Deep Learning Models for Analyzing the Dual Impact of Meshing and Quantization Techniques on Musical Inspiration in DAWs

The Digital Audio Workstations (DAWs) provide devices like quantizing and alignment of tracks, which enhance precision in recording music by improving the technical quality. Nevertheless, a critical timing correction and strict structural alignment can impact the musical inspiration and creative expression. This paper explores how meshing and quantization methods jointly influence musical inspiration perception based on deep-learned musical input in the form of symbolic music data. An experimental structure was used with a twelve-week design that included MIDI-based musical excerpts that were manipulated according to four conditions, which included the original performance, hard quantization, soft quantization and humanized timing. Features of timing deviation, velocity variation and inter track alignment were analyzed by use of sequence-based deep learning models. The quantitative data indicated that there were large variations in the measures of inspiration between the conditions (F = 16.4238.91, p ≤ 3.6 × 107 -3). Hard and soft quantization revealed a moderate and negative impact on perceived creativity (d = 0.58) and strong positive impacts on inspiration (d = 0.81–0.94) respectively under medium conditions, respectively. Self-report assessments of engagement, naturalness, and creative motivation were greater in humanized and softly quantized instances with these being correlated with expressive timing variability at the positive level (r = 0.63 -0.77). Qualitative feedback also indicated more musical flow, less creative pressure and more emotional connection. Although there are certain limitations associated with the sample size and the mix of genera, the results prove the usefulness of deep learning in the analysis of the creativity-based results in the music production with the help of the DAW and outline the necessity to balance the technical accuracy with the human expressiveness.