A mixed-signal correlator for acoustic transient classification

Abstract

Correlation computations are widely used in template-based temporal pattern recognition, but are expensive to compute in real time on DSP systems. We have designed and fabricated a low-power chip which uses current-mode analog circuits to compute the correlation between an auditory input signal in the time-frequency domain and a stored binary template. We use a bucket-brigade device (BBD) to accumulate partial column sums over time at rates consistent with the auditory-band input. Experimental results of the correlator demonstrate its correct operation over a wide range of operating frequencies. I . INTRODUCTIONMany time-based classification systems compute the correlation of an incoming discrete-time signal or signals with a predetermined template [ 11. While for speech and other complex long-term signals it is necessary to perform dynamic time warping (DTW) or similar weighting of the incoming signal [ 2 ] , for transients (short-term acoustic events of less than approximately 1/10 of a second), a simple correlation in the time-frequency domain yields accurate classification results [3].A general form of the simple correlation iswhere M is the number of frequency channels of the input, N is the maximum number of time bins in the window, IC is the array of input signals split into frequency bands, p , is the matrix of template pattern values for pattern z , and t is the current time.This formula produces a running correlation c,[t] of the input array with the template z. For large M and N , this algorithm can be expensive to execute on a DSP in terms of speed and power requirements. However, our approach lends itself elegantly to low-power parallel analog computation in the form of MOS transistor circuits operating primarily in the subthreshold mode.In the following discussion, we present our algorithm as a set of incremental modifications to the baseline algorithm of Equation (1). First, we normalize the input, which is essential for the steps which follow. Next, we take time derivatives of the input and templates, In this form, we are able to make the template values binary without significantly increasing classification error rates. Finally, we move the time-differencing to the output, yielding the simplest possible form of the equation. SIMPLIFYING THE CORRELATION EQUATIONWe assume the input x to the system is an acoustic signal, split into M frequency bands. We compute the (rectified) energy envelope for each band, denoted y, and then normalize these system inputs by the functionwhere 8 is a threshold value included to suppress noise during quiet invervals in the input. Pineda et a1. [4] have shown that the normalized input representation is essential to significantly simplify the pattern classifier algorithm, greatly reducing the size and complexity of the hardware implementation but not degrading the classification result.To make the calmputation less expensive, first we use the time difference between samples of the input and compare it to the difference between samples of the template. The effect of thi...

Synthesis of Log-Domain Filters from First-Order Building Blocks

“…We have fabricated and tested several different chips, one of which was a test chip with individual ®lter structures on it, and one a complete signal-processing system [13] containing a ®lterbank of log-domain bandpass ®lters (Fig. 10).…”

“…Design considerations for low-frequency operation ensure correct translinear operation of the bipolar transistors even at collector currents lower than 10 pA. We have characterized performance of the bandpass ®lters in the context of a high layout density 15-channel ®lterbank system. Performance of these ®lters is adequate for the applications for which they were designed [13]. …”

“…Our work instead concentrates on the use of logdomain ®lters for audio-frequency applications [8,11,12], the use of current-mode ®ltering for current-mode applications, and the application of log-domain ®lters in system design [13]. The low frequency range of audio requires a large RC time constant, which for the log-domain ®lters described here is inversely proportional to the bias current I b .…”

Mixed-mode correlator for micropower acoustic transient classification

“…The algorithm for acoustic transient classification on which the correlator is based is described in [17] and [18], demonstrating effective classification on a test set containing prerecorded examples of ten different transient sounds [19]. In its baseline form, the template correlation can be written (1) where (for acoustic tasks)…”