Publications by authors named "Yenisel Plasencia-Calana"
Article Synopsis
- - Human sound recognition is intuitive, while artificial systems struggle; recent advancements using deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have improved sound classification but often ignore the relationships between sound labels.
- - The researchers hypothesize that adding semantic information to DNNs can enhance sound recognition, mimicking how humans use both acoustic and semantic cues; they framed the task as a regression problem, training models to link sound spectrograms to continuous semantic representations.
- - Their findings show that the DNN model utilizing semantic labels (semDNN) outperformed the traditional label model (catDNN), aligning more closely with human similarity ratings in sound recognition, thus highlighting the importance of semantics in improving artificial
Taxonomies and ontologies for the characterization of everyday sounds have been developed in several research fields, including auditory cognition, soundscape research, artificial hearing, sound design, and medicine. Here, we surveyed 36 of such knowledge organization systems, which we identified through a systematic literature search. To evaluate the semantic domains covered by these systems within a homogeneous framework, we introduced a comprehensive set of verbal sound descriptors (sound source properties; attributes of sensation; sound signal descriptors; onomatopoeias; music genres), which we used to manually label the surveyed descriptor classes.
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