The EST signal processing library provides a set of standard signal processing tools designed specifically for speech analysis. The library includes:
The signal processing library is designed specifically for speech applications and hence all functions are written with that end goal in mind. The design of the library has centered around building a set of commonly used easy to configure analysis routines.
Speed: We have tried to make the functions as fast as possible. Signal processing can often be time critical, and so it will always be the case that if the code for a particular signal processing algroithm is written in a single function loop it will run faster than by using libraries.
However, the signal processing routines in the EST library are in general very fast, and the fact that they use classes such as EST_Track and EST_FVector does not make them slower than they would be if
float * etc was used.
types: The library makes heavy use of a small number of classes, specifically EST_Wave, EST_Track and EST_FVector. These classes are basically arrays and matrices, but take care of issues such as memory managment, error handling and file i/o. Using these classes in the library helps facilitate clean and simple algorithm writing and use. It is strongly recommended that you gain familiarity with these classes before using this part of the library.
At present, the issue of complex numbers in signal processing is somewhat fudged, in that a vector of complex numbers is represented by a vector of real parts and a vector of imaginary parts, rather than as a single vector of complex numbers.
In speech, a large number of algorithms follow the same basic model, in which a waveform is analysed by an algorithm and a Track, containing a series of time aligned vectors is produced. Regardless of the type of signal processing, the basic model is as follows:
Given this model, the signal processing library breaks down into a number of different types of function:
Nearly all functions in the signal processing library belong to one of the above listed types. Quite often functions are presented on both the utterance and frame level. For example, there is a function called sig2lpc which takes a single frame of windowed speech and produces a set of linear prediction coefficients. There is also a function called sig2coef which performs linear prediction on a whole waveforn, returning the answer in a Track. sig2coef uses the common processing model, and calls sig2lpc as the algorithm in the loop.
Partly for historical reasons some functions, e.g. pda are only available in the utterance based form.
When writing signal processing code for this library, it is often the case that all that needs to be written is the frame based algorithm, as other algorithms can do the frame shifting and windowing operations.
The signal processing library makes extensive use of the advanced features of the track class, specifically the ability to access single frames and channels.
Given a standard multi-channel track, it is possible to make a FVector point to any single frame or channel - this is done by an internal pointer mechanism in EST_FVector. Furthermore, a track can be made to point to a selected number of channels or frames in a main track.
For example, imagine we have a function that calculates the covariance matrix for a multi-dimensional track of data. But the data we actually have contains energy, cepstra and delta cepstra. It is non-sensical to calculate convariance on all of this, we just want the cepstra. To do this we use the sub-track facility to set a temporary track to just the cepstral coefficients and pass this into the covariance function. The temporary track has smart pointers into the original track and hence no data is copied.
Without this facility, either you would have to do a copy (expensive) or else tell the covariance function which part of the track to use (hacky).
Extensive documentation describing this process is found in sigpr-example-frames, tr_example_access_multiple_frames and tr_example_access_single_frames.
The following set of functions perform either a signal processing operation on a single frame of speech to produce a set of coefficients, or a transformation on an existing set of coefficients to produce a new set. In most cases, the first argument to the function is the input, and the second is the output. It is assumed that any input speech frame has already been windowed with an appropriate windowing function (eg. Hamming) - see "Windowing mechanisms" on how to produce such a frame. See also Functions for Generating Tracks.
It is also assumed that the output vector is of the correct size. No resizing is done in these functions as the incoming vectors may be subvectors of whole tracks etc. In many cases (eg. lpc analysis), an order parameter is required. This is usually derived from the size of the input or output vectors, and hence is not passed explicitly.
Functions which operate on a whole waveform and generate coefficients for a track.
These functions are a nice set of stuff
A filter modifies a waveform by changing its frequency characteristics. The following types of filter are currently supported:
The following are exectutable programs which are used for signal processing:
The following programs are also useful in signal processing: