Abstract
Human recognition of isolated vowels is quite robust considering intra and inter-speaker variability. Automatic recognition techniques typically exhibit poor performances, notably in the case of female or child speech because a higher fundamental frequency (F0) generates a sparser sampling of the magnitude spectrum. In this paper we extend previous results on a perceptually motivated concept of vowel recognition that is based on Perceptual Spectral Clusters (PSC) of harmonic partials. We study the effect of normalizing relevant PSC features by F0 taking as a reference the recognition performance of static features derived from either Linear Prediction (LP) analysis or Mel-Frequency Cepstral Coefficients (MFCC), and using the Mahalanobis distance on a data base of five natural Portuguese vowel sounds uttered by 44 speakers. Test results reveal that the recognition performance of F0-normalized PSC features increases approaching that of MFCC coefficients. These results are significant as PSC related features are amenable to concurrent vowel identification while LP or MFCC-related features are not.

Abstract
Vibrato is a frequency modulation effect of the singing voice and is very relevant in musical terms. Its most important characteristics are the vibrato frequency (in Hertz) and the vibrato extension (in semitones). In singing teaching and learning, it is very convenient to provide a visual feedback of those two objective signal characteristics, in real-time. In this paper we describe an algorithm performing vibrato detection and analysis. Since this capability depends on fundamental frequency (F0) analysis of the singing voice, we first discuss F0 estimation and compare three algorithms that are used in voice and speech analysis. Then we describe the vibrato detection and analysis algorithm and assess its performance using both synthetic and natural singing signals. Overall, results indicate that the relative estimation errors in vibrato frequency and extension are lower than 0.1%. © 2012 IEEE.

Abstract
This paper presents the Gene Fragment Competition concept that can be used with Hybrid Genetic Algorithms specially in signal and image processing. Memetic Algorithms have shown great success in real-life problems by adding local search operators to improve the quality of the already achieved "good" solutions during the evolutionary process. Nevertheless these traditional local search operators don't perform well in highly demanding evaluation processes. This stresses the need for a new semi-local non-exhaustive method. Our proposed approach sits as a tradeoff between classical Genetic Algorithms and traditional Memetic Algorithms, performing a quasi-global/quasi-local search by means of gene fragment evaluation and selection. The applicability of this hybrid Genetic Algorithm to the signal processing problem of Polyphonic Music Transcription is shown. The results obtained show the feasibility of the approach.

Abstract
Perceptual audio coders rely on the efficient reduction of perceptually irrelevant components of the audio signal as well as on the removal of statistical signal redundancies to achieve good coding gains. In order to reach high compression ratios without reducing the subjective quality of the encoded audio signal, it is necessary to identify critically interdependent functional units of the encoding algorithm and to jointly optimize their performance. A flexible and interactive simulation and analysis environment has been programmed to assist the development and optimization of a new perceptual audio coder. The main features of this environment will be explained and the most relevant aspects that were found to limit the encoding performance will be presented.

Abstract
This paper describes a method for automatically assessing the quality of manufactured roof-tiles using digital audio signal processing and pattern recognition techniques. A prototype system has been developed that is based on a mixed PC/DSP platform, where the real-time constraint is one of the main key issues. The suitability of the classification process for implementation in an industrial environment is also addressed.

Abstract
This paper presents several improvements that have been introduced to the design and operation of an adaptive 20-band room equalizer. The equalizer is implemented on a TMS320C6711 DSP platform and performs fast FIR filtering in the frequency domain. In order to reach fast adaptation to time-varying acoustic conditions, several adaptation rules operating in the frequency domain have been evaluated and the impact of a frequency-varying stepsize parameter on the convergence rate has been studied. These results will be presented along with ideas and plans for future developments.