Analysis/synthesis filter bank design based on time domain ..
Analysis/Synthesis Filter Bank Design Based on Time …
Analysis/synthesis filter bank design based
The purpose of this course is to familiarize students with the different types of trading strategies employed by various money management institutions. These financial trading strategies are used to manage the risk and return profiles of specific portfolios. Throughout the sessions, students will be challenged to understand and explore the application and implementation of these different strategies. Trading simulations employed on the Rotman Interactive Trader and Rotman Portfolio Manager (using real market data and computer generated data) will be used extensively in this course as a way to learn and test different strategies. All classes will be held in the new Real-time Analytics and Investment Lab (RAIL), located on the third floor of the Bass Building (B312). Students are expected to attend all sessions. Grades are based on in-class simulation results, class participation, and two written assignments.
In this paper a new algorithm to compute an additive synthesis model of a signal is presented. An analysis based on the Complex Continuous Wavelet Transform has been used to extract the time-varying amplitudes and phases of every component of the additive model. The mathematical relationships between the CCWT, the Hilbert Transform and complex filter banks are presented in order to obtain useful filter bank design parameters. The mathematical analysis of five different signals is presented: a pure cosine, a sum of cosines, a signal with frequency variations and two finite duration signals with Gaussian and exponential envelopes. The obtained theoretical results are finally compared with those computed with the developed algorithm.
Allpass-Based Analysis-Synthesis Filter-Banks: Design and ..
This course is designed to develop students' ability to interpret and use financial accounting information in an equity valuation context. The perspective taken is that of an outsider relying on publicly available financial information for investment purposes. The course relies heavily upon financial statement analysis tools and the residual income framework for equity valuation. Through lectures, in-depth case studies, and real-time exercises, the first half of the course covers traditional financial statement analysis-based tools for critically analyzing and assessing a firm's current financial performance and economic condition, including ratio analysis, accounting quality analysis and financial distress / bankruptcy prediction models. The second half of the course introduces the accounting-based valuation framework and develops the link between financial statement analysis, forecasting and equity valuation. The capstone to the course is the completion of a comprehensive, real-time valuation of a publicly traded firm (or registered IPO candidate). The course is structured for students to gain a deeper understanding of the economic pressures behind the valuation creation and valuation process, and will be useful to those students who anticipate making investment or credit decisions at least partially based on historical and prospective financial statement information.
Structures are presented for the perfect-reconstruction quadrature mirror filter bank that are based on lossless building blocks. These structures ensure that the frequency responses of the analysis (and synthesis) filters have pairwise symmetry with respect to π/2 and require fewer parameters than recently reported structures (also based on lossless building blocks). The design time for the proposed structures is correspondingly much less than for the earlier methods, which did not incorporate such symmetry.
A complete filter bank consists of the analysis and synthesis side
This is an advanced elective course on the economics of active investing in public equity markets. We will cover a set of foundational skills needed to select, and manage, a portfolio of public stocks. nSpecifically, the course material is designed to improve student skills in: (1) assessing the relative attractiveness of individual companies, (2) building stock screens to filter and rank firms based on user-specified parameters, (3) buying and shorting individual equity positions, and (4) monitoring and managing portfolio risk. nThis is a hands-on course with an emphasis on experiential learning. Students will make extensive use of the analytical tools. Some of the classes will be held in the "Real-time Analytics and Investment Lab" (R.A.I.L.) facility in the Bass Center. There is no final exam. However, there will be a number of individual cases and a final group project. 25% of the grade will be based on class participation, and 75% will be based on cases and projects. nBecause it is an advanced elective, students taking this class are expected to be well versed in core economic, accounting, and finance skills. Material covered in a second Financial Modeling course, as well as in Accounting 312 (Evaluating Financial Statement Information) and Accounting 313 (Accounting-based Valuation) will come in handy. However, none of these courses are required.
