github-actions
1041 lines
40 KiB
JSON
1041 lines
40 KiB
JSON
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"title": "Towards Blind Data Cleaning: A Case Study in Music Source Separation",
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"abstractNote": "The performance of deep learning models for music source separation heavily depends on training data quality. However, datasets are often corrupted by difficult-to-detect artifacts such as audio bleeding and label noise. Since the type and extent of contamination are typically unknown, cleaning methods targeting specific corruptions are often impractical. This paper proposes and evaluates two distinct, noise-agnostic data cleaning methods to address this challenge. The first approach uses data attribution via unlearning to identify and filter out training samples that contribute the least to producing clean outputs. The second leverages the Fr\\'echet Audio Distance to measure and remove samples that are perceptually dissimilar to a small and trusted clean reference set. On a dataset contaminated with a simulated distribution of real-world noise, our unlearning-based methods produced a cleaned dataset and a corresponding model that outperforms both the original contaminated data and the small clean reference set used for cleaning. This result closes approximately 66.7\\% of the performance gap between the contaminated baseline and a model trained on the same dataset without any contamination. Unlike methods tailored for specific artifacts, our noise-agnostic approaches offer a more generic and broadly applicable solution for curating high-quality training data.",
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"abstractNote": "The complex nature of musical emotion introduces inherent bias in both recognition and generation, particularly when relying on a single audio encoder, emotion classifier, or evaluation metric. In this work, we conduct a study on Music Emotion Recognition (MER) and Emotional Music Generation (EMG), employing diverse audio encoders alongside Frechet Audio Distance (FAD), a reference-free evaluation metric. Our study begins with a benchmark evaluation of MER, highlighting the limitations of using a single audio encoder and the disparities observed across different measurements. We then propose assessing MER performance using FAD derived from multiple encoders to provide a more objective measure of musical emotion. Furthermore, we introduce an enhanced EMG approach designed to improve both the variability and prominence of generated musical emotion, thereby enhancing its realism. Additionally, we investigate the differences in realism between the emotions conveyed in real and synthetic music, comparing our EMG model against two baseline models. Experimental results underscore the issue of emotion bias in both MER and EMG and demonstrate the potential of using FAD and diverse audio encoders to evaluate musical emotion more objectively and effectively.",
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"abstractNote": "The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used for this purpose even though its correlation with perceptual quality is understudied. We show that FAD performance may be hampered by sample size bias, poor choice of audio embeddings, or the use of biased or low-quality reference sets. We propose reducing sample size bias by extrapolating scores towards an infinite sample size. Through comparisons with MusicCaps labels and a listening test we identify audio embeddings and music reference sets that yield FAD scores well-correlated with acoustic and musical quality. Our results suggest that per-song FAD can be useful to identify outlier samples and predict perceptual quality for a range of music sets and generative models. Finally, we release a toolkit that allows adapting FAD for generative music evaluation.",
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"abstractNote": "Does cognitive reflection correlate with decision making and biases? Two recent studies have correlated cognitive abilities with less bias and more desirable decisions. To verify these results, 129 participants completed a set of 13 questions consisting of standard cognitive reflection test questions and 6 evaluation questions like the two previous studies. Our results successfully replicated the negative correlation of cognitive abilities to denominator neglect and belief biases, but we did not replicate the correlations to the other 4 evaluations including delayed gratification, risk-aversion faced with either potential gains or losses, and conjunction fallacy. The lack of correlation for delayed gratification and risk-aversion might be caused by our different (likely wealthier) participants, which might provide evidence against the expected utility theory that the option with the best expected value is most desirable. The lack of correlation for conjunction fallacy is likely due to the different wording used in the question.",
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"abstractNote": "Does natural doodling during lecture improve attention? A recent study by Andrade suggests that simulated doodling (shading in shapes) while listening will improve memory recall rate. However, their results might not extend to natural doodling. To investigate, 159 participants listened to a mock party invitation message with names to memorize. 69 of them were assigned to the doodling group where they freely doodled while listening. Unlike the study by Andrade, our study showed that the doodling group actually recalled 15% less names on a memory test. These results confirms that dual-tasking will hurt performance and shows that Andrade’s conclusions might only apply to shape-shading and not natural doodling. Further research could test whether the difference in results is due to natural doodling being more attention-demanding than shading, or if it is due to memory strategies such as rehearsal being used in the control group.",
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