106 research outputs found
Representation of Spectral Profiles in the Auditory System Part II: A Ripple Analysis Model
No full textBased on experimental results presented in [Vranic-Sowers and Shamma, 1993], and on further physiological and psychoacoustical evidence, it is argued that the auditory system analyzes a spectral profile along two largely independent dimensions. They correspond to the magnitude and phase of a localized Fourier transformation of the profile, closely analogues to the spatial frequency transformations described in the visual system. Within this general framework, a model of profile analysis is proposed in which a spectral profile is assumed to be represented by a weighted sum of sinusoidally modulated spectra (ripples). The analysis is performed by a bank of bandpass filters, each tuned to a particular ripple frequency and ripple phase. The parameters of the model are estimated using data from ripple detection experiments in [Green, 1986; Hillier, 1991]. Perceptual thresholds are then computed from the filter outputs and compared with thresholds measured for peak profile experiments, and for detection tasks with step, single component increment, and the alternating profiles
Daddy A Deconstructive View of Sylvia Plath's Poetry - Lighthouse Academy Syria - Hussam Shamma
No full textMany critics believe that Sylvia Plath"s productions in general and her poetry in particular are mere reflections of her personal and private life, which by she finds a comfortable medium to transmit her agonies and despair. Therefore, in order to deepen our understanding of her poetry, some critics assert, we need to equip ourselves with some biographical sketches of her life, for biographical criticism assumes a strong bond between the artist and her/his literary offspring. “Every author”, says J.W. Von Goethe, “in some way portrays himself in his works” (Gillespie 20); each single poem of her/his creation mirrors a certain incident or personal affair. Thus it would be easier for us to reach conclusions if we understood the writer"s world before delving into her/his works. But if we really confine ourselves to the authors' lives and experiences, the text"s door of explanations and interpretations will be closed, narrowing itself into a closed window of one conclusion opened only by the author.
For more information, please visit our website:
www.lighthouseacademy.orgThe question here is do we really need to wear our investigation glasses and begin to dig deep down in the artist's diaries and personal clothes to gain working knowledge and evidences that would help us to condemn a work of art? Or no longer the text, who always exists in the present time, is the obedient and submissive child who obeys his father's rules and law, instead it becomes the bad boy who leaves home since the day he is born to seek his path alone without the handcuffs of his deceased past father. Issac Bashevis Singer states that "If people are really hungry, they do not care about the biography of the baker" (Gillespie 20). This chapter is starving and because of its unquenchable hunger, it is not able to differentiate between the cake and its baker, thus both plates might make the text's mouth water
Auditory representations of speech sounds in a neural model: The role of peripheral processing
No full textNeural Networks That Recognize Phonemes by Their Acoustic Features
No full textThe ability of the ear model and lateral inhibitory networks (LIN) to preserve and enhance acoustic features of speech signal is examined by training neural networks to recognize phonemes by their LIN outputs. Using the back propagation learning algorithm, networks that are specialized to recognize specific classes of phonemes are trained and tested. Experiments are conducted both in single and multi-speaker cases. By using single layer networks, we can show that the phonemes are identified by their acoustic features that have been known to linguists and phoneticians. The networks generally yield satisfying results when tested in experiments for a single speaker, where we focus on the performance against phoneme variation induced by the context, and in multi-speaker experiments where errors in recognition are due to speaker variation. These results convince us that the acoustic features picked by the networks are reliable cues for phoneme recognitio
Modeling Perception of Spectral Profile Changes
No full textIn this thesis, we explore how the human auditory system represents and detects changes in a spectral profile. First, using profile analysis methods, we measure listeners' sensitivities to changes in spectral peak shapes and ripple phases. More specifically, we measure thresholds to changes in peak symmetry and bandwidth (which respectively are measures of the local evenness or oddness of a peak and of the tuning or sharpness of a peak). The effects of several other manipulations are also studied. It is found that the thresholds are constant for almost all initial peak shapes. Second, these changes in symmetry and bandwidth are interpreted as changes in the phase and magnitude of the profile's Fourier transform. In this light, the last set of experiments measured the sensitivity to (ripple) phase changes in spectral sinusoids. We find that the thresholds obtained are similar to the above-mentioned symmetry thresholds.A fundamental conclusion arising from this analysis is that spectral peaks are represented along two largely independent axes: the magnitude and phase of their Fourier transforms. More specifically, it is argued that, along these two dimensions, the auditory system analyzes an arbitrary spectral pattern in a localized Fourier transform domain. This is closely