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Found 7 papers, 4 papers with code
Advancing Natural-Language Based Audio Retrieval with PaSST and Large Audio-Caption Data Sets
This work presents a text-to-audio-retrieval system based on pre-trained text and spectrogram transformers.
Improving Natural-Language-based Audio Retrieval with Transfer Learning and Audio & Text Augmentations
The absence of large labeled datasets remains a significant challenge in many application areas of deep learning.
Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers
Standard machine learning models for tagging and classifying acoustic signals cannot handle classes that were not seen during training.
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.
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.
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning.
