ABSTRACT
Background:
Music therapy is a promising complementary intervention for addressing various
mental health conditions. Despite evidence of the beneficial effects of
music, the
acoustic features that make
music effective in
therapeutic contexts remain elusive.
Aims:
This study aimed to identify and validate distinctive
acoustic features of healing
music.
Methods:
We constructed a healing
music dataset (HMD) based on nominations from related professionals and extracted 370
acoustic features. Healing-distinctive
acoustic features were identified as those that were (1) independent from genre within the HMD, (2) significantly different from
music pieces in a
classical music dataset (CMD) and (3)
similar to pieces in a five-
element music dataset (FEMD). We validated the identified features by comparing jazz pieces in the HMD with a
jazz music dataset (JMD). We also examined the emotional properties of the features in a
Chinese affective
music system (CAMS).
Results:
The HMD comprised 165 pieces. Among all the
acoustic features, 74.59%
shared commonalities across genres, and 26.22% significantly differed between the HMD classical pieces and the CMD. The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46% of the features. The potential healing-distinctive
acoustic features were identified as the standard deviation of the roughness, mean and period
entropy of the third coefficient of the mel-frequency cepstral coefficients. In a three-dimensional space defined by these features, HMD's jazz pieces could be distinguished from those of the JMD. These three features could significantly predict both subjective valence and
arousal ratings in the CAMS.
Conclusions:
The distinctive
acoustic features of healing
music that have been identified and validated in this study have implications for the development of
artificial intelligence models for identifying
therapeutic music, particularly in contexts where access to professional expertise may be limited. This study contributes to the growing body of
research exploring the potential of
digital technologies for
healthcare interventions.