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Chirplet representation for audio signals based on model order selection criteria

In many signal processing applications including Audio and speech processing as well as other research areas of diagnosis of failures in rotating machinery, finding a compact representation of observations signals is often highly desirable. In this respect, chirplets have recently been introduced as an efficient tool for signal representation. However, determining the required number of chirp atoms for the perfect estimation of an observed signal is of great importance. In this research work we study the chirplets signal decomposition to find a compact representation for audio signals of music and speech. The number of chirplets to be used is determined by using the state-of-the-art information criteria. Simulation and experimental results show that by using 4 chirplets are enough for accurate signal representation of music while about 7 to 9 chirplets suffices for speech signal reconstruction. As another application the evaluations are also made on vibration signals. Finally, extracted chirplet toms are employed to reconstruct the observed signals of music and speech. Subjective tests show acceptable results for signal reconstruction.