3 accepted papers
Music listening context such as location or activity has been shown to greatly influence the users' musical tastes. In this work, we study the relationship between user context and audio content in order to enable context-aware music recommendation agnostic to user data. For that, we propose a semi-…
In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the sep…
In this paper, we propose a method for detecting marks of lossy compression encoding, such as MP3 or AAC, from PCM audio. The method is based on a convolutional neural network (CNN) applied to audio spectrograms and trained with the output of various lossy audio codecs and bitrates. Our method shows…