An Algorithm for Determining Talker Location using a Linear Microphone Array and Optimal Hyperbolic Fit

An Algorithm for Determining Talker Location using a Linear Microphone Array and Optimal Hyperbolic Fit Harvey F. Silverman Laboratory for Engineering Man/Machine Systems (LEMS) Division of Engineering Brown University Providence, RI 02912 Abstract One of the problems for all speech input is the necessity for the talker to be encumbered by a head. mounted, hand-held, or fixed position microphone. An intelfigent, electronically-aimed unidirectional micro- phone would overcome this problem. Array tech- niques hold the best promise to bring such a system to practicality. The development of a robust algorithm to determine the location of a talker is a fundamental is- sue for a microphone-array system. Here, a two-step talker-location algorithm is introduced. Step 1 is a rather conventional filtered cross-correlation method; the cross-correlation between some pair of micro- phones is determined to high accuracy using a some- what novel, fast interpolation on the sampled data. Then, using the fact that the delays for a point source should fit a hyperbola, a best hyperbolic fit is obtained using nonlinear optimization. A method which fits the hyperbola directly to peak-picked delays is shown to be far less robust than an algorithm which fits the hy- perbola in the cross-correlation space. An efficient, global nonlinear optimization technique, Stochastic re- gion Contraction (SRC) is shown to yield highly accu- rate (>90%), and computationally efficient, results for a normal ambient. Introduction One of the problems for all speech input is the necessity for the talker to be encumbered by a hcad- mounted, hand-held, or fixed position microphone, or, perhaps, a technician-conlxolled mechanical unidirectional microphone. Whether for teleconferencing [I], speech recognition [2], or large-room recording or conferencing [3], an intelligent, eleclronically-aimed unidirectional mi- crophone would overcome this problem. Array tech- niques hold the best promise to bring such a system to practicality. Algorithms for passive tracking -- the determina- tion of range, bearing, speed, and signature as a function of time for a moving object -- have been studied for near- ly 100 years partiomLqrly for radar and sonar systems. While there is currently much activity involved with the wacking of multiple sources using variants of the eigenvalue-hased decomposition MUSIC algorithm, [4], [5], [6], [7], [8], most systems still use correlational tech- niques [9], [10], [11]. The method presented here is also based on correla- tion. First, a coarse, normalized cross-correlation func- tion is computed over the delay range of interest. It turns out that, even for the relatively high sampfing rate of 20kHz, the 5Olas resolution of the time-delay estimates causes derived locations to be unsatisfactory. However, the latter may be refined by nearly two orders of magni- tude through accurate interpolation techniques which can be attained for a relatively small computational using multirate filtering[12]. For M microphones, one can estimate M-1 in- dependent relative delays. As, theoretically, only two re- lative delays are needed to triangulate a source, for M >3, the system is overspecified. However, since noise is al- ways present in a real system, this extra information can be profitably used to …

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