The capability of transcribing music audio into music notation is a fascinating example of
human intelligence. It involves perception (analyzing complex auditory scenes), cognition …

Following their success in Computer Vision and other areas, deep learning techniques have
recently become widely adopted in Music Information Retrieval (MIR) research. However …

Heart sound classification plays a vital role in the early detection of cardiovascular disorders,
especially for small primary health care clinics. Despite that much progress has been made …

Sound events often occur in unstructured environments where they exhibit wide variations in
their frequency content and temporal structure. Convolutional neural networks (CNNs) are …

The automatic recognition of spontaneous emotions from speech is a challenging task. On
the one hand, acoustic features need to be robust enough to capture the emotional content …

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs
take advantage of convolutional neural networks (CNNs) for local feature extraction and …

We advance the state of the art in polyphonic piano music transcription by using a deep
convolutional and recurrent neural network which is trained to jointly predict onsets and …

We present a content-based automatic music tagging algorithm using fully convolutional
neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional …

Sequences of feature vectors are a natural way of representing temporal data. Given a
database of sequences, a fundamental task is to find the database entry which is the most …

Deep neural networks in deep learning have been widely demonstrated to have higher
accuracy and distinct advantages over traditional machine learning methods in extracting …