Normalization techniques are a boon for modern deep learning. They let weights converge
more quickly with often better generalization performances. It has been argued that the …

Recent advances in deep learning accelerated the development of content-based automatic
music tagging systems. Music information retrieval (MIR) researchers proposed various …

Automatic music captioning, which generates natural language descriptions for given music
tracks, holds significant potential for enhancing the understanding and organization of large …

This paper presents CQT-Diff, a data-driven generative audio model that can, once trained,
be used for solving various different audio inverse problems in a problem-agnostic setting …

We present Music Tagging Transformer that is trained with a semi-supervised approach. The
proposed model captures local acoustic characteristics in shallow convolutional layers, then …

The spiking neural network (SNN) mimics the information-processing operation in the
human brain. Directly applying backpropagation to the training of the SNN still has a …

We present an MatchboxNet-an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D time …

Transformer is a successful deep neural network (DNN) architecture that has shown its
versatility not only in natural language processing but also in music information retrieval …

Deep learning models have improved cutting-edge technologies in many research areas,
but their black-box structure makes it difficult to understand their inner workings and the …

Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …