Audio embeddings are crucial tools in understanding large catalogs of music.
Typically embeddings are evaluated on the basis of the performance they provide
in a wide range of downstream tasks, however few studies have investigated the
local properties of the embedding spaces themselves which are important in
nearest neighbor algorithms, commonly used in music search and recommendation.
In this work we show that when learning audio representations on music datasets
via contrastive learning, musical properties that are typically homogeneous
within a track (e.g., key and tempo) are reflected in the locality of
neighborhoods in the resulting embedding space. By applying appropriate data
augmentation strategies, localisation of such properties can not only be
reduced but the localisation of other attributes is increased. For example,
locality of features such as pitch and tempo that are less relevant to
non-expert listeners, may be mitigated while improving the locality of more
salient features such as genre and mood, achieving state-of-the-art performance
in nearest neighbor retrieval accuracy. Similarly, we show that the optimal
selection of data augmentation strategies for contrastive learning of music
audio embeddings is dependent on the downstream task, highlighting this as an
important embedding design decision.
Analysis: Evaluating Audio Embeddings for Music Understanding
In the field of multimedia information systems, audio embeddings play a crucial role in understanding and organizing large catalogs of music. These embeddings are representations of audio data that capture the underlying features and characteristics of the music.
Traditionally, the performance of audio embeddings has been evaluated based on their effectiveness in various downstream tasks, such as music search and recommendation. However, this article highlights the importance of also examining the local properties of the embedding spaces themselves.
In nearest neighbor algorithms, which are commonly used in music search and recommendation systems, the locality of neighborhoods in the embedding space is crucial. This means that similar songs should be close to each other in the space, enabling accurate retrieval and recommendation.
The article presents a novel approach to learning audio representations on music datasets through contrastive learning. It demonstrates that certain musical properties that are typically consistent within a track, such as key and tempo, are reflected in the locality of neighborhoods in the resulting embedding space.
Furthermore, the authors introduce the concept of using data augmentation strategies to modify the local properties of the embedding space. By applying appropriate augmentation techniques, the localization of certain properties can be reduced while improving the localization of others. For example, less relevant features like pitch and tempo can be mitigated, while more salient features such as genre and mood can be better localized.
This research achieves state-of-the-art performance in nearest neighbor retrieval accuracy by optimizing the selection of data augmentation strategies for contrastive learning of music audio embeddings. It emphasizes the importance of embedding design decisions in achieving effective music understanding systems.
The multi-disciplinary nature of this research is evident in its integration of concepts from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The techniques and insights gained from this study can have implications not only in music understanding but also in other domains where audio analysis and representation are crucial for information retrieval and recommendation systems.