analogous to spatial frequency transformations in the visual system. Within this general framework, we propose a model of profile analysis in which a spectral profile is represented by a weighted sum of sinusoidally modulated spectra (ripples). The first part of the analysis is performed by a bank of bandpass filters, each tuned to a particular ripple frequency and ripple phase. The parameters of the model are estimated using data from several ripple discrimination experiments. The second part of the model is a detection stage which operates on the magnitude and phase of the computed transform, and varies with the type of perceptual task. The results of the detection operations are compared to experimental data from various profile analysis tasks. The model accounts well for the perceptual results in these tests. We propose two types of psychoacoustical experiments involving any arbitrary spectral patter, which should further verify the predictions of the model
Discrete Representation of Signals from Infinite Dimensional Hilbert Spaces with Applications to Noise Suppression and Compression
No full textAddressed in this thesis is the issue of representing signals from infinite dimensional Hilbert spaces in a discrete form. The discrete representations which are studied come from the irregular samples of a signal dependent transform called the group representation transform, e.g., the wavelet and Gabor transform. The main issues dealt with are (i) the recoverability of a signal from its discrete representation, (ii) the suppression of noise in a corrupted signal, and (iii) compression through efficient discrete representation.The starting point of the analysis lies with the intimate connection between the Duffin-Schaeffer theory of (global) frames and irregular sampling theory. This connection has lead elsewhere to the formulation of iterative schemes for the reconstruction of a signal from its irregular samples. However, these schemes have not addressed such issues as digital implementability and reconstruction from perturbed representations. Here, iterative reconstruction algorithms are developed and implemented which recover a signal from its possibly perturbed discrete representation.Robustness to perturbations occurring directly in the signal domain are also investigated. Based on the notion of coherence with respect to a frame, a simple non-linear thresholding scheme is developed for the rejection of noise.The structure of the discretization has many free parameters including the choice of group representation transform, the analyzing function associated with the group representation transform, and the sampling set. Each choice of parameters leads to a different discrete representation and the specification of an underlying set of primitive functions. Reconstructability is directly related to the frame properties of this set of primitive functions.Localized discrete representations around a particular signal are also investigated. Truncations and other signal dependent localization of global representations lead to finite representations. The approach to finite representations which is taken here can be stated in terms of local frames for the reproducing kernel Hilbert Space formed by the range of the group representation transform.Finally, numerical examples of discrete representations which are signal independent and new signal dependent discrete (positive extreme) wavelet representations are presented. Reconstruction, noise suppression, and compression experiments are conducted and demonstrated on numerical examples including speech and synthetic signals
Neural Networks for Speech Processing and Recognition.
No full textIn this report, we shall outline a specific approach to the analysis and recognition of speech phonemes based on the fundamental principles of processing in the auditory nervous system. The system consists of particular implementations of the three conceptual stages mentioned above: The cochlear transformations of speech sounds into spatiotemporal patterns (stage 1); the subsequent feature extraction by the central neural networks (stage 2); and the use of various adaptive nets to identify the acoustic features of speech phonemes (stage 3). We shall illustrate the utility of this approach in identifying and organizing the invariant features of nine American English vowels
The Auditory Processing of Speech.
No full textThe processing of speech in the mammalian auditory periphery is discussed in terms of the spatio-temporal nature of the distribution of the cochlear response and the novel encoding schemes this permits. Algorithms to detect specific morphological features of the response and the novel encoding schemes of the response patterns are also considered for the extraction of stimulus spectral parameters
Identification of Connectiviity in Neural Networks.
No full textAnalytical and experimental methods are provided for estimating synaptic connectivities from simultaneous recordings of multiple neurons. The results are based on detailed, yet flexible neuron models in which spike trains are modeled as general doubly stochastic point processes. The expressions derived can be used with non-stationary or stationary records, and can be readily extended from pair-wise to multi-neuron estimates. Furthermore, we show analytically how the estimates are improved as more neurons are sampled, and derive the appropriate normalizations to eliminate stimulus-related correlations. Finally, we illustrate the use and interpretation of the analytical expressions on simulated spike trains and neural networks, and give explicit confidence measures on the estimates
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