Discrete wavelet transform (DWT) has gained widespread recognition and popularity in signal processing due to its ability to underline and represent time-varying spectral properties of many transient and other nonstationary signals. However, DWT is a shiftvariant transform. This shift-variance is a major problem with the use of DWT for transient signal analysis and pattern recognition applications. A number of modified forms of DWT have been investigated in recent years that provide approximate shift-invariant transform but at the cost of increased redundancy and complexity. In this paper, a shift-invariant analysis scheme is proposed which is nonredundant. This scheme combines minimum-phase (MP) reconstruction with the DWT so that the resultant scheme provides a shift-invariant transform. The detailed properties of MP signal and different methods to reconstruct it are explained. The proposed scheme can be used for the analysis-synthesis, classification, and compression of transient sound signals.

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The use of high quality sound effects is growing rapidly in multimedia, interactive and virtual reality applications. The common source of audio events in these applications is impact sounds. The sound effects in such environments can be pre-recorded or synthesized in real-time as a result of a physical event. However, one of the biggest problems when using pre-recorded sound effects is the monotonous repetition of these sounds which can be tedious to the listener. In this paper, we present a new algorithm which generates non-repetitive impact sound effects using parameters from the physical interaction. Our approach aims to use audio grains to create finely-controlled synthesized sounds which are based on recordings of impact sounds. The proposed algorithm can also be used in a large set of audio data analysis, representation, and compression applications. A subjective test was carried out to evaluate the perceptual quality of the synthesized sounds.

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