Evaluating Compositional Structure in Audio Representations
Abstract
We propose a benchmark for evaluating compositionality in audio representations. Audio compositionality refers to representing sound scenes in terms of constituent sources and attributes, and combining them systematically. While central to auditory perception, this property is largely absent from current evaluation protocols. Our framework adapts ideas from vision and language to audio through two tasks: A-COAT, which tests consistency under additive transformations, and A-TRE, which probes reconstructibility from attribute-level primitives. Both tasks are supported by large synthetic datasets with controlled variation in acoustic attributes, providing the first benchmark of compositional structure in audio embeddings.
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Research summary
The paper presents a benchmark for evaluating compositionality in audio representations, introducing two complementary tasks that systematically assess the ability of audio encoders to capture complex sound structures. The methodology and experiments are robust, providing a valuable framework for future research in audio representation learning.
Methodology
The paper introduces a benchmark for evaluating compositionality in audio representations through two novel tasks, A-COAT and A-TRE. The methodology is well-structured, adapting concepts from vision and language to the audio domain, which is innovative. The tasks are defined clearly, and the use of synthetic datasets allows for controlled experimentation. The approach to measuring compositionality is systematic and reproducible, making it a valuable contribution to the field.
Results & evaluation
The experiments are comprehensive, benchmarking a diverse set of pretrained audio encoders and providing detailed results that highlight the differences in compositionality across models. The use of statistical tests to analyze the results adds rigor to the evaluation. The paper effectively demonstrates how different training paradigms influence the ability to capture compositional structure in audio representations.
Reproducibility
The paper provides sufficient implementation details, including the dataset generation process and evaluation protocols, which enhances reproducibility. The availability of code and datasets on GitHub further supports the reproducibility of the experiments.
Limitations
One limitation is the reliance on synthetic datasets, which may not fully capture the complexities of real-world audio compositions. Additionally, while the benchmark is a significant step forward, its effectiveness in evaluating real-world audio representations remains to be tested.
Broader impact
The proposed benchmark has the potential to influence future research in audio representation learning by providing a standardized way to evaluate compositionality, which is crucial for tasks involving complex audio scenes. This could lead to advancements in audio understanding, reasoning, and multimodal applications. The paper presents a benchmark for evaluating compositionality in audio representations, introducing two complementary tasks that systematically assess the ability of audio encoders to capture complex sound structures. The methodology and experiments are robust, providing a valuable framework for future research in audio representation learning.
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