TenseMusic: An automatic prediction model for musical tension.

The perception of tension and release dynamics constitutes one of the essential aspects of music listening. However, modeling musical tension to predict perception of listeners has been a challenge to researchers. Seminal work demonstrated that tension is reported consistently by listeners and can be accurately predicted from a discrete set of musical features, combining them into a weighted sum of slopes reflecting their combined dynamics over time. However, previous modeling approaches lack an automatic pipeline for feature extraction that would make them widely accessible to researchers in the field. Here, we present TenseMusic: an open-source automatic predictive tension model that operates with a musical audio as the only input. Using state-of-the-art music information retrieval (MIR) methods, it automatically extracts a set of six features (i.e., loudness, pitch height, tonal tension, roughness, tempo, and onset frequency) to use as predictors for musical tension. The algorithm was optimized using Lasso regression to best predict behavioral tension ratings collected on 38 Western classical musical pieces. Its performance was then tested by assessing the correlation between the predicted tension and unseen continuous behavioral tension ratings yielding large mean correlations between ratings and predictions approximating r = .60 across all pieces. We hope that providing the research community with this well-validated open-source tool for predicting musical tension will motivate further work in music cognition and contribute to elucidate the neural and cognitive correlates of tension dynamics for various musical genres and cultures.

Asymmetry in scales enhances learning of new musical structures.

Despite the remarkable variability music displays across cultures, certain recurrent musical features motivate the hypothesis that fundamental cognitive principles constrain the way music is produced. One such feature concerns the structure of musical scales. The vast majority of musical cultures use scales that are not uniformly symmetric-that is, scales that contain notes spread unevenly across the octave. Here we present evidence that the structure of musical scales has a substantial impact on how listeners learn new musical systems. Three experiments were conducted to test the hypothesis that nonuniformity facilitates the processing of melodies. Novel melodic stimuli were composed based on artificial grammars using scales with different levels of symmetry. Experiment 1 tested the acquisition of tonal hierarchies and melodic regularities on three different 12-tone equal-tempered scales using a finite-state grammar. Experiments 2 and 3 used more flexible Markov-chain grammars and were designed to generalize the effect to 14-tone and 16-tone equal-tempered scales. The results showed that performance was significantly enhanced by scale structures that specified the tonal space by providing unique intervallic relations between notes. These results suggest that the learning of novel musical systems is modulated by the symmetry of scales, which in turn may explain the prevalence of nonuniform scales across musical cultures.

Cortical encoding of melodic expectations in human temporal cortex.

Humans engagement in music rests on underlying elements such as the listeners' cultural background and interest in music. These factors modulate how listeners anticipate musical events, a process inducing instantaneous neural responses as the music confronts these expectations. Measuring such neural correlates would represent a direct window into high-level brain processing. Here we recorded cortical signals as participants listened to Bach melodies. We assessed the relative contributions of acoustic versus melodic components of the music to the neural signal. Melodic features included information on pitch progressions and their tempo, which were extracted from a predictive model of musical structure based on Markov chains. We related the music to brain activity with temporal response functions demonstrating, for the first time, distinct cortical encoding of pitch and note-onset expectations during naturalistic music listening. This encoding was most pronounced at response latencies up to 350 ms, and in both planum temporale and Heschl's gyrus.

Prior context in audition informs binding and shapes simple features.

A perceptual phenomenon is reported, whereby prior acoustic context has a large, rapid and long-lasting effect on a basic auditory judgement. Pairs of tones were devised to include ambiguous transitions between frequency components, such that listeners were equally likely to report an upward or downward 'pitch' shift between tones. We show that presenting context tones before the ambiguous pair almost fully determines the perceived direction of shift. The context effect generalizes to a wide range of temporal and spectral scales, encompassing the characteristics of most realistic auditory scenes. Magnetoencephalographic recordings show that a relative reduction in neural responsivity is correlated to the behavioural effect. Finally, a computational model reproduces behavioural results, by implementing a simple constraint of continuity for binding successive sounds in a probabilistic manner. Contextual processing, mediated by ubiquitous neural mechanisms such as adaptation, may be crucial to track complex sound sources over time.