  We currently lack good waveform models for many gravitational wave sources.
Examples where models are lacking include neutron star post merger signals,
core collapse supernovae, and signals of unknown origin. Wavelet based
techniques have proven effective at detecting and characterizing these signals.
Here we introduce a new method that uses collections of evolving
amplitude-frequency tracks, or "voices", to model generic gravitational wave
signals. The analysis is implemented using trans-dimensional Bayesian
inference, building on the earlier wavelet-based BayesWave algorithm. The new
algorithm, BayesWaveVoices, outperforms the original for long duration signals.
