Value must be 'None'. Valid values for this parameter on Windows are

'Indeo3', 'Indeo5', 'Cinepak', 'MSVC', 'RLE' or 'None'.

To use a custom compressor, the value can be the four character code as

specified by the codec documentation. An error will result during the

call to ADDFRAME if it can not find the specified custom compressor.

This parameter must be set before using ADDFRAME.

The default is 'Indeo5' on Windows and 'None' on UNIX.

Note: Indeo5 may not be available in some versions of Windows.

QUALITY - A number between 0 and 100. This parameter has no effect

on uncompressed movies. This parameter must be set before using

ADDFRAME. Higher quality numbers result in higher video quality and

larger file sizes, where lower quality numbers result in lower video

quality and smaller file sizes. The default is 75.

KEYFRAME - For compressors that support temporal compression, this

is the number of key frames per second. This parameter must be set

before using ADDFRAME. The default is 2 key frames per second.

COLORMAP - An M-by-3 matrix defining the colormap to be used for indexed AVI movies. M must be no greater than 256 (236 if using Indeo

compression). This parameter must be set before calling ADDFRAME, unless

you are using ADDFRAME with the MATLAB movie syntax. There is no

default colormap.

VIDEONAME - A descriptive name for the video stream. This parameter

must be no greater than 64 characters long and must be set before using

ADDFRAME. The default is the filename.

AVIFILE properties may also be set using MATLAB structure syntax. For

example, to set the Quality property to 100 use the following syntax:

aviobj = avifile(filename);

aviobj.Quality = 100;

Example:

t = linspace(0,2.5*pi,40);

fact = 10*sin(t);

fig=figure;

aviobj = avifile('example.avi')

[x,y,z] = peaks;

for k=1:length(fact)

h = surf(x,y,fact(k)*z);

axis([-3 3 -3 3 -80 80])

axis off

caxis([-90 90])

F = getframe(fig);

aviobj = addframe(aviobj,F);

end

close(fig)

aviobj = close(aviobj);

See also avifile/addframe, avifile/close, movie2avi.

Reference page in Help browser

doc avifile

Utilities

<lin2mu> - Convert linear signal to mu-law encoding.

LIN2MU Convert linear signal to mu-law encoding.

MU = LIN2MU(Y) converts linear audio signal amplitudes

in the range -1 <= Y <= 1 to mu-law encoded "flints"

in the range 0 <= MU <= 255.

See also mu2lin, auwrite for more details and references.

Reference page in Help browser

doc lin2mu

<mu2lin> - Convert mu-law encoding to linear signal.

MU2LIN Convert mu-law encoding to linear signal.

Y = MU2LIN(MU) converts mu-law encoded 8-bit audio signals,

stored as "flints" in the range 0 <= MU <= 255, to

linear signal amplitude in the range -s < Y < s where

s = 32124/32768 ~= .9803. The input MU is often obtained

using fread(...,'uchar') to read byte-encoded audio files.

"Flints" are MATLAB's integers -- floating point numbers

whose values are integers.

See also lin2mu, auread.

Reference page in Help browser

doc mu2lin

Example audio data (MAT files)

<chirp> - Frequency sweeps (1.6 sec, 8192 Hz)

CHIRP Swept-frequency cosine generator.

Y = CHIRP(T,F0,T1,F1) generates samples of a linear swept-frequency

signal at the time instances defined in array T. The instantaneous

frequency at time 0 is F0 Hertz. The instantaneous frequency F1

is achieved at time T1. By default, F0=0, T1=1, and F1=100.

Y = CHIRP(T,F0,T1,F1,method) specifies alternate sweep methods.

Available methods are 'linear','quadratic', and 'logarithmic'; the

default is 'linear'. Note that for a logarithmic-sweep, F0>=1e-6 is

required and by default F0=1e-6.

Y = CHIRP(T,F0,T1,F1,method, PHI) allows an initial phase PHI to

be specified in degrees. By default, PHI=0.

Y = CHIRP(T,FO,T1,F1,'quadratic',PHI,'concave') generates samples of

a quadratic swept-frequency signal whose spectrogram is a parabola with

its concavity in the positive frequency axis.

Y = CHIRP(T,FO,T1,F1,'quadratic',PHI,'convex') generates samples of

a quadratic swept-frequency signal whose spectrogram is a parabola with

its convexity in the positive frequency axis.

Default values are substituted for empty or omitted trailing input

arguments.

