Generating Emotion-Driven Melody Harmonizations by Modeling Musical Keys and Melodic Variation

Основні поняття

The proposed functional representation for symbolic music, which encodes melody notes and chords using Roman numerals relative to musical keys, enables effective modeling of musical keys and generates diverse harmonies to convey desired emotional valence.

Анотація

The paper proposes a novel functional representation for symbolic music that models musical keys explicitly, in contrast to existing approaches that rely on note pitch values and chord names. The functional representation uses Roman numerals to denote melody notes and chords relative to the musical key, allowing for key-aware harmonization and melodic variation.

The key highlights of the paper are:

1. The functional representation empowers the model to learn the relationships between notes, chords, and musical scales (major or minor) more effectively compared to existing representations. It also addresses the data scarcity issue by enabling the representation of melodies across various keys using the same set of symbols.

2. The paper employs a Transformer-based framework to harmonize key-adaptable melodies, where the key can be determined in a rule-based or model-based manner, driven by the target emotional valence (positive or negative).

3. Objective evaluations confirm the functional representation's effectiveness in modeling musical keys and generating satisfactory harmonizations. Subjective evaluations with human listeners demonstrate the ability of the proposed approach to convey desired emotional valence through melodic variation and key-aware harmonies.

4. The paper explores two research questions: (1) Can the proposed representation effectively model musical keys and yield satisfactory harmonization outcomes? (2) Is it possible to generate different music variants from a single melody to influence the perceived valence?

The findings suggest that the functional representation outperforms existing approaches in emotion controllability, and that rule-based key determination performs better than model-based methods in this task.

Статистика

The paper uses two datasets for the experiments:

HookTheory dataset: 18,206 lead sheet segments with high-quality melody, chord, and key annotations.
EMOPIA dataset: 1,071 piano music clips with human-annotated emotion labels.

Цитати

"Valence is often found to be related to major-minor tonality [14], and keys play important roles in affecting such tonality."
"Our representation also supports transposition between parallel keys1 for any music pieces. Specifically, when transposing to a parallel key, the scale degrees of notes and chords remain consistent, while pitches may be adjusted to reflect the new key mode, i.e., melodic variation."

Ключові висновки, отримані з

Emotion-Driven Melody Harmonization via Melodic Variation and Functional Representation

by Jingyue Huan... о arxiv.org 09-26-2024

https://arxiv.org/pdf/2407.20176.pdf

Глибші Запити

How can the model-based key determination approach be improved to better match the perceived emotional valence?

The model-based key determination approach can be enhanced by integrating a more sophisticated emotional analysis framework that considers the nuances of musical expression. One potential improvement is to employ a multi-layered neural network that not only predicts the key based on the emotional condition but also incorporates contextual features from the melody and historical data of similar compositions. This could involve training the model on a larger dataset that includes a diverse range of emotional expressions across various keys, allowing it to learn the intricate relationships between specific keys and their emotional connotations. Additionally, incorporating reinforcement learning techniques could enable the model to iteratively refine its key predictions based on feedback from generated outputs, thus aligning more closely with the desired emotional valence. Furthermore, integrating user feedback mechanisms could provide real-time adjustments to the key selection process, ensuring that the generated harmonies resonate more effectively with listeners' emotional perceptions.

What other musical attributes, beyond keys and chords, could be leveraged to further enhance the controllability of emotion in melody harmonization?

Beyond keys and chords, several other musical attributes can be leveraged to enhance emotional controllability in melody harmonization. One significant attribute is rhythm, as variations in tempo and rhythmic patterns can profoundly influence the emotional impact of a piece. For instance, faster tempos often convey excitement or joy, while slower tempos can evoke sadness or introspection. Additionally, dynamics—the variations in loudness—can be manipulated to emphasize emotional peaks and valleys within the music.
Another attribute is timbre, which refers to the quality of sound that distinguishes different types of sound production, such as instruments or voices. By varying timbre, composers can evoke different emotional responses; for example, a warm, rich timbre may elicit feelings of comfort, while a harsh, bright timbre might provoke tension or unease.
Moreover, melodic contour—the shape of the melody as it rises and falls—can also be a powerful tool for emotional expression. Melodies that ascend may convey hope or joy, while descending melodies can suggest sadness or resignation. Finally, incorporating textural elements, such as the density of musical layers or the interplay between instruments, can further enrich the emotional landscape of a piece, allowing for a more nuanced and controlled emotional expression in melody harmonization.

Can the proposed functional representation be extended to other music generation tasks, such as unconditional music composition or multi-instrument arrangement, to explore its broader applicability?

Yes, the proposed functional representation can indeed be extended to other music generation tasks, including unconditional music composition and multi-instrument arrangement. The functional representation's key feature is its ability to encode musical elements in a way that is independent of specific pitches, focusing instead on the relationships between notes, chords, and scales. This flexibility makes it suitable for various musical contexts.
In unconditional music composition, the functional representation can facilitate the generation of melodies and harmonies that adhere to established musical rules and structures, allowing for the creation of coherent and emotionally resonant pieces without predefined emotional conditions. By leveraging the same principles of key awareness and functional harmony, composers can explore a wider range of musical ideas and styles.
For multi-instrument arrangements, the functional representation can be adapted to include additional layers of information, such as instrument timbres and roles within the arrangement. By representing each instrument's part in relation to the overall harmonic structure, the model can generate complex arrangements that maintain harmonic coherence while allowing for rich textural variations. This approach could also incorporate real-time adjustments based on performance dynamics, further enhancing the expressiveness of the generated music.
Overall, the functional representation's adaptability and focus on musical relationships position it as a valuable tool for a variety of music generation tasks, paving the way for innovative explorations in both composition and arrangement.