This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

Abstract Deep Neural Networks (DNN) are nothing but neural networks with many hidden
layers. DNNs are becoming popular in automatic speech recognition tasks which combines …

Current speech enhancement techniques operate on the spectral domain and/or exploit
some higher-level feature. The majority of them tackle a limited number of noise conditions …

Countless learning tasks require awareness of time. Image captioning, speech synthesis,
and video game playing all require that a model generate sequences of outputs. In other …

The performance of automatic speech recognition (ASR) has improved tremendously due to
the application of deep neural networks (DNNs). Despite this progress, building a new ASR …

In contrast to the conventional minimum mean square error (MMSE)-based noise reduction
techniques, we propose a supervised method to enhance speech by means of finding a …

Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to
give state-of-the-art performance on the TIMIT speech database. However, the results in that …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end
training methods such as Connectionist Temporal Classification make it possible to train …