## 4 Discussion
For automatic music analysis, Kohonen neural network models are popular for recognizing chords and tonality in music recordings [1; 49; 50; 51; 52], however they have been lacking the temporal information necessary for modeling novelty in music. Therefore, the recent applications of nearby contextual information in time [53], Hidden Markov Models (HMM) and the Viterbi algorithms [54] seem relevant, as these models are able to integrate the transitions from preceding musical chords in the chord recognition. Also, algorithms including memory in modeling of dissonance perception have been developed, showing improved performance in predicting dissonance perceived by listeners when including a memory for the preceding sound material [55]. Such approaches could be useful for automatically identifying time points with novel events in music. Future studies might in addition continue investigating the usage of biologically-inspired neural network models with time integration and memory mechanisms, such as simple recurrent short-term memory models [56], hippocampus-inspired memory models facilitating temporary storage and retrieval for new stimulus patterns [57], or frontal cortex inspired working-memory neural network models enabling selective update-maintenance-retrieval procedures [58] (e.g., which are all freely available in the open source software Emergent [59]). An additional topic worth to consider with respect to the measurement of brain responses is the attack of the acoustical events in the musical pieces, i.e., the rise in sound amplitude after the initial onset of a sound. The attack is dependent on the instruments involved; for example, a bass drum has a rapid attack shorter than 10 ms, while violins and flutes have relatively slower attacks in the range of 100 ms [60], whereas the MoRI measure was here optimized to measure magnitudes of increases fixed in relation to 50 ms attack curves. The attack can also be modified with music production, altering the acceleration of the rapid increase in acoustical features without modifying the composition. A complication resulting from this issue is that the perception of the rhythmic onset of a sound event in relation to other sound events (called the 'perceived attack time') is modulated by the acoustical characteristics of the attack [60]. For example, the slow attack of string instruments, and the initial anharmonic noise and delayed high spectral energy in certain wind or brass instruments, causes a delayed perception of the rhythmic onset in relation to the initial rise in sound amplitude (e.g., strings or saxophones are perceived as rhythmically delayed in relation to a bass drum, even though their attack curves show identical onset times) [60]. For instance, the Spectral Flux measure applied in the current study, which in the MIR Toolbox measures changes in loudness, timbre and pitch simultaneously (by summarizing the level of energy change across all spectral frequency bands), might be relevant for capturing the perceived rhythmic onsets in recordings with low dynamic range or containing wind instruments with delayed high spectral energy [2]. As an example of this, the bass drum, keyboard, guitar and bass with distortion at the novel loudness increase at 20.316 s. in the metal rock piece appears to have a faster attack compared to the strings playing the novel chord at 198.646 s. in the _Rite of Spring_ piece. However, it remains to be investigated in future studies whether the latencies of early cortical responses are related to the perceived sound onset, or they relate to a delayed perception of the rhythmic onset influenced by the attack characteristics of recorded music (e.g., see Reference [16]).