Voiceband signal classifier

Details Voiceband signal classifier Voiceband signal classifier

1999-04-30

2004-03-16

Knepper, David D. (Department: 2654)

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

Reexamination Certificate

active

06708146

ABSTRACT:

BACKGROUND OF THE INVENTION
Within digital communications networks it is often desirable to be able to monitor the different types of traffic that are being transported and, specifically, to be able to assign each monitored connection to one of a number of expected signal classes. For example, within a digital telephone network it is often desirable to determine which type of voiceband traffic is being carried on 64 Kbps channels. Possible voiceband classes could be idle channels, voice signals, and voiceband data signals such as modem signals and facsimile signals. For the voiceband classification problem several methods have been proposed in the literature.
For example, using two discriminant variables, Benvenuto reports that voice and VBD signals can be distinguished in as little as 32 ms [N. Benvenuto, A Speech/Voiceband Data Discriminator,
IEEE Trans. Comm
., vol. 41, no. 4, April 1993, pp. 539-543 and see U.S. Pat. Nos. 4,815,136 and 4,815,137 of Benvenuto]. The normalized second lag of the autocorrelation sequence (ACS) and the normalized central second-order moment of the amplitude of the complex baseband signal are used as the two sole discriminant variables. Benvenuto observes that the second lag of the ACS is usually positive for voice and negative for non-voice signals. The central second-order moment is shown to be an approximate indicator of the non-voice signal complexity in addition to being useful for voice versus non-voice discrimination.
Before classification, the signal is sampled (if analog) and divided into segments containing N samples each. Each segment must contain sufficient signal energy throughout to be acceptable for further processing. Benvenuto denotes the complex discrete-time low-pass signal by &ggr;(n), where n is the discrete time index. This signal is obtained by mixing the passband signal with an estimated carrier of 2 KHz and then low pass filtered. The autocorrelation sequence at lag k, denoted by R
&ggr;
(k), is estimated by Benvenuto as
R
&ggr;
(
k
)=(1
/N
)&Sgr;
i=1
N
&ggr;(
i+k
)&ggr;*(
i
),
where &ggr;*(i) denotes the complex conjugate of &ggr;(i). The values of R
&ggr;
(k) are often normalized with respect to R
&ggr;
(0), which is the average power for cyclostationary processes. When so normalized, the autocorrelation at lag k is denoted by (~R)
&ggr;
(k). The normalized central second-order moment of a signal &ggr;(n) is given by (~&eegr;)
2
=(m
2
/m
1
2
)−1, where
m
1
=(1
/N
)&Sgr;
i=1
N
|&ggr;(
i
)|
m
2
=(1
/N
)&Sgr;
i=1
N
|&ggr;(
i
)|
2
,
and |&ggr;(i)| denotes the phasor amplitude of &ggr;(i).
Benvenuto found experimentally that (~&eegr;)
2
and the normalized second lag (~R)
&ggr;
(2), when considered together as discriminant variables, are effective for discriminating voice from non-voice. Using 32 ms signal segments, speech was misclassified as VBD about 1% of the time. With well-chosen decision boundaries, VBD is rarely misclassified as speech. On the other hand, Benvenuto's method has less success when applied to classify other voiceband signals.
Signals such as V.34 modem, V.22bis modem, and speech, may be classified on the basis of their differing power spectral density (PSD) shapes. The PSD of a signal can be obtained by computing the Fourier transform directly, or the Fourier transform can be estimated using faster techniques. However, computing Fourier transforms requires large numbers of floating point operations (FLOPS), in the order of 10
5
FLOPS per PSD. On the other hand, computing autocorrelations requires substantially fewer FLOPS, in the order of 10
4
FLOPS for a 32 ms signal segment.
Commercial voiceband classifiers known to be available in the art include CTel's NET-MONITOR System 2432, AT&T's Voice/Data Call Classifier, Tellabs' Digital Channel Occupancy Analyzer, and MPR Teltech Ltd.'s Service Discrimination Unit. Many of these units exploit call set-up signaling to aid classification and/or use computationally expensive spectral analysis techniques. For the voiceband signal classification problem, the new classification method permits physically smaller and cheaper classifiers with classification resolution and accuracy superior to that of commercially available units.
SUMMARY OF THE INVENTION
The inventors propose a new signal classifier and method of classifying a signal. The new classification method achieves greater accuracy with lower computational effort than prior art methods such as that of Benvenuto. For the voiceband classification problems the new method classifies a broader set of voiceband signals and has lower misclassification rates by virtue of employing computationally efficient discriminant variables and preferably using statistically optimal (or near-optimal) discriminant functions.
The signal classification method may operate on the signal being carried by a connection without having knowledge of when the connection may have been created. The method may also be employed in situations where there is access to only one direction of a bidirectional connection. Thus connections do not have to be monitored full-time; this avoids requiring knowledge of initial handshaking sequences or signalling data and is consistent with the scenario where the classifier sequentially scans over many connections, spending only a brief time monitoring the signal on each connection in turn.
The invention involves the use of information in the initial lags of the autocorrelation function of the signal.
In other aspects of the invention, improved techniques are used to classify signals: (a) to perform full-wave rectification rather than complex demodulation; (b) to use an improved estimate of the ACS on the passband signal; (c) to use statistical methods to determine an optimal subset of ACS lags to include as discriminant variables for greater VBD signal resolution; and (d) to use statistical methods to form optimal or near-optimal discriminant functions.
Therefore, there is provided, in accordance with one aspect of the invention, a signal classifier for classifying a signal into one of a plurality of signal classes, the signal having at least one segment with N samples. The signal classifier comprises an autocorrelator that generates more than one autocorrelation coefficient and a discriminator that operates on more than one, but less than N, autocorrelation coefficients to discriminate between signal classes. The discriminator implements both a linear decision sub-system and a non-linear decision sub-system. In another aspect of the invention, there is provided means to compute a normalized central second-order moment of the segment, and in which the discriminator is operable on the normalized central second-order moment. The means to compute the central second-order moment of the segment preferably includes a rectifier for rectifying the signal before computation of the central second-order moment.
A power estimator, for estimating the average power of the signal over the segment, may be used, together with an idle channel detector, to identify when the signal power is below a threshold for a given segment. The output of the power estimator may also be used to normalize the autocorrelation coefficients.
These and other aspects of the invention are described in the detailed description and claims that follow.


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