Spectral analysis for nonstationary audio
This work addresses nonstationary signal analysis for audio applications, representing an incremental improvement over earlier contributions.
The paper tackled the problem of analyzing nonstationary audio signals by modeling them with stationarity-breaking operators and developing an approximate maximum-likelihood approach, resulting in the JEFAS algorithm validated on synthetic and real audio signals.
A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. The focus is on time warping and amplitude modulation, and an approximate maximum-likelihood approach based on suitable approximations in the wavelet transform domain is developed. This paper provides theoretical analysis of the approximations, and introduces JEFAS, a corresponding estimation algorithm. The latter is tested and validated on synthetic as well as real audio signal.