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Anything will help! Will Rate!! Look at profile for other parts of question.

Download 'clarinet.wav' from the Resources folder in Owlspace. Create a discrete time FIR filter which extracts an estimate of the amplitude envelope of a signal and apply this filter to 'clarinet.wav'. Using this estimate of the amplitude envelope, synthesize a clarinet by using the spectral model detailed above and modulate this waveform by your estimated ADSR envelope. Plot the original clarinet signal in the time domain (before modulation), the amplitude envelope your algorithm extracts, and the final amplitude modulated waveform. Describe how you designed the filter to extract amplitude envelopes. Now, read in a short segment of an external wavfile (can be an existing sample in your bank) and estimate the amplitude envelope of this wavfile. Modulate a waveform from your additive synthesis block containing multiple harmonics with this extracted amplitude envelope. You can also use noise as an input into the ADSR envelope. gether an arbitrary number of signals (of the same length) with different coefficients. After mixing the signals, rescale the signal (between +/- 1) to ensure that clipping does not occur during playback. Create a short composition (5-10 seconds) that consists of at least one of the new instruments you have generated ADSR envelopes for (clarinet, guitar, piano, extracted envelopes). You can also create chords (mixing of different fundamental frequencies) consisting of a single instrument.

Explanation / Answer

MathWorks signal processing products provide extensive tools and algorithm libraries that let you analyze, design, and simulate DSP systems in a fraction of the time it takes with traditional programming languages such as C and C++. Within a single, integrated algorithm and system development environment, you can: Acquire measured data and analyze signals Develop algorithms for signal processing, communications, audio, and video applications Simulate the effect of real-world conditions and system-level interactions on algorithm behavior Generate C or HDL code for execution on embedded DSPs, ASICs, and FPGAs MATLAB is integrated extensively into the text Appendices present MATLAB commands and Simulink blocksets for signal processing applications Many examples illustrate key concepts, stimulate interest, and bring out connections with any application Covers the theoretical basis and mathematical derivations Comprehensive supplementary material such as powerpoint slides and a solution manual is available for instructors. The fully comprehensive text covers most of the theoretical foundations and mathematical derivations that will be used in higher-level related subjects such as signal processing, communication, and control, minimizing the mathematical difficulty and computational burden. Complying actively with the requirement by ABET (Accreditation Board for Engineering and Technology) on integrating computer tools, it illustrates the usage of MATLAB and Simulink for signal and system analysis and design. The ideas are developed gradually with continual reference to the practical situations where they would be applicable. Many worked examples have been included in order to help develop the idea and to illustrate the material as it is presented. Throughout the book continuous-time and discrete-time signals/systems have been developed in parallel to save the time/space for explaining the two similar ones as far as there is no concern over confusion. This has the advantage of being able to contrast and compare the two ones so that ideas, insights, and intuition acquired in one can help understanding the other one and the differences between them are exploited to sharpen an understanding of the distinctive properties of each. That approach is also expected to make the readers become exposed to and interested in the relationship between continuous-time and discrete-time signals/systems above understanding each one of them individually.

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