Tuesday afternoon, 11 November - The Acoustical Society of America

not random; rather, they systematically reflect the phonetic implementation
of each tonal target. For example, the peak is in the final portion of a syllable
in R, and it takes more time for the peak to be fully realized. This
effect can be clearly observed when the following tone is weak.
2pSC27. Investigating the influence of context frequency on lexical tone
perception. Jingyuan Huang and Lori Holt Dept. of Psych., Carnegie Mellon
Univ., 5000 Forbes Ave., Pittsburgh, PA 15232, jingyuan@andrew.cmu
.edu
Tone languages such as Mandarin use pitch variations to contrast
meaning. Within tone languages, large variability exists in the pitch of tones
produced by different speakers. However, previous studies of speaker normalization
for contour tones have produced inconsistent results; whether
speakers rely on context information in tone perception is unclear. The
present study intended to provide an unambiguous test of the effect of context
on contour lexical tone perception and to explore its underlying mechanisms
and sources of information. In four experiments, Mandarin listeners’
perceptions of Mandarin first and second level and rising tones were investigated
with preceding speech and nonspeech contexts. Results indicate
that 1 the mean fundamental frequency f0 of a preceding sentence affects
the perception of contour lexical tones and the effect is spectrally
contrastive: Following a sentence with a higher-frequency mean f0, a following
word is more likely to be perceived as a low-frequency tone and vice
versa; 2 nonspeech precursors also elicit this effect, suggesting general
perceptual rather than articulatory-based mechanisms; 3 listeners can use
information from both fundamental frequency and periodicity to normalize
tone perception. Work supported by NIH NIDCD 2 R01DC004674-04A2.
TUESDAY AFTERNOON, 11 NOVEMBER 2008 LEGENDS 12, 1:55 TO 4:30 P.M.
Session 2pSP
Signal Processing in Acoustics and Underwater Acoustics: Signal Processing for High Clutter Environments
Ronald A. Wagstaff, Cochair
Univ. of Mississippi, Natl. Ctr. for Physical Acoustics, 1 Coliseum Dr., University, MS 38677
Joal Newcomb, Cochair
NAVOCEANO, 1002 Balch Blvd., Stennis Space Center, MS 39522-5001
Chair’s Introduction—1:55
Invited Papers
2:00
2pSP1. Signal processor for detection of signals in cluttered environments. Ronald Wagstaff and Heath Rice NCPA, Univ. of Mississippi,
1 Coliseum Dr., University, MS 38677, rwagstaf@olemiss.edu
The well-publicized experience of the USS Cole in a foreign port demonstrates the potential danger our military ships are frequently
exposed to. When docked, they are the most vulnerable to attack by small fast boats that can hit and run before their threat is recognized,
and defenses can be activated. This is a challenge that has many facets. Attack by a small high-powered fast-boat is just one type of
threat. However, it is important. One way of quickly identifying a fast-boat is with an underwater acoustic sensing system. Such a
system is not without challenges. Harbors are typically busy and contaminated by many forms of acoustic clutter. Detecting and separating
the signals of fast-boats from among the clutter are difficult tasks. Fortunately, a signal processor has been developed with highly
coherent fast-boat signals, and harbor clutter in mind. This fast-boat processor invokes temporal coherence constraints, by degree, to
strip away incoherent noise and less coherent shipping signals, and leaves the fast-boat signals exposed. This signal processor will be
discussed, and the results will be presented to illustrate the substantial signal-to-noise ratio and automatic signal detection gains that can
be achieved.
2:20
2pSP2. Human detection algorithm for seismic and ultrasonic detectors. Alexander E. Ekimov and James M. Sabatier NCPA, Univ.
of Mississippi, 1 Coliseum Dr., University, MS 38677
Range detection methodologies for human acoustic signals are discussed. Seismic, passive, and active Doppler ultrasonic sensors are
used in the presented methods. The algorithm developed for recognition of human’s presence in the measured signals is based on detection
of the specific acoustic signatures resulting from specific human motion characteristics. These signatures have two characteristic
times, T1 the footstep repetition time, which is equal to the time of the whole body periodic motion and T2 the footstep duration
time. The footstep duration time is equal to the time interval for a single footstep from “heel strike” to “toe slap and weight transfer.”
Taking advantage of these times in signal processing for optimization of the signal-to-noise ratio and applying a procedure of cadence
frequency detection allow recognition of human presence in the analyzed signals. This algorithm was tested and it was experimentally
demonstrated that cadence frequencies for walking human and their harmonics for seismic and ultrasonic signatures passive and active
Doppler signatures were the same. Cadence frequencies were stable and had no detectable variation in time for a regular walking style.
This stability feature potentially may be used for human tracking. Work supported by the Department of the Army, Army Research
Office, Contract No. W911NF-04-1-0190.
2499 J. Acoust. Soc. Am., Vol. 124, No. 4, Pt. 2, October 2008 156th Meeting: Acoustical Society of America
2499
2p TUE. PM