27 research outputs found
NOVEL OFDM SYSTEM BASED ON DUAL-TREE COMPLEX WAVELET TRANSFORM
The demand for higher and higher capacity in wireless networks, such as cellular,
mobile and local area network etc, is driving the development of new signaling
techniques with improved spectral and power efficiencies. At all stages of a
transceiver, from the bandwidth efficiency of the modulation schemes through highly
nonlinear power amplifier of the transmitters to the channel sharing between different
users, the problems relating to power usage and spectrum are aplenty. In the coming
future, orthogonal frequency division multiplexing (OFDM) technology promises to
be a ready solution to achieving the high data capacity and better spectral efficiency in
wireless communication systems by virtue of its well-known and desirable
characteristics.
Towards these ends, this dissertation investigates a novel OFDM system based on
dual-tree complex wavelet transform (D
Perceptual models in speech quality assessment and coding
The ever-increasing demand for good communications/toll
quality speech has created a renewed interest into the
perceptual impact of rate compression. Two general areas are
investigated in this work, namely speech quality assessment
and speech coding.
In the field of speech quality assessment, a model is
developed which simulates the processing stages of the
peripheral auditory system. At the output of the model a
"running" auditory spectrum is obtained. This represents
the auditory (spectral) equivalent of any acoustic sound such
as speech. Auditory spectra from coded speech segments serve
as inputs to a second model. This model simulates the
information centre in the brain which performs the speech
quality assessment. [Continues.
Computing Intelligence Technique and Multiresolution Data Processing for Condition Monitoring
Condition monitoring (CM) of rotary machines has gained increasing importance and extensive research in recent years. Due to the rapid growth of data volume, automated data processing is necessary in order to deal with massive data efficiently to produce timely and accurate diagnostic results. Artificial intelligence (AI) and adaptive data processing approaches can be promising solutions to the challenge of large data volume. Unfortunately, the majority of AI-based techniques in CM have been developed for only the post-processing (classification) stage, whereas the critical tasks including feature extraction and selection are still manually processed, which often require considerable time and efforts but also yield a performance depending on prior knowledge and diagnostic expertise.
To achieve an automatic data processing, the research of this PhD project provides an integrated framework with two main approaches. Firstly, it focuses on extending AI techniques in all phases, including feature extraction by applying Componential Coding Neural Network (CCNN) which has been found to have unique properties of being trained through unsupervised learning, capable of dealing with raw datasets, translation invariance and high computational efficiency. These advantages of CCNN make it particularly suitable for automated analyzing of the vibration data arisen from typical machine components such as the rolling element bearings which exhibit periodic phenomena with high non-stationary and strong noise contamination. Then, once an anomaly is detected, a further analysis technique to identify the fault is proposed using a multiresolution data analysis approach based on Double-Density Discrete Wavelet Transform (DD-DWT) which was grounded on over-sampled filter banks with smooth tight frames. This makes it nearly shift-invariant which is important for extracting non-stationary periodical peaks. Also, in order to denoise and enhance the diagnostic features, a novel level-dependant adaptive thresholding method based on harmonic to signal ratio (HSR) is developed and implemented on the selected wavelet coefficients. This method has been developed to be a semi-automated (adaptive) approach to facilitate the process of fault diagnosis. The developed framework has been evaluated using both simulated and measured datasets from typical healthy and defective tapered roller bearings which are critical parts of all rotating machines. The results have demonstrated that the CCNN is a robust technique for early fault detection, and also showed that adaptive DD-DWT is a robust technique for diagnosing the faults induced to test bearings. The developed framework has achieved multi-objectives of high detection sensitivity, reliable diagnosis and minimized computing complexity
Computer Models for Musical Instrument Identification
PhDA particular aspect in the perception of sound is concerned with what is commonly
termed as texture or timbre. From a perceptual perspective, timbre is what allows us
to distinguish sounds that have similar pitch and loudness. Indeed most people are
able to discern a piano tone from a violin tone or able to distinguish different voices
or singers.
This thesis deals with timbre modelling. Specifically, the formant theory of timbre
is the main theme throughout. This theory states that acoustic musical instrument
sounds can be characterised by their formant structures. Following this principle, the
central point of our approach is to propose a computer implementation for building
musical instrument identification and classification systems.
Although the main thrust of this thesis is to propose a coherent and unified
approach to the musical instrument identification problem, it is oriented towards the
development of algorithms that can be used in Music Information Retrieval (MIR)
frameworks. Drawing on research in speech processing, a complete supervised system
taking into account both physical and perceptual aspects of timbre is described.
The approach is composed of three distinct processing layers. Parametric models
that allow us to represent signals through mid-level physical and perceptual representations
are considered. Next, the use of the Line Spectrum Frequencies as spectral
envelope and formant descriptors is emphasised. Finally, the use of generative and
discriminative techniques for building instrument and database models is investigated.
Our system is evaluated under realistic recording conditions using databases of isolated
notes and melodic phrases
Music-listening systems
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. [235]-248).When human listeners are confronted with musical sounds, they rapidly and automatically orient themselves in the music. Even musically untrained listeners have an exceptional ability to make rapid judgments about music from very short examples, such as determining the music's style, performer, beat, complexity, and emotional impact. However, there are presently no theories of music perception that can explain this behavior, and it has proven very difficult to build computer music-analysis tools with similar capabilities. This dissertation examines the psychoacoustic origins of the early stages of music listening in humans, using both experimental and computer-modeling approaches. The results of this research enable the construction of automatic machine-listening systems that can make human-like judgments about short musical stimuli. New models are presented that explain the perception of musical tempo, the perceived segmentation of sound scenes into multiple auditory images, and the extraction of musical features from complex musical sounds. These models are implemented as signal-processing and pattern-recognition computer programs, using the principle of understanding without separation. Two experiments with human listeners study the rapid assignment of high-level judgments to musical stimuli, and it is demonstrated that many of the experimental results can be explained with a multiple-regression model on the extracted musical features. From a theoretical standpoint, the thesis shows how theories of music perception can be grounded in a principled way upon psychoacoustic models in a computational-auditory-scene-analysis framework. Further, the perceptual theory presented is more relevant to everyday listeners and situations than are previous cognitive-structuralist approaches to music perception and cognition. From a practical standpoint, the various models form a set of computer signal-processing and pattern-recognition tools that can mimic human perceptual abilities on a variety of musical tasks such as tapping along with the beat, parsing music into sections, making semantic judgments about musical examples, and estimating the similarity of two pieces of music.Eric D. Scheirer.Ph.D
The Fifth NASA Symposium on VLSI Design
The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design
NASA Tech Briefs, May 1990
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences