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Found 9 papers, 5 papers with code
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples
If no data with similar sounds and matching recording conditions is available, data sets with a larger diversity in these two dimensions are preferable.
Towards Musically Meaningful Explanations Using Source Separation
Prior work on explainable models in MIR has generally used image processing tools to produce explanations for DNN predictions, but these are not necessarily musically meaningful, or can be listened to (which, arguably, is important in music).
audioLIME: Listenable Explanations Using Source Separation
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable.
Receptive-Field Regularized CNNs for Music Classification and Tagging
However, the MIR field is still dominated by the classical VGG-based CNN architecture variants, often in combination with more complex modules such as attention, and/or techniques such as pre-training on large datasets.
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Data augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models.
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels.
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features
Emotional aspects play an important part in our interaction with music.
Two-level Explanations in Music Emotion Recognition
Current ML models for music emotion recognition, while generally working quite well, do not give meaningful or intuitive explanations for their predictions.
An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems
We introduce an evolutionary stochastic-local-search (SLS) algorithm for addressing a generalized version of the so-called 1/V/D/R cutting-stock problem.
