The evolution of inharmonicity and noisiness in contemporary popular music

Much of Western classical music uses instruments based on acoustic resonance. Such instruments produce harmonic or quasi-harmonic sounds. On the other hand, since the early 1970s, popular music has largely been produced in the recording studio. As a result, popular music is not bound to be based on harmonic or quasi-harmonic sounds. In this study, we use modified MPEG-7 features to explore and characterise the way in which the use of noise and inharmonicity has evolved in popular music since 1961. We set this evolution in the context of other broad categories of music, including Western classical piano music, Western classical orchestral music, and musique concr\`ete. We propose new features that allow us to distinguish between inharmonicity resulting from noise and inharmonicity resulting from interactions between relatively discrete partials. When the history of contemporary popular music is viewed through the lens of these new features, we find that the period since 1961 can be divided into three phases. From 1961 to 1972, there was a steady increase in inharmonicity but no significant increase in noise. From 1972 to 1986, both inharmonicity and noise increased. Then, since 1986, there has been a steady decrease in both inharmonicity and noise to today's popular music which is significantly less noisy but more inharmonic than the music of the sixties. We relate these observed trends to the development of music production practice over the period and illustrate them with focused analyses of certain key artists and tracks.

"Melatonin": A Case Study on AI-induced Musical Style

Although the use of AI tools in music composition and production is steadily increasing, as witnessed by the newly founded AI song contest, analysis of music produced using these tools is still relatively uncommon as a mean to gain insight in the ways AI tools impact music production. In this paper we present a case study of "Melatonin", a song produced by extensive use of BassNet, an AI tool originally designed to generate bass lines. Through analysis of the artists' work flow and song project, we identify style characteristics of the song in relation to the affordances of the tool, highlighting manifestations of style in terms of both idiom and sound.

Auto-adaptive Resonance Equalization using Dilated Residual Networks

In music and audio production, attenuation of spectral resonances is an important step towards a technically correct result. In this paper we present a two-component system to automate the task of resonance equalization. The first component is a dynamic equalizer that automatically detects resonances and offers to attenuate them by a user-specified factor. The second component is a deep neural network that predicts the optimal attenuation factor based on the windowed audio. The network is trained and validated on empirical data gathered from an experiment in which sound engineers choose their preferred attenuation factors for a set of tracks. We test two distinct network architectures for the predictive model and find that a dilated residual network operating directly on the audio signal is on a par with a network architecture that requires a prior audio feature extraction stage. Both architectures predict human-preferred resonance attenuation factors significantly better than a baseline approach.