Volume 109, Issue 3, March 2001
Index of content:
- ACOUSTIC SIGNAL PROCESSING 
109(2001); http://dx.doi.org/10.1121/1.1349536View Description Hide Description
The problem of detecting a source in shallow water is addressed. The complexity of such a propagation channel makes precise modeling practically impossible. This lack of accuracy causes a deterioration in the performance of the optimal detector and motivates the search for suboptimal detectors which are insensitive to uncertainties in the propagation model. A novel, robust detector which measures the degree of spatial stationarity of a received field is presented. It exploits the fact that a signal propagating in a bounded channel induces spatial nonstationarity to a higher degree than mere background noise. The performance of the proposed detector is evaluated using both simulated data and experimental data collected in the Mediterranean Sea. This performance is compared to those of three other detectors, employing different extents of prior information. It is shown that when the propagation channel is not completely known, as is the case of the experimental data, the novel detector outperforms the others in terms of threshold signal-to-noise ratio (SNR). In the presence of environmental mismatch, the threshold SNR of the novel detector for the experimental data appears 2–5 dB lower than the other detectors. That is, this detector couples good performance with robustness to propagation uncertainties.
109(2001); http://dx.doi.org/10.1121/1.1342075View Description Hide Description
The automatic identification of musical instruments is a relatively unexplored and potentially very important field for its promise to free humans from time-consuming searches on the Internet and indexing of audio material. Speaker identification techniques have been used in this paper to determine the properties (features) which are most effective in identifying a statistically significant number of sounds representing four classes of musical instruments(oboe, sax, clarinet,flute) excerpted from actual performances. Features examined include cepstral coefficients, constant-Q coefficients, spectral centroid, autocorrelation coefficients, and moments of the time wave. The number of these coefficients was varied, and in the case of cepstral coefficients, ten coefficients were sufficient for identification. Correct identifications of 79%–84% were obtained with cepstral coefficients, bin-to-bin differences of the constant-Q coefficients, and autocorrelation coefficients; the latter have not been used previously in either speaker or instrument identification work. These results depended on the training sounds chosen and the number of clusters used in the calculation. Comparison to a human perception experiment with sounds produced by the same instruments indicates that, under these conditions, computers do as well as humans in identifying woodwind instruments.