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Lab 7. Vocoder and Guitar Effects

Lab 7. Vocoder and Guitar Effects#

Aim of the experiment#

This experiment demonstrates audio effects commonly applied to harmonic instruments like the guitar.

Reading assignment#

  • Lab 7 primer

Lab 7 instructions#

Vocoder training in MATLAB#

In this exercise, we will encode a voice recording into a matrix of IIR filter coefficients.

  1. Record or generate a short segment of speech and save it to a file with 48kHz sampling rate.

    NET.addAssembly('System.Speech');
speech_synth = System.Speech.Synthesis.SpeechSynthesizer;
SetOutputToWaveFile(speech_synth,'example.wav');
Speak(speech_synth,'do re mi fa so la tee');
Dispose(speech_synth);
[y,fs] = audioread('example.wav'); y = resample(y,320,147); fs = 48e3;

    optionally, remove silence from the beginning and end of the clip

    y = flipud(y);
ind = find(abs(y) > 5e-3);
y(1:ind(1)) = [];
y = flipud(y);
ind = find(abs(y) > 5e-3);
y(1:ind(1)) = [];

  2. Break the signal into frames of length \(L = \frac{\text{sample rate}}{\text{vocoder parameter update rate}}\)

    L = 1024; N = floor(length(y)/L);
y(N*L+1:end) = [];
y = reshape(y,L,N);

  3. Learn the filter corresponding to each frame of data

    order = 16;
a = zeros(N,order);
b = zeros(N,1);
for i_frame = 1:N
    [r,lg] = xcorr(y(:,i_frame),'biased'); r(lg<0) = [];
    a(i_frame,:) = levinson(r,order-1);
    b(i_frame) = 1./freqz(1,a(i_frame,:),1);
end
a(isnan(a)) = 0;
b(isnan(b)) = 0;

  4. Format the coefficients for C

    num_coeffs = sprintf('%f,',b)
den_coeffs = sprintf( ['{', repmat('%f,',[1,order]) '},\n'], a')

Vocoder effect in C#

In this exercise, you will apply the learned vocal filter to input data in real time.

  1. Define L, N, and ORDER based on the vocoder filters from MATLAB

    #define L 1024
#define N 68
#define ORDER 16

  2. Initialize a matrix with the filter coefficients computed in MATLAB.

    float32_t B[N] = {<exported numerator coefficients>};
float32_t A[N][ORDER] = {<exported denominator coefficients>};

  3. Create variables to track the current filter index

    uint32_t i_sample = 0;
uint32_t i_filter = 0;

  4. Create an array to hold the previous output values

    float32_t y[ORDER] = {0};

  5. In process_left_sample, apply the all-pole IIR filter

    y[0] = B[i_filter]*INPUT_SCALE_FACTOR*input_sample;
for (uint32_t delay = 1; delay < ORDER; delay+=1)
{
	y[0] -= y[delay]*A[i_filter][delay];
}

output_sample = OUTPUT_SCALE_FACTOR*y[0];

for (uint32_t delay = ORDER-1; delay > 0; delay-=1)
{
	y[delay] = y[delay-1];
}

  6. Increment the variables which track the current filter index

    i_sample += 1;
if (i_sample == L)
{
    i_sample = 0;
    i_filter += 1;
    if (i_filter == N){i_filter = 0;}
}

  7. In process_right_sample, set the output to be identical to the left channel

    output_sample = OUTPUT_SCALE_FACTOR*y[0];

  8. Using a separate phone/laptop, provide any input which contains harmonically rich instrument(s), like a guitar, violin, or synthesizer. Verify that the output shares characteristics of the original speech signal.

    One option is to use a synthesizer app in your browser controlled by the top row (QWERTY…) of your keyboard.

Flanger#

In this exercise, you will implement the flanger using the feedforward form of the comb filter.

\[y[n] = x[n] + \alpha x\left[n-K[n]\right]\]
\[K[n] = \frac{R}{2} \left( \cos(\omega_{\text{flanger}}) + 1 \right)\]

  1. Define the parameters of the flanger. For this example, the flanger has frequency of 0.5 Hz and a maximum delay of 10 ms so \(\omega_{\text{flanger}} = 2 \pi \frac{0.5}{48000} = 6.5 \times 10^{-05} \frac{\text{radians}}{\text{sample}}\) and \(R = 48000 \text{ Hz} \times 10 \text{ ms} = 480 \text{ samples}\)

    #define alpha 0.75
#define R 480
#define BUFFER_LENGTH 481
#define OMEGA 0.0000654498469497874
#define FLANGER_PERIOD 96000

  2. Create two circular buffers (one for each channel) to store previous input values. Also create a counter to track the oscillation.

    int16_t x_circ[2][BUFFER_LENGTH] = {0};
int32_t i1 = 0;
int32_t i2 = 0;
int32_t n = 0;

  3. In process_left_sample, update the indices of the circular buffer and add the most recent sample.

    i2 = i1;
i1 = (i1+1) % BUFFER_LENGTH;
x_circ[0][i2] = input_sample;

  4. In process_right_sample, add the newest input sample but leave the indices unchanged

    x_circ[1][i2] = input_sample;

  5. Implement the flanger

    float32_t x_current;
float32_t x_delayed;
size_t Kn;
size_t ind;

x_current = INPUT_SCALE_FACTOR*x_circ[<channel>][i2];

Kn = 0.5*R*(arm_cos_f32(OMEGA*n) + 1.0);

if (Kn > i2)
{
    ind = (i2 + BUFFER_LENGTH) - Kn;
} else
{
    ind = i2 - Kn;
}

x_delayed = INPUT_SCALE_FACTOR*x_circ[<channel>][ind];

output_sample = OUTPUT_SCALE_FACTOR*(alpha*x_delayed + x_current);

  6. In process_right_sample only, increment the counter

    n = (n+1) % FLANGER_PERIOD;

  7. Using a separate phone/laptop, play a recording which contains harmonically rich instrument(s), like a guitar, violin, or synthesizer, and listen to the output.

Distortion#

In this exercise, you will implement harmonic distortion by clipping.

  1. Add a nonlinearity to the input signal to increase the energy at higher harmonics. Whenever the absolute value of the signal exceeds some threshold \(L\), set the value equal to \(\text{sgn}(x[n]) L\)

    #define CLIP 0.1

    float32_t x;
float32_t y;
x = INPUT_SCALE_FACTOR*input_sample;
if (x < -CLIP)
{
    y = -CLIP;
}
else if ( x > CLIP )
{
    y = CLIP;
}
else
{
    y = x;
}
output_sample = OUTPUT_SCALE_FACTOR*y;

    Ensure that the same effect is active on both channels.

  2. Using a separate phone/laptop, play a recording which contains harmonically rich instrument(s), like a guitar, violin, or synthesizer, and listen to the output.

Lab report contents#

Good luck on exam two!

Contents