InxGa1−xN films with x=0.06 to x=0.49 were characterized by optical transmittance, Raman, and photoluminescence excitation spectroscopies. Previous microstructural characterizations detected phase separation only in films with x>0.2. The transmittance data suggest that compositional inhomogeneity is also present in the lower-x films (x<0.2). Both Raman and photoluminescence excitation spectra show features that correlate with compositional inhomogeneity and phase separation in the films with x>0.2. The composition dependence of the Raman spectra, from x=0.28 to x=0.49, is consistent with an increase in the size of the phase-separated regions with increasing x.

InGal-xN films with x=0.06 to x=0.49 were characterized by optical transmittance, Raman, and photoluminescence excitation spectroscopies. Previous microstructural characterizations detected phase separation only in films with x>0.2. The transmittance data suggest that compositional inhomogeneity is also present in the lower-x films (x<0.2). Both Raman and photoluminescence excitation spectra show features that correlate with compositional inhomogeneity and phase separation in the films with x>0.2. The composition dependence of the Raman spectra, from x=0.28 to x=0.49, is consistent with an increase in the size of the phase-separated regions with increasing x.

The time–frequency representation (TFR) is the initial stage of analysis in sound/music analysis–resynthesis (A–R) systems. Given a time-domain waveform, the TFR makes temporal and spectral detail available to the remainder of the analysis, so that the component features may be extracted. The resulting ‘feature set’ must represent the sound as completely as the original time-domain signal, if the A–R system is to be capable of effective transformation and good synthesis sound quality. Therefore the system as a whole is reliant upon the TFR to make the sound components detectable, separable and measurable. Yet the standard TFR to-date is the short-time Fourier transform (STFT), of which the shortcomings, in terms of resolution, are well recognised. The purpose of this paper is to demonstrate the importance of the TFR to system function and system design. Poor feature extraction is shown to result from the use of inappropriate TFRs, whose underlying assumptions and expectations do not match those of the system. Existing models are used as case studies, with examples of performance for different sound types. A philosophy for A–R system design that includes TFR design is presented and a methodology for implementing it is proposed.

Analysis–resynthesis (A–R) systems gain their flexibility for creative transformation of sound by representing sound as a set of musically useful features. The analysis process extracts these features from the time domain signal by means of a time–frequency representation (TFR). The TFR provides an intermediate representation of sound that must make the features accessible and measurable to the rest of the analysis. Until very recently, the short-time Fourier transform (STFT) has been the obvious choice for time–frequency representation, despite its limitations in terms of resolution. Recent and ongoing developments are providing several alternative schemes that allow for a more considered choice of TFR. This paper reviews these contemporary approaches in comparison with the more classical ones and with reference to their applicability, merits and shortcomings for application to sound analysis. (Where they have been successfully applied, details are provided.) The techniques reviewed include linear, bilinear and higher-order spectra, nonparametric and parametric methods and some sound-model-specific TFRs.