques such as deep learning (DL), have been widely represented in the literature in the field of bearing vibration condition monitoring. While these approaches achieve excellent performance in detecting bearing faults on controlled laboratory datasets, there is little information available on their applicability to...

Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of labels and fault samples imposes a significant challenge for existing data-driven fault diagnosis meth...

Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of labels and fault samples imposes a significant challenge for existing data-driven fault diagnosis meth...

Intelligibility listening tests are necessary during development and evaluation of speech processing algorithms, despite the fact that they are expensive and time consuming. In this paper, we propose a monaural intelligibility prediction algorithm, which has the potential of replacing some of these listening tests. The proposed algorithm shows simi...

One way to improve speech understanding in noise for HI with a unilateral hearing loss is by contralateral routing of signals (CROS). Such a CROS-system captures sounds with an additional microphone at the worst hearing ear and transmits these to the better one. The better ear is then provided with a mix of signals originating from both ears. The g...

The presence of environmental additive noise in the vicinity of the user typically degrades the speech intelligibility of speech processing applications. This intelligibility loss can be compensated by properly preprocessing the speech signal prior to play-out, often referred to as near-end speech enhancement. Although the majority of such algorith...

Matlab implementation of the Short-Time Objective Intelligibility (STOI) measure described in C.H. Taal, R.C. Hendriks, R. Heusdens, J. Jensen 'A Short-Time Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech', ICASSP 2010, Texas, Dallas.

This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amp...

In this letter the focus is on linear filtering of speech before degradation due to additive background noise. The goal is to design the filter such that the speech intelligibility index (SII) is maximized when the speech is played back in a known noisy environment. Moreover, a power constraint is taken into account to prevent uncomfortable playbac...

A speech pre-processing algorithm is presented that improves the speech intelligibility in noise for the near-end listener. The algorithm improves intelligibility by optimally redistributing the speech energy over time and frequency according to a perceptual distortion measure, which is based on a spectro-temporal auditory model. Since this auditor...

A linear time-invariant filter is designed in order to improve speech understanding when the speech is played back in a noisy environment. To accomplish this, the speech intelligibility index (SII) is maximized under the constraint that the speech energy is held constant. A nonlinear approximation is used for the SII such that a closed-form solutio...

This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a prediction model based on the hypothesis that intelligibility is monotonically related to the the amount of Shannon information the critical-band amplitude en...

Perceptual models exploiting auditory masking are frequently used in audio and speech processing applications like coding and watermarking. In most cases, these models only take into account spectral masking in short-time frames. As a consequence, undesired audible artifacts in the temporal domain may be introduced (e.g., pre-echoes). In this artic...

A speech pre-processing algorithm is presented to improve the speech intelligibility in noise for the near-end listener. The algorithm improves the intelligibility by optimally redistributing the speech energy over time and frequency for a perceptual distortion measure, which is based on a spectro-temporal auditory model. In contrast to spectral-on...

In this paper the earlier proposed short-time objective intelligibility predictor (STOI) is simplified such that it can be expressed as a weighted ℓ2 norm in the auditory domain. Due to the mathematical properties of a norm, STOI can now be used with the matching pursuit algorithm in the n-of-m channel selection technique as found in several cochle...

Perceptual models exploiting auditory masking are frequently used in audio and speech processing applications like coding and watermarking. In most cases, these models only take into account spectral masking in short-time frames. As a consequence, undesired audible artifacts in the temporal domain may be introduced (e.g., pre-echoes). In this artic...

Existing objective speech-intelligibility measures are suitable for several types of degradation, however, it turns out that they are less appropriate in cases where noisy speech is processed by a time-frequency weighting. To this end, an extensive evaluation is presented of objective measure for intelligibility prediction of noisy speech processed...

In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve th...

Existing objective speech-intelligibility measures are suitable for several types of degradation, however, it turns out that they are less appropriate for methods where noisy speech is processed by a time-frequency (TF) weighting, e.g., noise reduction and speech separation. In this paper, we present an objective intelligibility measure, which show...

In general, single-channel noise-reduction algo-rithms do not improve the speech intelligibility for normal-hearing listeners. In order to understand this problem, a reliable objective intelligibility measure is of great interest. Such a measure could be used for analysis and/or optimization of noise-reduction algorithms. For this application it is...

In this research various objective quality measures are evaluated in order to predict the intelligibility for a wide range of non-linearly processed speech signals and speech degraded by additive noise. The obtained results are compared with the prediction results of a more advanced perceptual-based model proposed by Dau et al. and an objective int...

The use of psychoacoustical masking models for audio coding applications has been wide spread over the past decades. In such applications, it is typically assumed that the original input signal serves as a masker for the distortions that are introduced by the lossy coding method that is used. Up to now, these masking models are mostly based on spec...