FPGA Implementation of an Adaptive Noise Canceller for Robust Speech Enhancement Interfaces

This paper describes the design and implementation results of an adaptive Noise Canceller useful for the construction of Robust Speech Enhancement Interfaces. The algorithm being used has very good performance for real time applications. Its main disadvantage is the requirement of calculating several operations of division, having a high computational cost. Besides that, the accuracy of the algorithm is critical in fixed-point representation due to the wide range of the upper and lower bounds of the variables implied in the algorithm. To solve this problem, the accuracy is studied and according to the results obtained a specific word-length has been adopted for each variable. The algorithm has been implemented for Altera and Xilinx FPGAs using high level synthesis tools. The results for a fixed format of 40 bits for all the variables and for a specific word-length for each variable are analyzed and discussed

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

Un cosechero riojano : drama en un acto y en verso

Estrenado en el teatro Salon Eslava la noche del 1º de diciembre de 187

Influence of Seaweeds on the Quality of Pasta as a Plant-Based Innovative Food

This study evaluated the effect of the incorporation of seaweed on the physicochemical and technological quality of pasta. For this purpose, enriched wheat pastas from different seaweeds (sea lettuce—Ulva lactuca, nori—Porphyra tenera, and wakame—Undaria pinnatifida) were made and compared with durum wheat pasta as a control treatment. Firstly, optimal cooking times were established by visual and instrumental methods. Then, the technological properties of weight gain (WG), swelling index (SI), cooking losses (CL), and moisture (H%) were determined. Protein and fiber analyses, texture profile analysis (TPA), and color measurements were also performed to evaluate the physicochemical properties. Overall, enriched pasta with seaweed revealed slightly shorter optimal cooking times than control pasta. Texture properties were also modified, with a lower value of hardness, and higher values of adhesiveness and resilience. However, due to the low percentages of seaweed (3%), noticeable effects were not appreciated. Moreover, color variations of enriched pasta were relevant due to the difference among seaweeds. Nonetheless, these additions increased the protein content and soluble fiber in these foods. In conclusion, pasta enriched with marine ingredients improved this nutritional profile, and the changes in technological properties did not have a major impact on the product quality

Low-fat fresh sausage from rabbit meat: An alternative to traditional rabbit consumption

The study aimed at the development of fresh sausages using rabbit exclusively as raw material. The idea was to offer an innovative product to increase rabbit consumption. Also, to meet currently consumers' requirements, a low-fat version was made. Two final formulations, a control sausage and a low-fat version using konjac gum, were developed through an iterative process and stored in a MAP under refrigeration. Sensory, microbiological and physicochemical analyses were carried out on days 1, 6, 8 and 13 after packaging. The shelf-life of the sausages was determined according to a multivariate criterion. Results showed a significant reduction in fat content and energy value. Sensory analysis showed a decrease in characteristic aroma and flavour and an increase in rancid odour, while hardness and fragility decreased in the low-fat treatment. The shelf-life was 7 days for all treatments, concluding that the multivariate method was a powerful technique as physicochemical, microbiological and sensory criteria were considered

Glottal-Source Spectral Biometry for Voice Characterization

The biometric signature derived from the estimation of the power spectral density singularities of a speaker’s glottal source is described in the present work. This consists in the collection of peak-trough profiles found in the spectral density, as related to the biomechanics of the vocal folds. Samples of parameter estimations from a set of 100 normophonic (pathology-free) speakers are produced. Mapping the set of speaker’s samples to a manifold defined by Principal Component Analysis and clustering them by k-means in terms of the most relevant principal components shows the separation of speakers by gender. This means that the proposed signature conveys relevant speaker’s metainformation, which may be useful in security and forensic applications for which contextual side information is considered relevant

A Hybrid Parameterization Technique for Speaker Identification

Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. The separation methodology is based in Joint Process Estimation under the un-correlation hypothesis between vocal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency domains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speakers are shown comparing different parameterization strategies. The results confirm the better performance of decoupled parameterization compared against approaches based on plain speech parameterization