Otoacoustic emission (OAE) data recorded for tone bursts presented separately and as a two-tone burst complex, that had been reported previously [Yoshikawa, H., Smurzynski, J., Probst R., 2000. Suppression of tone burst evoked otoacoustic emissions in relation to frequency separation. Hear. Res. 148, 95–106], were re-processed using the method of adaptive approximations by matching pursuit (MP). Two types of stimuli were applied to record tone burst OAEs (TBOAEs): (a) cosine-windowed tone bursts of 5-ms duration with center frequencies of 1, 1.5, 2 and 3kHz, (b) complex stimuli consisting of a digital addition of the 1-kHz tone burst together with either the 1.5-, 2- or 3-kHz tone burst. The MP method allowed decomposition of signals into waveforms of defined frequency, latency, time span, and amplitude. This approach provided a high time–frequency (t–f) resolution and identified patterns of resonance modes that were characteristic for TBOAEs recorded in each individual ear. Individual responses to single-tone bursts were processed off-line to form ‘sum of singles’ responses. The results confirmed linear superposition behavior for a frequency separation of two-tone bursts of 2kHz (the 1-kHz and 3-kHz condition). For the 1, 1.5-kHz condition, the MP results revealed the existence of closely positioned resonance modes associated with responses recorded individually with the stimuli differing in frequency by 500Hz. Then, the differences between t–f distributions calculated for dual (two-tone bursts) and sum-of-singles conditions exhibited mutual suppression of resonance modes common to both stimuli. The degree of attenuation depended on the individual pattern of characteristic resonance modes, i.e., suppression occurred when two resonant modes excited by both stimuli overlapped. It was postulated that the suppression observed in case of dual stimuli with closely-spaced components is due to mutual attenuation of the overlapping resonance modes.

'It is not a lack of spectrum. It is an issue of efficient use of the available spectrum"--conclusions of the FCC Spectrum Policy Task Force. There is growing interest towards providing broadband communication with high bit rates and throughput, especially in the ISM band, as it was an ignition of innovation triggered by the FCC to provide, to some extent, a regulation-free band that anyone can use. But with such freedom comes the risk of interference and more responsibility to avoid causing it. Therefore, the need for accurate interference detection and identification, along with good blind detection capabilities are inevitable. Since cognitive radio is being adopted widely as more researchers consider it the ultimate solution for efficient spectrum sharing [1], it is reasonable to study the cognitive radio in the ISM band [2]. Many indications show that the ISM band will have less regulation in the future, and some even predict that the ISM may be completely regulation free [3]. In the dawn of cognitive radio, more knowledge about possible interfering signals should play a major role in determining optimal transmitter configurations. Since signal identification and interference will be the core concerns [4], [5], we will describe a novel approach for a cognitive radio spectrum sensing engine, which will be essential to design more efficient ISM band transceivers. In this thesis we propose a novel spectrum awareness engine to be integrated in the cognitive radios. Furthermore, the proposed engine is specialized for the ISM band, assuming that it can be one of the most challenging bands due to its free-to-use approach. It is shown that characterization of the interfering signals will help with overcoming their effects. This knowledge is invaluable to help choose the best configuration for the transceivers and will help to support the efforts of the coexistence attempts between wireless devices in such bands.

<p>Laser vibration sensing can be used to classify military targets by its unique vibration signature. A coherent laser radar receives the target´s rapidly oscillating surface vibrations and by using proper demodulation and Doppler technique, stationary, radially moving and even accelerating targets can be taken care of. </p><p>A frequency demodulation method developed at the former FOA, is for the first time validated against real data with turbulence, scattering, rain etc. The issue is to find a robust and reliable system for target recognition and its performance is therefore compared with some frequency distribution methods. The time frequency distributions have got a crucial drawback, they are affected by interference between the frequency and amplitude modulated multicomponent signals. The system requirements are believed to be fulfilled by combining the FOA method with the new statistical method proposed here, the combination being suggested as aimpoint for future investigations.</p>

Laser vibration sensing can be used to classify military targets by its unique vibration signature. A coherent laser radar receives the target´s rapidly oscillating surface vibrations and by using proper demodulation and Doppler technique, stationary, radially moving and even accelerating targets can be taken care of. A frequency demodulation method developed at the former FOA, is for the first time validated against real data with turbulence, scattering, rain etc. The issue is to find a robust and reliable system for target recognition and its performance is therefore compared with some frequency distribution methods. The time frequency distributions have got a crucial drawback, they are affected by interference between the frequency and amplitude modulated multicomponent signals. The system requirements are believed to be fulfilled by combining the FOA method with the new statistical method proposed here, the combination being suggested as aimpoint for future investigations.

Music recordings contain the mixed contribution of multiple overlapping instruments. In order to better understand the music, it would be beneficial to understand each instrument independently. This thesis focuses on separating the individual instrument recordings within a song. In particular, we propose novel algorithms for separating instrument recordings given only their mixture. When the number of source signals does not exceed the number of mixture signals, we focus on a subclass of source separation algorithms based on joint diagonalization. Each approach leverages a different form of source structure. We introduce repetitive structure as an alternative that leverages unique repetition patterns in music and compare its performance against the other techniques. When the number of source signals exceeds the number of mixtures (i.e. the underdetermined problem), we focus on spectrogram factorization techniques for source separation. We extend single-channel techniques to utilize the additional spatial information in multichannel recordings, and use phase information to improve the estimation of the underlying components.

