Speech dereverberation and speaker separation using microphone arrays in realistic environments

This thesis concentrates on comparing novel and existing dereverberation and speaker separation techniques using multiple corpora, including a new corpus collected using a microphone array. Many corpora currently used for these techniques are recorded using head-mounted microphones in anechoic chambers. This novel corpus contains recordings with noise and reverberation made in office and workshop environments. Novel algorithms present a different way of approximating the reverberation, producing results that are competitive with existing algorithms. Dereverberation is evaluated using seven correlation-based algorithms and applied to two different corpora. Three of these are novel algorithms (Hs NTF, Cauchy WPE and Cauchy MIMO WPE). Both non-learning and learning algorithms are tested, with the learning algorithms performing better. For single and multi-channel speaker separation, unsupervised non-negative matrix factorization (NMF) algorithms are compared using three cost functions combined with sparsity, convolution and direction of arrival. The results show that the choice of cost function is important for improving the separation result. Furthermore, six different supervised deep learning algorithms are applied to single channel speaker separation. Historic information improves the result. When comparing NMF to deep learning, NMF is able to converge faster to a solution and provides a better result for the corpora used in this thesis

Special oils for halal and safe cosmetics

Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products

Acetylcholine esterase as a possible marker for the detection of halal way of slaughtering

Introduction: Different methods of slaughtering are being practiced because of differences in religious guidelines and environmental issues (use of electricity) or convenience of handling etc. Variation in methods of slaughtering results in different conditions namely, release of varying amount of blood and different degree of movement of its body parts prior to death. These issues are related to the release of neurotransmitter (NT) at the neuro-muscular junction (NMJ) eventually is subject to be released from the body through the blood flow. Experimental design: Muscle samples from chicken in small pieces were collected immediately after slaughtering. Slaughtering was carried out using sharp knife. Two different conditions pertaining to the Islamic guidelines of slaughtering were investigated. such as whether the neck was severed (S+) or not (S-) from the body during slaughtering and whether the animal just after slaughtering was released (R+) or not (R-). The level of acetylecholine esterase mRNA involved in the degradation of acetylecholine, a NT at NMJ was investigated by RT-PCR. Results: The level of acetylecholine esterase mRNA was not detected in the sample obtained from the chicken slaughtered following Islamic guidelines i.e., neck should not be severed and body should be released just after the slaughtering (R+S-). Conclusions: Level of acetylcholine or acetylcholine esterase can be used as a biomarker to identify if the slaughtering is performed following Islamic guidelines

Calophyllum canum : antibacterial and anticancer plant

Human have used plants as a source of medicine throughout the world since time immemorial. Today there are at least 120 distinct chemical substances derived from plants that are considered as important drugs currently in use in one or more countries in the world. In particular, 60% drugs currently in clinical use for treatment of cancer were found to be of natural origin. Calophyllum canum is a large tree which grows in South East Asia and which is popular for its timber. This plant belongs to the family Guttiferae; a family that boasts species which are rich in bioactive phytochemicals. Some species are believed to having medicinal values and are used against several diseases including anti-inflammatory, anti infectious, astringent and antipyretic. We have successfully isolated two compounds from the methanol extract of Calophyllum canum stembarks that active inhibit the growth of Staphylococcus aureus (ATCC 29213 and ATCC 25923). The cytotoxic study on the extracts revealed that the n-hexane extract had the strongest antiproliferation activity, followed by the methanol extract. n-hexane strongly inhibited the growth of TE1 and MCF7 cell lines. IC50 for n-hexane and methanol extract activity on the A549 cell line was found to be 27.96 μg/mL and 78.9 μg/mL respectively.The compounds (CE0 - CE5) isolated from ethyl acetate extract of C. canum are active to inhibit cell proliferation of human cervix adenocarcinoma cells

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity