An analysis of chord progressions in 680,000 songs reveals common patterns and some surprising trends. The most frequent progressions are simple, diatonic, and often found in popular music across genres. While major chords and I-IV-V-I progressions dominate, the data also highlights the prevalence of the vi chord and less common progressions like the "Axis" progression. The study categorized progressions by "families," revealing how variations on a core progression create distinct musical styles. Interestingly, chord progressions appear to be getting simpler over time, possibly influenced by changing musical tastes and production techniques. Ultimately, while common progressions are prevalent, there's still significant diversity in how artists utilize harmony.
In a comprehensive study encompassing a vast dataset of 680,000 songs extracted from the Hooktheory website, the author embarked on a meticulous analysis of chord progressions, aiming to uncover prevailing patterns and gain insights into the harmonic landscape of popular music. Utilizing a Markov chain model, the author represented musical transitions between chords as probabilities, effectively creating a map of harmonic movement within the analyzed songs. This model not only captured the likelihood of moving from one specific chord to another but also accounted for the broader harmonic context by considering the preceding chord as well. This approach allowed for the identification of common progressions and a deeper understanding of how harmonic sequences unfold in real-world musical compositions.
The author's analysis delved into several key areas. First, they investigated the most frequently occurring chord progressions, unveiling the prevalence of certain harmonic patterns in popular music. This involved quantifying the occurrence of specific chord transitions and identifying statistically significant progressions that appear with greater frequency than expected by chance. Secondly, the study explored the concept of "harmonic distance," which describes the perceived difference or similarity between two chords. By examining the relationship between harmonic distance and transition probabilities, the author aimed to determine whether closely related chords, in terms of their harmonic properties, are more likely to follow each other in musical sequences. Thirdly, the author examined the distribution of chords within the dataset, shedding light on the relative prevalence of major and minor chords and providing insight into the overall tonal character of the analyzed music. Furthermore, the research considered the influence of musical genre on chord progressions, exploring whether certain harmonic patterns are more characteristic of specific genres, thus contributing to their unique sonic identities. The findings were presented using visualizations, including network diagrams, to illustrate the interconnectedness of chords and the flow of harmonic movement within the analyzed musical corpus. This visual representation offered an intuitive way to grasp the complex relationships between chords and understand the underlying harmonic principles governing musical composition in a large-scale dataset.
Summary of Comments ( 100 )
https://news.ycombinator.com/item?id=43723020
HN users generally praised the analysis and methodology of the original article, particularly its focus on transitions between chords rather than individual chord frequency. Some questioned the dataset's limitations, wondering about the potential biases introduced by including only songs with available chord data, and the skewed representation towards Western music. The discussion also explored the subjectivity of music theory, with commenters highlighting the difficulty of definitively labeling certain chord functions (like tonic or dominant) and the potential for cultural variations in musical perception. Several commenters shared their own musical insights, referencing related analyses and discussing the interplay of theory and practice in composition. One compelling comment thread delved into the limitations of Markov chain analysis for capturing long-range musical structure and the potential of higher-order Markov models or recurrent neural networks for more nuanced understanding.
The Hacker News post titled "I analyzed chord progressions in 680k songs" sparked a discussion with several interesting comments. Many users engaged with the methodology and findings presented in the linked article.
A recurring theme in the comments is the challenge of accurately extracting chord progressions from audio. Several users pointed out the difficulties in distinguishing between different inversions of the same chord, and the potential for errors in automatic chord recognition software. One commenter highlighted the issue of key modulation within a song, suggesting it could skew the analysis if not handled properly. Another user questioned the reliability of the dataset itself, wondering about the source of the chord progressions and the potential for biases in the selection of songs.
Some commenters expressed skepticism about the novelty of the findings. One user argued that the prevalence of common chord progressions is well-established in music theory, and the analysis simply confirms what musicians already know. Another commenter suggested that the focus on chord progressions alone overlooks other important aspects of music, such as melody, rhythm, and timbre.
Despite these criticisms, several commenters found the analysis intriguing. One user appreciated the visualization of the chord progression network, finding it a helpful way to understand the relationships between different chords. Another user expressed interest in exploring the dataset further, suggesting potential applications for music generation and analysis. A commenter also raised the question of cultural influences on chord progressions, wondering if certain progressions are more common in specific genres or regions.
Several users discussed the limitations of using only harmonic information to analyze music. They pointed out that melody, rhythm, and instrumentation play crucial roles in a song's overall impact. One commenter argued that while common chord progressions might be prevalent, they can be used in vastly different ways to create unique musical experiences.
A few commenters also shared their own experiences with music analysis and composition. One user mentioned using Markov chains to generate melodies, while another discussed the importance of understanding music theory for aspiring composers. These comments added a personal touch to the discussion and highlighted the practical applications of music analysis.