Music Information Research (MIR) comprises all the research topics involved in modeling
and understanding music. Visualizations are frequently adopted to convey better …

Several speaker recognition algorithms failed to get the best results because of the wildly
varying datasets and feature sets for classification. Gender information helps reduce this …

The lack of data tends to limit the outcomes of deep learning research, particularly when
dealing with end-to-end learning stacks processing raw data such as waveforms. In this …

The work aims to propose a novel approach for automatically identifying all instruments
present in an audio excerpt using sets of individual convolutional neural networks (CNNs) …

Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks
for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw …

Audio classification is regarded as a great challenge in pattern recognition. Although audio
classification tasks are always treated as independent tasks, tasks are essentially related to …

Over the last decade, music-streaming services have grown dramatically. Pandora, one
company in the field, has pioneered and popularized streaming music by successfully …

We propose a two-stream convolutional network for audio recognition, that operates on time-
frequency spectrogram inputs. Following similar success in visual recognition, we learn …

A number of recent advances in neural audio synthesis rely on up-sampling layers, which
can introduce undesired artifacts. In computer vision, upsampling artifacts have been …

Recent work has shown that the end-to-end approach using convolutional neural network
(CNN) is effective in various types of machine learning tasks. For audio signals, the …