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Signal Processing

The EST signal processing library provides a set of standard signal processing tools designed specifically for speech analysis. The library includes:

Overview

Design Issues

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.

Common Processing model

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:

  1. Start with a waveform and a series of analysis positions, which can be a fixed distance apart of specified by some other means.
  2. For each analysis position, define a small portion of the waveform around that position, Multiply this by a windowing function to produce a vector of speech samples.
  3. Pass this to a frame based signal processing routine which outputs values in another vector.
  4. Add this vector to a position in an EST_Track which correponds to the analysis time position.

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.

Track Allocation, Frames, Channels and sub-tracks

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 Frame based signal processing, Access multiple frames or channels. and Access single frames or single channels..

Functions

Functions for Generating 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 for Generating Tracks

Functions which operate on a whole waveform and generate coefficients for a track.

These functions are a nice set of stuff

Functions for Windowing Frames of Waveforms

Filter functions

A filter modifies a waveform by changing its frequency characteristics. The following types of filter are currently supported:

Filter design

Example

Example of Signal Processing code

Programs

The following are exectutable programs which are used for signal processing:

The following programs are also useful in signal processing: