Applied Signal Processing: A MATLAB™-Based Proof of Concept by Thierry Dutoit

By Thierry Dutoit

This is a project-oriented laboratory e-book with on-line supplementary documents involved in electronic sign processing (DSP) for college students, teachers and execs. The authors offer 11 huge and distinct initiatives which take the reader in the course of the necessities of sign processing functions. incorporated are MATLAB codes that are absolutely commented on for constructing operating proofs of strategies. The accompanying on-line fabric including the textual content at once integrates thought into practice.

The 11 initiatives comprise speech coding for mobile phones, figuring out mind experiment imaging, how MP3 gamers compress the information cost of audio CDs, embedding deepest details in song with no detection, and masses more.

Applied sign Processing: A MATLAB™-Based facts of Concept permits readers to learn from the adventure and services of execs, researchers, and teachers in various utilized sign processing comparable fields, provided in a undertaking framework. it truly is excellent for lecture and laboratory classes, and the subject material is suitable to be used in addition to most traditional textbooks.

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Extra resources for Applied Signal Processing: A MATLAB™-Based Proof of Concept

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Ai_perceptual, LP_residual, z_gamma_s); % Filtering both codebooks LTP_codebook_filt = filter(1, ai_perceptual, LTP_codebook); codebook_filt = filter(1, ai_perceptual, codebook); The search for the best long-term predictor is performed as before, except that the perceptually filtered speech input is used as the reference from which to find codebook components. , zeros(frame_shift,1), z_gamma_e); signal = perceptual_speech - ringing; [LTP_gain, LTP_index] = find_Nbest_components(signal, ... LTP_codebook_filt, 1); % Generating the corresponding prediction LT_prediction= LTP_codebook(:,LTP_index)*LTP_gain; % Finding speech_frame components in the filtered codebook % taking long term prediction into account signal = signal - LTP_codebook_filt(:,LTP_index)*LTP_gain; [gains, indices] = find_Nbest_components(signal, ...

Fig. 21 LPC10 speech How is speech produced in a cell phone conversation? 7 CELP analysis–synthesis of a speech file Our last step will be to replace the LPC10 excitation by a more realistic code-excited linear prediction (CELP) excitation obtained by selecting the best linear combination of excitation components from a codebook. Component selection is performed in a closed loop so as to minimize the difference between the synthetic and original signals. 11 MATLAB function involved: • [gains, indices] = find_Nbest_components(signal, ...

In contrast, formants (resonant frequencies of the vocal tract) appear as dark (and rather wide) horizontal traces. , guess the corresponding words). This clearly shows that formants are a good indicator of the underlying speech sounds. , 240 samples) of the speech file and plot its samples (Fig. 12). input_frame=speech(3500:3739); plot(input_frame); Fig. , 80 ms; fundamental frequency = 125 Hz). Note, though, that this is only apparent; in practice, no sequence of samples can be found more than once in the frame.

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