Abstract
Cellular Genetic Algorithms (cGAs) exhibit a natural parallelism that makes them interesting candidates for hardware implementation, as several processing elements can operate simultaneously on subpopulations shared among them. This paper presents a scalable architecture for a cGA, suitable for FPGA implementation. A regular array of custom designed processing elements (PEs) works on a population of solutions that is spread into dual-port memory blocks locally shared by adjacent PEs. A travelling salesman problem with 150 cities was used to verify the implementation of the proposed cGA on a Virtex-6 FPGA, using a population of 128 solutions with different levels of parallelism (1, 4, 16 and 64 PEs). Results have shown that an increase of the number of PEs does not degrade the quality of the convergence of the iterative process, and that the throughput increases almost linearly with the number of PEs. Comparing with a software implementation running in a PC, the cGA with 64 PEs has shown a 45x speedup.

Abstract
The main problem behind Automatic Transcription (Multiple Fundamental Frequency - F0 - Estimation) relies on its complexity. Harmonic collision and partial overlapping create a frequency lattice that is almost impossible to de-construct. Although traditional approaches to this problem of rely mainly in Digital Signal Processing (DSP) techniques, evolutionary algorithms have been applied recently to this problem and achieved competitive results. We describe all evolutionary approaches to the problem of automatic music transcription and how some were improved so they could achieve competitive results. Finally, we show how the best evolutionary approach performs on piano transcription, when compared with the state-of-the-art.

Abstract
This paper presents a new method for multiple fundamental frequency (F0) estimation on piano recordings. We propose a framework based on a genetic algorithm in order to analyze the overlapping overtones and search for the most likely F0 combination. The search process is aided by adaptive spectral envelope modeling and dynamic noise level estimation: while the noise is dynamically estimated, the spectral envelope of previously recorded piano samples (internal database) is adapted in order to best match the piano played on the input signals and aid the search process for the most likely combination of F0s. For comparison, several state-of-the-art algorithms were run across various musical pieces played by different pianos and then compared using three different metrics. The proposed algorithm ranked first place on Hybrid Decay/Sustain Score metric, which has better correlation with the human hearing perception and ranked second place on both onset-only and onset-offset metrics. A previous genetic algorithm approach is also included in the comparison to show how the proposed system brings significant improvements on both quality of the results and computing time. Index Terms-Acoustic signal analysis, automatic

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
Current state-of-The-Art speaker identification systems achieve high performances in reasonably well controlled conditions. However, some scenarios still elicit significant challenges, particularly in audio forensics when voice records are typically just a few seconds long and are severely affected by distortion, interferences, and abnormal speaking attitudes. In this paper we are inspired by the concept of minutiae in the context of fingerprinting, and try to extract localized, phase-related singularities from the speech signal denoting glottal source idiosyncratic information. First, we perform MFCC+GMM experiments in order to find the most effective phonetic segmentation of the speech signal for speaker modelling and discrimination. Secondly, we rely on effective phonetic segmentation and, in addition to MFCC features, we extract Normalized Relative Delays (NRDs) obtained from the phase of spectral harmonics. We use a Nearest Neighbour generalized classifier for speaker modelling and identification. Our results indicate that combining a careful phonetic segmentation and the inclusion of phase-related information, performance in speaker identification may increase significantly. Copyright © 2012 Audio Engineering Society, Inc.

Abstract
Techniques using RSS fingerprinting for localization have been studied over a number of different technologies in many different scenarios. In the case of underground tunnels localization can be quite challenging, yet it is extremely important for safety reasons. In the specific case of the CERN tunnels, accurate and automatized localization methods would additionally allow the workflow of some activities to become substantially faster. In a radiation area this would also have the added benefit of reducing the exposure time of personnel conducting so called radiation surveys which have to be carried out before access can be granted. In this paper Fingerprinting techniques for GSM and Wireless LAN are studied and enhanced to take advantage of both network technologies simultaneously as well as the channels RSS differential and an observed effect in the radiated power in the leaky-feeder cables. Besides the higher accuracy achieved for a single technology, this methodology looks promising for scenarios where several types of wireless networks are available or expected to be installed at a later stage.