It is well known that the analysis and synthesis filters of orthonormal DFT filter banks can not have good frequency selectivity. The reason for this is that each of the analysis and synthesis filters have only one passband. Such frequency stacking (or configuration) in general does not allow alias cancellation when the individual filters have good stopband attenuation. A frequency stacking of this nature is called nonpermissible and should be avoided if good filters are desired. In a usual M-channel filter bank with real-coefficient filters, the analysis and synthesis filters have two passbands. It can be shown that the configuration is permissible in this case. Many designs proposed in the past demonstrate that filter banks with such configurations can have perfect reconstruction and be good filters at the same time. We develop the two-parallelogram filter banks, which is the class of 2-D filter banks in which the supports of the analysis and synthesis filters consist of two parallelograms. The two-parallelogram filter banks are analyzed from a pictorial viewpoint by exploiting the concept of permissibility. Based on this analysis, we construct and design a special type of two-parallelogram filter banks, namely, cosine-modulated filter banks (CMFB). In two-parallelogram CMFB, the analysis and synthesis filters are cosine-modulated versions of a prototype that has a parallelogram support. Necessary and sufficient conditions for perfect reconstruction of two-parallelogram CMFB are derived.
The analysis filter bank divides ..
30/11/2017 · Analysis/Synthesis f..
Design and simulation of a speech signals speech analysis-synthesis system based on short-time Fourier analysis
Part 1 covers the principles of filter bank design
Filter bank - Wikipedia
Filter Bank Overview Filter banks, ..
Filter banks as time ..
Time-domain filter bank analysis: A new design theory
This thesis deals with the signal analysis, auditory perception, and physics-based synthesis of the piano sound. Contributions of this thesis can be grouped into four main categories: Analysis and modeling of the sustain pedal effect, analysis of harmonic and inharmonic musical tones by means of an inverse comb filter, loss filter design for waveguide piano synthesis, and perception of longitudinal vibrations in piano tones. The sustain pedal effect is studied through signal analysis of recorded tones, and the results show that the use of the sustain pedal increases the decay times of middle range tones, increases beating, and makes the sounds more reverberant. Based on the results, an algorithm is designed for simulating the sustain pedal effect. Objective and subjective studies show that the algorithm is capable of producing the main effects of the sustain pedal. The signal analysis of tones played with a partial sustain pedal reveals that the tone decay can be divided into three distinct time intervals, namely the initial decay, the damper-string interaction, and the final free vibration. Additionally, the nonlinear amplitude limitation during the damper-string interaction can excite missing modes in the lowest piano tones. Decomposition of harmonic and inharmonic musical instrument tones to tonal and noise components and selecting single partials with an inverse comb filter structure is discussed. The filters are designed based on the fundamental frequency and the inharmonicity coefficient, and they are found to provide a simple and efficient analysis tool for musical signals. A multi-stage ripple filter structure for modeling the complicated decay process of the piano tones is presented. The filter is capable of accurately matching a desired number of partial decay times or, alternatively, modeling the overall decay characteristics of a piano tone. Finally, the threshold of audibility is sought for perception of longitudinal components in fortissimo piano tones through formal listening tests. The results suggest that the longitudinal components are audible up to note C (fundamental frequency 523 Hz), but based on the listeners' opinions modeling the longitudinal components in a piano synthesizer up to note A (fundamental frequency 220 Hz) only is sufficient.
Cosine-Modulated Filter Banks | SpringerLink
The left-hand side of Figure 6-1 depicted a single stage of a wavelet decomposition, yielding the wavelet coefficients c, and d, at a single scale. We can cascade this filter in a few different fashions to carry the decomposition one level further, with one quite common method known as the pyramid decomposition structure. The pyramid structure is shown in Figure 6-2, where the approximation wavelet coefficients C are fed into the same two-channel filter bank, which in turn emits another set of approximation and detail coefficients. Thus as the cascade proceeds in a dyadic (power-of-two) fashion, the DWT effectively tiles the time-frequency space. This tiling is known as a multi-resolution analysis (MRA), and really gets to the heart of the primary advantage of the wavelet transform – it solves the localization problem of the Fourier and other classical transforms. The MRA decomposes the signal into nested subspaces, thereby giving us the ability to know not only if a particular event or characteristic occurred, but when. Using this formulation of the wavelet decomposition, the output of the analysis cascade is arranged as shown in Figure 6-3, using the example of a three-level wavelet decomposition. The synthesis filter bank reconstitutes the input signal using a similar structure to that of Figure 6-2, but with different filters and the downsampling operation replaced with upsampling of the DWT coefficients (see Figure 6-4).
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