In this thesis work, time-frequency filtering of nonstationary signals in noise using Wigner-Ville Distribution is investigated. Continuous-time, discrete-time and discrete Wigner Ville Distribution definitions, their relations, and properties are given. Time-Frequency Peak Filtering Method is presented. The effects of different parameters on the performance of the method are investigated, and the results are presented. Time-Varying Wiener Filter is presented. Using simulations it is shown that the performance of the filter is good at SNR levels down to -5 dB. It is proposed and shown that the performance of the filter improves by using Support Vector Machines. The presented time-frequency filtering techniques are applied on test signals and on a real world signal. The results obtained by the two methods and also by classical zero-phase low-pass filtering are compared. It is observed that for low sampling rates Time-Varying Wiener Filter, and for high sampling rates Time-Frequency Peak Filter performs better.

It is commonly accepted that the induced noise on DFT phase spectrum has a negligible effect on speech intelligibility for short durations of analysis windows, as the early intelligibility studies pointed out. This fact is confirmed by recent intelligibility studies as well. Based on this phenomenon, classical speech enhancement algorithms do not modify DFT phase spectrum and only make changes in the DFT magnitude spectrum. However, in recent studies it is also indicated that these classical speech enhancement algorithms are not capable of improving the intelligibility scores of noise degraded speech signals. In other words, the contained information in a noise degraded signal cannot be increased by classical enhancement methods. Instead the ease of listening, i.e. quality, can be improved. Hence additional effort can be made to increase the amount of quality improvement using both DFT magnitude and DFT phase. Therefore if the performances of the classical methods are to be improved in terms of speech quality, the effect of DFT phase on speech quality needs to be studied. In this work, the contribution of DFT phase on speech quality is investigated through some simulations using an objective quality assessment criterion. It is concluded from these simulations that, the phase spectrum has a significant effect on speech quality for short durations of analysis windows. Furthermore, phase values of low frequency components are found to have the largest contribution to this quality improvement. Under the motivation of these results, a new enhancement method is proposed which modifies the phase of certain low frequency components as well as the magnitude spectrum. The proposed algorithm is implemented in MATLAB environment. The results indicate that the proposed system improves the performance of the classical methods in terms of speech quality.

No description available.

This thesis applies an electromagnetic feature extraction technique to design electromagnetic target classifiers for conductors, dielectrics and dielectric coated conductors using their natural resonance related late-time scattered responses. Classifier databases contain scattered data at only a few aspects for each candidate target. The targets are dielectric spheres of varying sizes and refractive indices, perfectly conducting spheres varying sizes and dielectric coated conducting spheres of varying refractive indices and thickness in coating. The applied classifier design technique is suitable for real-time target classification because of the computational efficiency of feature extraction and decision making approaches. The Wigner-Ville Distribution (WD) is employed in this study in addition to the Principal Components Analysis (PCA) technique to extract target features mainly from late-time target responses. WD is applied to the back-scattered responses at different aspects. To decrease aspect dependency, feature vectors are extracted from selected late-time portions of the WD outputs that include natural resonance related information. Principal components analysis is also used to fuse the feature vectors and/or late-time target responses extracted from reference aspects of a given target into a single characteristic feature vector for each target to further reduce aspect dependency.

Made available in DSpace on 2014-07-29T15:08:19Z (GMT). No. of bitstreams: 1 Reconhecimento automatico tinamidae.pdf: 1584398 bytes, checksum: 2f87f01e251266076588ff37c7d6322f (MD5) Previous issue date: 2012-04-11 / This work presents a comprehensive approach to develop a system for recognizing birds by vocalization. The approach specifically addresses the recognition of birds of the Tinamidae family proposing the analysis of data related to the frequency and song of the bird and also classifying and determining the species of bird. The study differs from related research primarily for performing the pre-processing stage automatically. This stage determines the following characteristics: the minimum, the maximum and the stronger frequencies. It s still made a segmentation of the bird singing in periods of sound and silence. The time of singing is also used as a characteristic peculiar to each species analyzed. For the automatic determination of the characteristics of the frequency and song of the bird, an analysis of the power spectral density was made for each time period specified in the frequency using the spectrogram of the song. The recognition and classification technique adopted was the nearest neighbor, using Euclidean distance normalized by the standard deviation. The accuracy of the technique used was 94.12%. / O presente trabalho apresenta uma abordagem completa para o desenvolvimento de um sistema de reconhecimento de aves através da vocalização. A abordagem trata especificamente do reconhecimento de aves da família Tinamidae, propondo analisar dados relacionados à frequência e canto da ave, classificando e determinando a espécie da ave. O estudo feito diferencia-se das pesquisas correlatas principalmente por realizar a etapa de pré-processamento de maneira automática, determinando nesta fase as características de frequência mínima, máxima e mais intensa. Ainda é feita uma segmentação do canto da ave, dividindo o mesmo em períodos de som e silêncio, sendo o tempo de canto também usado como característica peculiar de cada espécie analisada. Para a determinação das características de frequência e canto da ave de maneira automática foi feita uma análise da densidade espectral de potência para cada período de tempo especificado em relação à frequência, utilizando para isso o espectrograma do canto. A técnica de classificação e reconhecimento adotada foi a do vizinho mais próximo, aplicando a distância Euclidiana normalizada pelo desvio padrão. A acurácia da técnica utilizada foi de 94,12%.