Recently, new methods for smart decomposition of time-frequency representations of audio have been proposed in order to address the problem of blind automatic music transcription. However those techniques are not necessarily suitable for notes having variations of both pitch and spectral envelope over time. The HALCA (Harmonic Adaptive Latent Component Analysis) model presented in this article allows considering those two kinds of variations simultaneously. Each note in a constant-Q transform is locally modeled as a weighted sum of fixed narrowband harmonic spectra, spectrally convolved with some impulse that defines the pitch. All parameters are estimated by means of the expectation-maximization (EM) algorithm, in the framework of Probabilistic Latent Component Analysis. Interesting priors over the parameters are also introduced in order to help the EM algorithm converging towards a meaningful solution. We applied this model for automatic music transcription: the onset time, duration and pitch of each note in an audio file are inferred from the estimated parameters. The system has been evaluated on two different databases and obtains very promising results.