EXAMPLE 1: Compute the spectrogram of a linear chirp.

t=0:0.001:2; % 2 secs @ 1kHz sample rate

y=chirp(t,0,1,150); % Start @ DC, cross 150Hz at t=1sec

spectrogram(y,256,250,256,1E3); % Display the spectrogram

EXAMPLE 2: Compute the spectrogram of a quadratic chirp.

t=-2:0.001:2; % +/-2 secs @ 1kHz sample rate

y=chirp(t,100,1,200,'q'); % Start @ 100Hz, cross 200Hz at t=1sec

spectrogram(y,128,120,128,1E3); % Display the spectrogram

EXAMPLE 3: Compute the spectrogram of a "convex" quadratic chirp

t= 0:0.001:1; % 1 second @ 1kHz sample rate

fo=25;f1=100; % Start at 25Hz, go up to 100Hz

y=chirp(t,fo,1,f1,'q',[],'convex');

spectrogram(y,256,200,256,1000); % Display the spectrogram.

EXAMPLE 4: Compute the spectrogram of a "concave" quadratic chirp

t= 0:0.001:1; % 1 second @ 1kHz sample rate

fo=100;f1=25; % Start at 100Hz, go down to 25Hz

y=chirp(t,fo,1,f1,'q',[],'concave');

spectrogram(y,256,200,256,1000); % Display the spectrogram.

EXAMPLE 5: Compute the spectrogram of a logarithmic chirp

t= 0:0.001:10; % 10 seconds @ 1kHz sample rate

fo=10;f1=400; % Start at 10Hz, go up to 400Hz

y=chirp(t,fo,10,f1,'logarithmic');

spectrogram(y,256,200,256,1000); % Display the spectrogram.

See also gauspuls, sawtooth, sinc, square.

Reference page in Help browser

doc chirp

<gong> - Gong (5.1 sec, 8192 Hz)

gong not found.

Use the Help browser search field to search the documentation, or

type "help help" for help command options, such as help for methods.

<handel> - Hallelujah chorus (8.9 sec, 8192 Hz)

handel not found.

Use the Help browser search field to search the documentation, or

type "help help" for help command options, such as help for methods.

<laughter> - Laughter from a crowd (6.4 sec, 8192 Hz)

laughter not found.

Use the Help browser search field to search the documentation, or

type "help help" for help command options, such as help for methods.

<splat> - Chirp followed by a splat (1.2 sec, 8192 Hz)

splat not found.

Use the Help browser search field to search the documentation, or

type "help help" for help command options, such as help for methods.

<train> - Train whistle (1.5 sec, 8192 Hz)

--- help for network/train ---

TRAIN Train a neural network.

[NET,TR] = train(NET,X,T) takes a network NET, input data X

and target data T and returns the network after training it, and a

a training record TR.

[NET,TR] = train(NET,X) takes only input data, in cases where

the network's training function is unsupervised (i.e. does not require

target data).

[NET,TR] = train(NET,X,T,Xi,Ai,EW) takes additional optional

arguments suitable for training dynamic networks and training with

error weights. Xi and Ai are the initial input and layer delays states

respectively and EW defines error weights used to indicate

the relative importance of each target value.

train calls the network training function NET.trainFcn with the

parameters NET.trainParam to perform training. Training functions

may also be called directly.

train arguments can have two formats: matrices, for static

problems and networks with single inputs and outputs, and cell arrays

for multiple timesteps and networks with multiple inputs and outputs.

The matrix format is as follows:

X - RxQ matrix

Y - UxQ matrix.

Where:

Q = number of samples

R = number of elements in the network's input

U = number of elements in the network's output

The cell array format is most general:

X - NixTS cell array, each element X{i,ts} is an RixQ matrix.

Xi - NixID cell array, each element Xi{i,k} is an RixQ matrix.

Ai - NlxLD cell array, each element Ai{i,k} is an SixQ matrix.

Y - NOxTS cell array, each element Y{i,ts} is a UixQ matrix.

Xf - NixID cell array, each element Xf{i,k} is an RixQ matrix.

Af - NlxLD cell array, each element Af{i,k} is an SixQ matrix.

Where:

TS = number of time steps

Ni = NET.numInputs

Nl = NET.numLayers,

No = NET.numOutputs

ID = NET.numInputDelays

LD = NET.numLayerDelays

Ri = NET.inputs{i}.size

Si = NET.layers{i}.size

Ui = NET.outputs{i}.size

The error weights EW can be 1, indicating all targets are equally

important. It can also be either a 1xQ vector defining relative sample

importances, a 1xTS cell array of scalar values defining relative

timestep importances, an Nox1 cell array of scalar values defining

relative network output importances, or in general an NoxTS cell array

of NixQ matrices (the same size as T) defining every target element's

relative importance.

Here a static feedforward network is created, trained on some data, then

simulated using SIM and network notation.

[x,t] = simplefit_dataset;

net = feedforwardnet(10);

net = train(net,x,t);

y1 = sim(net,x)

y2 = net(x)

Here a dynamic NARX network is created, trained, and simulated on

time series data.

[X,T] = simplenarx_dataset;

net = narxnet(1:2,1:2,10);