9 accepted papers
Sound designers search for sounds in large sound effects libraries using aspects such as sound class or visual context. However, the metadata needed for such search is often missing or incomplete, and requires significant manual effort to add. Existing solutions to automate this task by generating m…
Human auditory perception is compositional in nature — we identify auditory streams from auditory scenes with multiple sound events. However, such auditory scenes are typically represented using clip-level representations that do not disentangle the constituent sound sources. In this work, we learn…
The COVID-19 pandemic had an unprecedented effect in human activity and city landscapes. A very notorious transformation during this period was the change in noise levels and patterns across cities. Small scale studies have show this change in noise levels across different locations in the globe. In…
Supervised learning for audio classification typically imposes a fixed class vocabulary, which can be limiting for real-world applications where the target class vocabulary is not known a priori or changes dynamically. In this work, we introduce a few-shot continual learning framework for audio clas…
Embedding models that encode semantic information into low-dimensional vector representations are useful in various machine learning tasks with limited training data. However, these models are typically too large to support inference in small edge devices, which motivates training of smaller yet com…
There are many sound classification problems that have target classes which are rare or unique to the context of the problem. For these problems, existing data sets are not sufficient and we must create new problem-specific datasets to train classification models. However, annotating a new dataset f…
Automated objective methods of audio source separation evaluation are fast, cheap, and require little effort by the investigator. However, their output often correlates poorly with human quality assessments and typically require ground-truth (perfectly separated) signals to evaluate algorithm perfor…
Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In pre…
Automated objective methods of audio evaluation are fast, cheap, and require little effort by the investigator. However, objective evaluation methods do not exist for the output of all audio processing algorithms, often have output that correlates poorly with human quality assessments, and require g…