Software Listing of Author : "Theodoros Giannakopoulos"

This demo provides a simple GUI for basic filtering of audio data. Using the GUI you can: * Load audio data stored in a .wav file. * Generate uniform noise in specific audio frequencies, using lowpass, highpass or bandpass digital filters. * Add the filtered noised to the original audio signal. * Remove the noise by inverse filtering. The purpose of this DEMO is not to provide robust and sophisticated denoising algorithms, but simply to demonstrate some basic audio filtering processes in Matlab.

  • Platform: Matlab, Scripts

Theodoros Giannakopoulos ------------------------------------ The provided m-file: * Reads a wav file. * Splits the audio data into non-overlapping windows (e.g 1 second). * For each window, an image of the audio data and the corresponding spectrogram is created and attached to an animated .gif file. function createAnimatedGifFromWav(wavFileName, windowLength, Width, framesPerSec) ARGUMENTS: - wavFileName: the name of the .wav file to read - windowLength: the length (in seconds) of each window to be plotted in the gif - Width: the width of the generated .gif file - framesPerSec: frames per second for the gif annotated file. NOTE: the generated .gif file's name is [wavFileName_animatedGIF.gif]...

  • Platform: Matlab, Scripts

The provided functions demonstrate a histogram-based measure for class separability, given the samples from two classes (binary classification problem). The proposed error classification estimation method is described in (B) and it is based on estimating the pdf of each class using histograms. The function that estimates the class seperability method is computeHistError(). Function theoreticalError() computes the theoretical error for two Gaussian distributed classes. Function testClassSeperability() calls the other two functions and displays the results for two Gaussian distributed functions. It has to be noted that computeHistError() can be used for any kind of class distribution, since it estimates the pdf of each class using the histogram method. We can use computeHistError() in order to estimate the separabilty of a binary...

  • Platform: Matlab, Scripts

This is a GUI that demonstrates some basic image processing functionalities, e.g., color filtering, motion filtering, etc. The user can load any of the basic image file types and then generate a secondary image based on the provided functionalities. All of the adopted functionalities make direct use of Matlab buildin functions, apart from the "Color Filter" procedure, which is not straightforward and has been implemented for this demo.

  • Platform: Matlab, Scripts

Content-based image retrieval is the task of searching images in databases by analyzing the image contents. In this demo, a simple image retrieval method is presented, based on the color distribution of the images. The user simply provides an "example" image and the search is based upon that example (query by image example). For this first version of the demo no relevance feedback is used. Almost 1000 images have been used for populating the database. For each image a 3-D histogram of it's HSV values is computed. At the end of the training stage, all 3D HSV histograms are stored in the same .mat file. In order to retrieve M (user-defined) query results, the following steps are executed: The 3D (HSV) histogram of the query image is computed. Then, the number of bins in each direction (i.e., HSV space)is duplicated by means of...

  • Platform: Matlab, Scripts

AudioAnnotation Demo v.1.0 is an open source demo implemented in Matlab(R) for manual segmentation and annotation of audio files. You can use it for defining the ground truth, in order to check your segmentation-classification algorithm's performance. It also provides the ability of calculating and plotting basic audio features (e.g. short time energy, zero crossing rate) of the selected audio segments. The following main areas are defined in the GUI: - File Info: Used for loading .wav files and presenting the file path and other audio information (e.g. sampling rate) - Current Segment: Presents time limits of the current segment, button for playing current segment and volume control - Labelling: Selection of current segment's label and button for updating the annotation file. (Important note: The class selection combo box has...

  • Platform: Matlab, Scripts

The current Matlab-code can be used for real-time audio and image processing. Fixed-length segments of audio data are recorded from soundcard's input and an image is also captured in each block. In particular, the provided code does the following: - Repetively records audio segments of fixed length. - Plots the (applitude) values of the current audio segment. - Plots the spectogram of the current audio segment. - Calculates the mean and std values of the Zero Crossing Rate for each segment, and plots those statistics for the last five segments. - Calculates and plots the average spectral distance between the current and the previous audio segment. This is actually a simple measure of change detection in the audio information. - Captures and plots an image for each block (frame). - Plots an estimation of the motion between the...

  • Platform: Matlab, Scripts

This is a simple method for silence removal and segmentation of audio streams that contain speech. The method is based in two simple audio features (signal energy and spectral centroid). As long as the feature sequences are extracted, as thresholding approach is applied on those sequence, in order to detect the speech segments.

  • Platform: Matlab, Scripts

Theodoros Giannakopoulos http:/ --------------------------------------- Feature extraction (as in most pattern recognition problems) is maybe the most important step in audio classification tasks. The provided Matlab code computes some of the basic audio features for groups of sounds stored in WAV files. Furthermore, a simple class separability measure, based on feature histograms is used for measuring the ability of each feature to be used for classifying the given classes. Therefore, you can use the provided m-files for computing the features of an audio classification problem (i.e. specific audio classes) and understanding "how good" those features are for the specific classification task. The features are calculated in a two-step way: In particular, the following audio features and respective statistics...

  • Platform: Matlab, Scripts
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