Extracting Disentangled Representations of Instrumental Sounds for Flexible Music Similarity Assessment

Khái niệm cốt lõi

The proposed method learns a single similarity embedding space with disentangled dimensions, where each subspace represents the similarity focusing on a particular instrument, enabling flexible music similarity assessment.

Tóm tắt

The paper proposes a method to compute music similarities focusing on individual instrumental sounds using a single network that takes mixed sounds as input. The key highlights are:

1. The network is trained using metric learning with triplet loss, where the triplets are constructed from pseudo-mixed pieces to enable learning of disentangled subspaces for each instrument.

2. An auxiliary loss function is used to encourage each subspace to represent the characteristics of the corresponding instrument sound.

3. Experimental results show that the proposed method can obtain more accurate feature representations than using individual networks with separated instrumental sounds. Each subspace holds the characteristics of the assigned instrument, and the learned similarity criteria match human perception, especially for drums and guitar.

4. The proposed approach allows users to select the element they want to focus on when assessing music similarity, enabling a more flexible music information retrieval system.

Thống kê

To achieve a flexible recommendation and retrieval system, it is desirable to calculate music similarity by focusing on multiple partial elements of musical pieces.
Using separated instrumental sounds often results in lower accuracy due to artifacts.
The proposed method can obtain more accurate feature representation than using individual networks using separated sounds as input.
Each sub-embedding space can hold the characteristics of the corresponding instrument.
The selection of similar musical pieces focusing on each instrumental sound by the proposed method can obtain human consent, especially in drums and guitar.

Trích dẫn

"To achieve a flexible recommendation and retrieval system, it is desirable to calculate music similarity by focusing on multiple partial elements of musical pieces and allowing the users to select the element they want to focus on."
"Using separated instrumental sounds alternatively resulted in less accuracy due to artifacts."
"Experimental results have shown that (1) the proposed method can obtain more accurate feature representation than using individual networks using separated sounds as input, (2) each sub-embedding space can hold the characteristics of the corresponding instrument, and (3) the selection of similar musical pieces focusing on each instrumental sound by the proposed method can obtain human consent, especially in drums and guitar."

Thông tin chi tiết chính được chắt lọc từ

Learning Multidimensional Disentangled Representations of Instrumental Sounds for Musical Similarity Assessment

by Yuka Hashizu... lúc arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06682.pdf

Yêu cầu sâu hơn

How can the proposed method be extended to handle music with vocals and incorporate vocal characteristics into the disentangled representation

To extend the proposed method to handle music with vocals and incorporate vocal characteristics into the disentangled representation, a few modifications and additions can be made. One approach could involve training separate networks or subspaces specifically for vocal characteristics. This would entail creating pseudo-mixed pieces that focus on vocals, similar to how it was done for instrumental sounds. By mixing vocal segments from different songs and training the network to learn the similarities and differences in vocal characteristics, the model can disentangle vocal features effectively. Additionally, incorporating techniques from speech domain disentanglement, such as separating speaker identity and noise, could be beneficial in isolating vocal attributes within the representation space. This way, the model can learn to distinguish and represent vocal elements independently, enhancing the overall disentangled representation of music with vocals.

What are the potential limitations of the pseudo-mixed pieces approach, and how could it be further improved to better capture the relationships between different instrumental sounds

The pseudo-mixed pieces approach, while effective in capturing relationships between different instrumental sounds, may have limitations that could be addressed for further improvement. One potential limitation is the reliance on manual selection and mixing of instrumental segments, which may introduce biases or inconsistencies in the training data. To mitigate this, automated or semi-automated methods for creating pseudo-mixed pieces could be developed, ensuring a more diverse and representative dataset for training. Additionally, incorporating data augmentation techniques, such as pitch shifting or time stretching, could enhance the variability and robustness of the pseudo-mixed pieces. Furthermore, exploring the use of generative models to generate synthetic pseudo-mixed pieces based on learned instrumental sound distributions could provide a more scalable and adaptable approach to capturing relationships between different instrumental sounds.

Given the success in capturing instrument-specific similarities, how could this framework be adapted to enable users to explore music based on other high-level musical attributes, such as mood or genre

Building on the success of capturing instrument-specific similarities, the framework can be adapted to enable users to explore music based on other high-level musical attributes, such as mood or genre, by expanding the disentangled representation space. This expansion could involve incorporating additional subspaces or dimensions dedicated to encoding mood, genre, tempo, or other musical attributes. By training the network to disentangle these attributes within the representation space, users can navigate and explore music based on specific criteria of interest. Furthermore, integrating user feedback mechanisms or interactive interfaces that allow users to adjust the focus on different attributes dynamically could enhance the user experience and customization options. This adaptive framework would enable users to explore and discover music based on a wide range of high-level musical attributes, providing a more personalized and engaging music exploration experience.