… of the transfer learning potential with additional datasets and CNNs. In parallel, we will further
evaluate and compare transfer learning (upon … (learning rate, mini batch size, and epochs). …

… the characteristics inherited from audio. To this end, we propose a novel transfer learning
model pre-trained on large scale audio data for a heart sound classification task. In this study, …

… large-scale audio-based song classification tasks, and leverage them to perform other
classification tasks on different datasets. We show that this approach to transfer learning, which …

… -of-the-art methods for audio classification tasks for their predictive … For example, collecting
an Indonesian scops owl audio … To overcome such an issue, we propose a transfer learning …

… Thus, a classification instance in this work is a 30second long recording, referred to as
mixture, which is a background recording with or without an added target sound event. During …

… fields of transfer learning, image classification, educational machine learning tools, and
spectrogram audio … [5], an educational machine learning tool developed by Google that builds on …

… the concept of transfer learning and its potential applications to MIR tasks such as music
audio classification and similarity. In a traditional supervised machine learning setting, a system …

… audio transfer learning applications [47], [48]. Different from these, we study transfer learning
… ] hold even when we consider transfer learning between two domains with entirely different …

… This work proposes a transfer learning methodology for fault diagnosis of industrial bearings.
The VGGish architecture, originally pre-trained for sound classification on 70 million audio …

… most significant obstacles in fulfilling audio-based HAR (AHAR) … on transfer learning [9],
addressing exactly this problem. … feature extraction, transfer learning, and classification modules; …