Mel-Frequency Cepstral Coefficients and Recording Studio Features for the Analysis of Producer-Driven Music

Computational methods for big data music research mostly come from the field of music information retrieval. Through feature extraction and machine learning, many practical tasks have been automated, like genre recognition and playlist generation. However, for musicological purposes, conventional features do not provide enough insight into the music production process. In this study, we evaluate how well Mel-frequency cepstral coefficients and recording studio features reveal aspects of early house and techno music from the United States of America and Germany. The explorative study is an exemplary case-study where music production plays an essential role. Further studies may reveal how much the findings transfer to other producer-driven music, like hip hop and electronic dance music.