This paper describes a system for cross synthesis of rasterised time-domain audio. Rasterisation of the audio allows alignment of the macroscopic features of audio samples of instrument tones prior to principal component analysis (PCA). Specifically a novel algorithm for straightening and aligning rastogram features has been developed which is based on an interactive process incorporating the Canny detection algorithm and variable resampling. Timbral cross-synthesis is achieved by projecting a given instrument tone onto the principal components derived from a training set of sounds for a different tone. The alignment algorithm improves the efficiency of PCA for resynthesizing tones.

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This paper describes methods for the removal and/or separation of amplitude and frequency modulation of individual components within a Fourier spectrum. The first proposed method has a relatively low cost and works under assumptions about the behaviour of both the local and non-local magnitude and phase of sinusoidal components for these two forms of component nonstationarity. The second method is more expensive and resynthesizes components either in the Fourier or time domain following a parameter estimation stage. Typical applications are the adjustment of expressive parameters in music signals and conditioning of signals prior to cross-synthesis.

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A comparison of analysis and resynthesis methods for use with a system for dividing time-coincident stereo audio signals into directional segments is presented. The purpose of such a system is to give greater flexibility in the presentation of spatial information when two-channel audio is reproduced. Example applications include up-mixing and transforming panning from amplitude to time-delay based. Included in the methods are the dualtree complex wavelet transform and wavelet packet decomposition with best basis search. The directional segmentation system and the analysis and resynthesis methods are briefly described, with reference to the relevant underlying theory, figures of merit are presented for each method applied to three stereo mixtures of contrasting material and the subjective quality of the output (with links to all audio examples) is discussed.

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This paper describes a source separation system with the intent to be used in high quality audio post-processing tasks. The system is to be used as the front-end of a larger system capable of modifying the individual sources of existing, two-channel, multi-source recordings. Possible applications include spatial re-configuration such as up-mixing and pan-transformation, re-mixing, source suppression/elimination, source extraction, elaborate filtering, timestretching and pitch-shifting. The system is based on a new implementation of the Matching Pursuit algorithm and uses a known mixing matrix. We compare the results of the proposed system with those from mpd-demix of the ’MPTK’ software package and show that we get similar evaluation scores and in some cases better perceptual scores. We also compare against a segmentation algorithm which is based on the same principles but uses the STFT as the front-end and show that source separation algorithms based on adaptive decomposition schemes tend to give better results. The novelty of this work is a new implementation of the original Matching Pursuit algorithm which adds a pre-processing step into the main sequence of the basic algorithm. The purpose of this step is to perform an analysis on the signal and based on important extracted features (e.g frequency components) create a mini-dictionary comprising atoms that match well with a specific part of the signal, thus leading to focused and more efficient exhaustive searches around centres of energy in the signal.

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