Studying the Effect of Oral Transmission on Melodic Structure using Online Iterated Singing Experiments

Abstract

Since generations, singing and speech have been mainly transmitted orally. How does oral transmission shape the evolution of music? Here, we developed a method for conducting online transmission experiments, in which sung melodies are passed from one singer to the next. We show that cognitive and motor constraints play a profound role in the emergence of melodic structure. Specifically, initially random tones develop into more structured systems that increasingly reuse and combine fewer elements, making melodies easier to learn and transmit over time. We discuss how our findings are compatible with melodic universals found in most human cultures and culturally specific characteristics of participants’ previous musical exposure. Overall, our method efficiently automates online singing experiments while enabling large-scale data collection using standard computers available to everyone. We see great potential in further extending this work to increase the efficiency, scalability, and diversity of research on cultural evolution and cognitive science.

Core Principles of Melodic Organisation Emerge from Transmission Chains with Random Melodies

“…Finally, hums become less entropic over generations, interpretable as reduced randomness and increase in combinatorial structure -which indeed the emergence of motifs suggests. Reduced entropy is an information-theoretic signature of ILM chains with musical stimuli, previously established for rhythms (Jacoby & McDermott, 2016;Ravignani et al, 2016), and which we now found for the pitch domain also, as have Anglada-Tort et al (2022), whose recent study we now return to.…”

“…Nonetheless -and remarkablymost results across the two studies concord. Like us, Anglada-Tort et al (2022) essentially found that random melodies acquire structure by increasingly reusing and combining fewer elements, making melodies easier to learn and transmit, all of which translates into arched contours and decreasing entropy and copying error. They provide a few additional results, namely that the number of unique intervals and their size decreased, and that the trends described align with the perceived pleasantness of melodic intervals, rated in a different sample.…”

“…Indeed, ILM has been used in music cognition to show for instance that priors on rhythm can be estimated to have high probability for inter-onset-intervals forming small-integer ratios (Jacoby & McDermott, 2016); that a system of whistled signals became speech-like in its combinatoriality (Verhoef et al, 2014); that cadence points in Western folk music tend to lose certain scale degrees through the act of oral transmission (Shanahan & Albrecht, 2019); and that initially random sequences turn into rhythmically structured patterns characterised by similar statistical universals (Ravignani et al, 2016). However, with the exception of a very recent study by Anglada-Tort et al (2022), whose results we return to in the Discussion, ILM has not been used to study how melodic transformations happen through transmission chains .…”

Giving Robots a Voice: Human-in-the-Loop Voice Creation and open-ended Labeling

“…Human-in-theloop methods efficiently integrate human decision-making with computer algorithms, so that a complex computation such as sampling or optimization can be collectively performed by humans and computers [24,69]. Human-in-the-loop techniques have been proposed in the context of mapping internal representation in visual memory [44], 3D pose perception [43], color perception [91], musical rhythm and melody [4,29]. More recently, various human-in-the-loop techniques have been developed in the context of speech, including human-in-the-loop evolutionary algorithms to maximize the emotional content in sound [67,84] and a GUI-based tool allowing users to build a custom TTS voice [38].…”