By Ian McLoughlin
This hands-on, one-stop source describes the main strategies of speech and audio processing illustrated with vast MATLAB examples.
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Additional resources for Applied Speech and Audio Processing : With Matlab Examples
4 Mixing sounds Given two audio arrays of equal length we may combine or mix them together. 0 limit which would cause the sound() function to clip). 77, 8000, soundsc(C+E+G+B, 8000); 2); 2); 2); 2); For those readers with a musical theory background, it should be relatively easy to create Matlab functions able to create speciﬁed chords, minors or majors and replay them on demand. 001:5],0,5,500); z=[y,y[length(y):-1:1],y]; %construct a frequency chirp %make a zig-zag First let’s take one huge FFT and plot this as shown below: f=abs(fft(z, 8192)); plot(f(1:4096)); Absolute FFT plot of chirp spectrum.
By that I mean some type of sound that is contained within a vector of samples. Now when that vector is analysed it might happen that the feature is split into two: half appears in one audio frame, and the other half in another frame. The complete feature does not appear in any analysis window, and may have effectively been hidden. In this way, features that are lucky enough to fall in the centre of a frame are emphasised at the expense of features which are chopped in half. 2), this problem is exacerbated further since audio at the extreme ends of an analysis frame will be de-emphasised further.
1). One further temporal generalisation is that of syllabic rate, the rate at which syllables are articulated. For most languages this remains fairly constant, and varies only slightly between individuals and types of speech . One or more agglomerated phonemes can make up a syllable sound. For humans, it seems that the simplest unit of recognition may be the syllable, whereas the phoneme is a distinction generally made by speech researchers aiming to determine a set of basic building blocks for speech sounds.
Applied Speech and Audio Processing : With Matlab Examples by Ian McLoughlin