RESEARCH
RESEARCH
INTERESTS

Audio fingerprinting

Machine learning

Timefrequency analysis and reassignment methods

Bioacoustic signal analysis

Signaladaptive timefrequency methods

Chirplet transforms

Music information retrieval
PH.D. RESEARCH SUMMARY
For my Ph.D. dissertation, I studied reassignment methods for timefrequency analysis, which are nonlinear transformations used to obtain concentrated, precise information in the timefrequency domain. I combined these methods with a novel type of chirplet transform to create a new, signaladaptive transform that can sharply represent signals with fastvarying instantaneous frequencies, and that showed more impressive results with noisy signals than existing methods. I also proved the theoretical accuracy of these methods in the nonnoisy case, and ran sample experiments on synthetic and realworld bioacoustic data to demonstrate their effectiveness.
Why reassignment methods? For linear transformations like Fourier or wavelet transforms, the Fourier uncertainty principle causes a tradeoff between time precision and frequency precision: the more precise information you want in time, the less precise the frequency information becomes, and viceversa. The nonlinearity of reassignment methods circumvents this uncertainty principle, and allows a representation that is simultaneously precise in time and frequency. In audio for instance, this is useful for determining the precise times at which a sound starts and ends, together with its fundamental frequencies. As another example, in the medical field it may be useful for determining precisely when a patient has entered the REM sleep stage.
Why chirplet transforms? Fourier transforms are optimal for representing sinusoids of constant frequency, but in many realworld signals, sinusoidal components have timevarying frequencies. A couple of examples are vocal glissandos in music, or many animal calls in the wild. Chirplet transforms are useful for deblurring the Fourier transform of nonstationary sinusoid signals, including instances when the signal is contaminated with noise.