SOME SIMPLE MANIPULATIONS OF SOUND USING DIGITAL SIGNAL PROCESSING

Richard M. Stern

18-791 demo

August 31, 2004

Department of Electrical and Computer Engineeringand School of Computer Science

Carnegie Mellon UniversityPittsburgh, Pennsylvania 15213

CarnegieMellon Slide 2 18-791 Digital Signal Processing I

The original sound and its spectrogram

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CarnegieMellon Slide 3 18-791 Digital Signal Processing I

Downsampling the waveform

Downsampling the waveform by factor of 2:

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CarnegieMellon Slide 4 18-791 Digital Signal Processing I

Consequences of downsampling

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Downsample

Downsampled:

CarnegieMellon Slide 5 18-791 Digital Signal Processing I

Upsampling the waveform

Upsampling by a factor of 2:

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CarnegieMellon Slide 6 18-791 Digital Signal Processing I

Consequences of upsampling

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Upsampled:

CarnegieMellon Slide 7 18-791 Digital Signal Processing I

Linear filtering the waveform

x[n] y[n]

Filter 1:y[n] = 3.6y[n–1]+5.0y[n–2]–3.2y[n–3]+.82y[n–4]+.013x[n]–.032x[n–1]+.044x[n–2]–.033x[n–3]+.013x[n–4]

Filter 2:y[n] = 2.7y[n–1]–3.3y[n–2]+2.0y[n–3–.57y[n–4]+.35x[n]–1.3x[n–1]+2.0x[n–2]–1.3x[n–3]+.35x[n–4]

CarnegieMellon Slide 8 18-791 Digital Signal Processing I

Filter 1 in the time domain

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CarnegieMellon Slide 9 18-791 Digital Signal Processing I

Output of Filter 1 in the frequency domain

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Lowpass:

CarnegieMellon Slide 10 18-791 Digital Signal Processing I

Filter 2 in the time domain

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CarnegieMellon Slide 11 18-791 Digital Signal Processing I

Output of Filter 2 in the frequency domain

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Highpass:

CarnegieMellon Slide 12 18-791 Digital Signal Processing I

The source-filter model of speech

A useful model for representing the generation of speech sounds:

Pitch

Pulse train source

Noise source

Vocal tract model

Amplitude

p[n]

CarnegieMellon Slide 13 18-791 Digital Signal Processing I

Separating the vocal-tract excitation from the filter

Original speech:

Speech with 75-Hz excitation:

Speech with 150-Hz excitation:

Speech with noise excitation: