Music is modelled as an ensemble of time-frequency events, in analogy with materials as ensembles of atoms, characterised by the macro-properties of energy and entropy. We define energy as a measure of temporal dissonance or tension, and entropy as a measure of unexpectedness or surprise; two quantities that fluctuate over time and give rise to emotionally perceptible musical contours. The model demonstrates that music-like structures can emerge outside equilibrium, without relying on predefined rules or learned styles. Yet they conform to well-established subjective norms, such as those delineated by the melodic principles of Species Counterpoint. Unlike mainstream AI-generated music, which relies on trained models and probabilistic interpolation, our system is ab initio, operating purely through algebraic transformations in the time-frequency domain, without predefined scales, chords, or rules. This shifts composition from the frequency-time domain to the tension-surprise domain, providing a more direct access to emotional experience. The system contains no trainable parameters, producing entirely novel, unique, and genre-fluid compositions, rather than interpolating between existing musical data; thus, mitigating copyright risks commonly associated with AI music. Beyond offering insight into the nature-inspired mechanisms underlying musical emergence, the system functions as a ‘smart’ instrument, enabling real-time adaptability. This makes it particularly well-suited for applications in EEG-coupled neurofeedback, music therapy, gaming, and interactive media, where dynamic emotional expression is crucial. In contrast to common generative approaches that emphasise structure or style, compositional decisions using this system are made considering instantaneous variations of the emotionally relevant physical analogues. This may offer new tools for researchers and composers seeking expressive means beyond conventional theory or machine-learned imitation.