Sines + Noise + Transients Models

As we have seen, sinusoids efficiently model spectral peaks over time, and filtered noise efficiently models the spectral residual left over after pulling out everything we want to call a ``tonal component'' characterized by a spectral peak the evolves over time. However, neither is good for abrupt transients in a waveform. At transients, one may retain the original waveform or some compressed version of it (e.g., MPEG-2 AAC with short window [149]). Alternatively, one may switch to a transient model during transients. Transient models have included wavelet expansion [6] and frequency-domain LPC (time-domain amplitude envelope) [290].

In either case, a reliable transient detector is needed. This can raise deep questions regarding what a transient really is; for example, not everyone will notice every transient as a transient, and so perceptual modeling gets involved. Missing a transient, e.g., in a ride-cymbal analysis, can create highly audible artifacts when processing heavily based on transient decisions. For greater robustness, hybrid schemes can be devised in which a continuous measure of ``transientness'' $ {\cal T}$ can be defined between 0 and 1, say.

Also in either case, the sinusoidal model needs phase matching when switching to or from a transient frame over time (or cross-fading can be used, or both). Given sufficiently many sinusoids, phase-matching at the switching time should be sufficient without cross-fading.

Sines+Noise+Transients Time-Frequency Maps

Figure 10.13 shows the multiresolution time-frequency map used in the original S+N+T system [149]. (Recall the fixed-resolution STFT time-frequency map in Fig.7.1.) Vertical line spacing in the time-frequency map indicates the time resolution of the underlying multiresolution STFT,11.11 and the horizontal line spacing indicates its frequency resolution. The time waveform appears below the time-frequency map. For transients, an interval of data including the transient is simply encoded using MPEG-2 AAC. The transient-time in Fig.10.13 extends from approximately 47 to 115 ms. (This interval can be tighter, as discussed further below.) Between transients, the signal model consists of sines+noise below 5 kHz and amplitude-modulated noise above. The spectrum from 0 to 5 kHz is divided into three octaves (``multiresolution sinusoidal modeling''). The time step-size varies from 25 ms in the low-frequency band (where the frequency resolution is highest), down to 6 ms in the third octave (where frequency resolution is four times lower). In the 0-5 kHz band, sines+noise modeling is carried out. Above 5 kHz, noise substitution is performed, as discussed further below.

Figure 10.13: S+N+T Time-Frequency Map (from [149]).
\includegraphics[width=0.9\twidth]{eps/scottl-tf-aac}

Figure 10.14 shows a similar map in which the transient interval depends on frequency. This enables a tighter interval enclosing the transient, and follows audio perception more closely (see Appendix E).

Figure 10.14: Quasi-Constant-Q (Wavelet) Time-Frequency Map [149].
\includegraphics[width=0.9\twidth]{eps/scottl-tf-smooth}


Sines+Noise+Transients Noise Model

Figure 10.15 illustrates the nature of the noise modeling used in [149]. The energy in each Bark band11.12 is summed, and this is used as the gain for the noise in that band at that frame time.

Figure 10.15: Bark-band noise modeling (from [149]).
\includegraphics[width=0.9\twidth]{eps/scottl-bark-noise}

Figure 10.16 shows the frame gain versus time for a particular Bark band (top) and the piecewise linear envelope made from it (bottom). As illustrated in Figures 10.13 and 10.14, the step size for all of the Bark bands above 5 kHz is approximately 3 ms.

Figure 10.16: Amplitude envelope for one noise band (from [149]).
\includegraphics[width=0.9\twidth]{eps/scottl-noise-env}


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S+N+T Sound Examples
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Sines + Noise Modeling