Applied Signal Processing: A MATLAB™-Based Proof of Concept by Thierry Dutoit
By Thierry Dutoit
This is a project-oriented laboratory ebook with on-line supplementary documents keen on electronic sign processing (DSP) for college kids, teachers and execs. The authors supply 11 huge and designated initiatives which take the reader during the necessities of sign processing purposes. integrated are MATLAB codes that are absolutely commented on for constructing operating proofs of thoughts. The accompanying on-line fabric including the textual content at once integrates thought into practice.
The 11 initiatives comprise speech coding for mobile phones, knowing mind experiment imaging, how MP3 avid gamers compress the knowledge expense of audio CDs, embedding inner most details in track with no detection, and lots more and plenty more.
Applied sign Processing: A MATLAB™-Based evidence of Concept permits readers to learn from the event and services of execs, researchers, and teachers in numerous utilized sign processing comparable fields, offered in a undertaking framework. it truly is perfect for lecture and laboratory classes, and the subject material is appropriate to be used in addition to most traditional textbooks.
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Extra info for Applied Signal Processing: A MATLAB™-Based Proof of Concept
If we want to synthesize speech with constant pitch period length different from a submultiple of 80 samples (say, N0=65 samples), we additionally need to take care of a possible pitch period offset in the How is speech produced in a cell phone conversation? 21 excitation signal. 5 Unvoiced linear prediction synthesis of a speech file Synthesizing the complete speech file as LPC unvoiced speech is easy. 3. % Generating 10 ms of excitation excitation=randn(80,1); % White Gaussian noise gain=sigma; As expected, the resulting speech sounds like whisper.
The resulting synthetic speech sounds more natural than in LPC10. Plosives are much better rendered, and voiced sounds are no longer buzzy, but speech sounds a bit noisy. Note that pitch and V/UV estimation are no longer required. One can see that the closed-loop optimization leads to excitation frames, which can somehow differ from the LP residual, while the resulting synthetic speech is similar to its original counterpart (Fig. 22). In the above script, though, each new frame was processed independently of past frames.
Since voiced speech is strongly selfcorrelated, it makes sense to incorporate a long-term prediction filter in cascade with the LPC (short-term) prediction filter. In the example below, we can reduce the number of stochastic components from 10 to 5 while still increasing speech quality, thanks to long-term prediction. N_components= 5; % number of codebook components per frame How is speecch produced in n a cell phonee conversation? , a bit mo ore than 30 ms of speeech) for findingg the best lonng-term prediiction delay in i the range [0–256] sam mples.