Cees Taal

Cees Taal
SKF · research and technology department

PhD

About

29
Publications
12,731
Reads
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3,061
Citations
Introduction
I currently work at SKF as a researcher in the field of bearing and machine monitoring., e.g., make a prognosis of the remaining life of a mechanical system in operation and/or detect system anomalies by using machine learning and signal processing techniques.
Additional affiliations
April 2015 - present
Quby
Position
  • R&D, algorithms, signal processing
Description
  • DSP algorithm development and data processing/analysis for smart-home applications, e.g., thermostats (Toon) and power monitoring.
December 2013 - March 2015
Philips
Position
  • Researcher
October 2012 - November 2013
Leiden University Medical Centre
Position
  • PostDoc Position

Publications

Publications (29)
Preprint
Full-text available
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)...
Article
Full-text available
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...
Article
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...
Preprint
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Data
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.
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
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...
Article
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...
Article
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...