Journal: Frontiers in Neuroscience

Article Title: Atonal Music: Can Uncertainty Lead to Pleasure?

doi: 10.3389/fnins.2018.00979

Figure Lengend Snippet: Single piece analysis of features (key and pulse clarity) by means of automated extraction. This figure displays the results of the analysis of single pieces with MIR Toolbox for Matlab . Red lines represent classical pieces, blue lines atonal pieces. In (A) results for key clarity are displayed, using a window size of 5 s and a hop factor of 33%. Even though at some points the values of modern pieces lie above classical piece values, classical pieces tend to obtain higher values over time (compare Table ). In order to demonstrate the scope of key clarity that the algorithm is able to reveal we included a C Major chord (yellow) and white noise (green) into our analysis as reference points. Even though the representation particularly of the white noise indicating peaks and lows extracted from a constant signal can be seen as critical, it gives a reference of how to evaluate the key-related feature extraction of the pieces (see Table for absolute and mean values of key clarity). In case of pulse clarity (B) , classical pieces obtain much higher values than atonal pieces and results are more significant. However, the Chopin piece shows lower values, which can be explained by the rubato interpretation of the musician, which generates slight changes in tempo at formal borders within a piece (e.g., “ritardandos” = deceleration). Particularly, at around 10 s, a drop of pulse clarity can be seen which is reflecting the deceleration of the pianist at the end of the first presentation of the main motif.

Article Snippet: While we use music theoretical analyses to give a functional analysis as well as to provide a visual illustration of the scores of the musical excerpts, the analyses conducted with the MIR (music information retrieval) toolbox for Matlab ( ) have the purpose to calculate particular features both in single pieces and in a corpus analysis.

Techniques: Extraction

Journal: Frontiers in Neuroscience

Article Title: Atonal Music: Can Uncertainty Lead to Pleasure?

doi: 10.3389/fnins.2018.00979

Figure Lengend Snippet: Corpus analysis of 100 piano pieces. Figure shows the mean key and pulse clarity values for each musical style, extracted with MIR Toolbox for Matlab . Two independent-samples t -tests were conducted to compare the two corpora (AM = atonal music; TM = tonal music). There was a significant difference in key clarity values for atonal ( M = 0.5, SD = 0.1) and tonal ( M = 0.8, SD = 0.1) excerpts; t (98) = 15, p < 0.0001; Effect size d Cohen = 3; 95% CI (2.19/3.81). Similarly, there was a significant difference in pulse clarity values for atonal ( M = 0.2, SD = 0.1) and tonal ( M = 0.4, SD = 0.1) excerpts; t (98) = 10, p < 0.0001; Effect size d Cohen = 2; 95% CI (1.32/2.68). The results represent the striven manifestation of features and demonstrate the reliability of this computational approach.

Article Snippet: While we use music theoretical analyses to give a functional analysis as well as to provide a visual illustration of the scores of the musical excerpts, the analyses conducted with the MIR (music information retrieval) toolbox for Matlab ( ) have the purpose to calculate particular features both in single pieces and in a corpus analysis.

Techniques: