This study focuses on the application of Convolutional Neural Networks (CNN) and combined LSTM (Long Short-Term Memory) and GRU (Gated Recurrent unit) models ...
Mar 14, 2023 · This case study aims is to develop audio tagging system that takes sound input, can figure out the category in an audio recording which contains noise also.
This study focuses on the application of Convolutional Neural Networks (CNN) and combined LSTM (Long Short-Term Memory) and GRU (Gated Recurrent unit) models
Accurate and automated sound classification enables a strong groundwork for diverse advanced deep learning applications within the audio and music domain.
This research aims to advance the state-of-the-art in multi-label sound classification and pave the way for more accurate and efficient audio classification ...
Jan 29, 2016 · The idea is to use a deep convolutional neural networks to recognize segments in the spectrogram and output one (or many) class labels.
Aug 31, 2020 · Neural network models for multi-label classification tasks can be easily defined and evaluated using the Keras deep learning library.
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Aug 31, 2024 · Abstract:Zero-shot learning models are capable of classifying new classes by transferring knowledge from the seen classes using auxiliary ...
Sep 29, 2024 · In this paper, we propose a learning method for a deep learning model that becomes more complex as multi label datasets increase. The existing ...
Abstract—Multi-label learning is an essential component of supervised learning that aims to predict a list of relevant labels for a given data point.