Detecting Performance Anomalies: A Novel Method for Analyzing Musical Differences in WAV and MIDI Data

In this study, we propose a new method for extracting differences between songs that takes advantage of the strengths of WAV and MIDI data. The proposed method converts acoustic signals into mel-spectrogram images and extracts differences by applying anomaly detection techniques. Although studies have been conducted to detect similarities between musical pieces, none have been conducted to identify differences in the musical performances of different pieces. In a verification experiment, we recorded 100 piano performances of a single piece of music for piano and compared them with a model performance to see if intentional mistakes could be detected. The results revealed an accuracy of 93.6% or higher in correctly identifying discrepancies The proposed method is compatible with traditional methods of instrumental performance instruction and proves to be more adaptable to the teaching field compared to evaluating performance using an absolute scale.