ENERGY SEPARATION IN SIGNAL MODULATIONS WITH APPLICATION TO SPEECH ANALYSIS

Oscillatory signals that have both an amplitude-modulation (AM) and a frequency-modulation (FM) structure are encountered in almost all communication systems. We have also used these structures recently for modeling speech resonances, being motivated by previous work on investigating fluid dynamics phenomena during speech production that provide evidence for the existence of modulations in speech signals. In this paper, we use a nonlinear differential operator that can detect modulations in AM-FM signals by estimating the product of their time-varying amplitude and frequency. This operator essentially tracks the energy needed by a source to produce the oscillatory signal. To solve the fundamental problem of estimating both the amplitude envelope and instantaneous frequency of an AM-FM signal we develop a novel approach that uses nonlinear combinations of instantaneous signal outputs from the energy operator to separate its output energy product into its amplitude modulation and frequency modulation components. The theoretical analysis is done first for continuous-time signals. Then several efficient algorithms are developed and compared for estimating the amplitude envelope and instantaneous frequency of discrete-time AM-FM signals. These energy separation algorithms are then applied to search for modulations in speech resonances, which we model using AM-FM signals to account for time-varying amplitude envelopes and instantaneous frequencies. Our experimental results provide evidence that bandpass filtered speech signals around speech formants contain amplitude and frequency modulations within a pitch period. Overall, the energy separation algorithms, due to their very low computational complexity and instantaneously-adapting nature, are very useful in detecting modulation patterns in speech and other time-varying signals.