Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-
temporal patterns makes them well suited to environmental sound classification. However …

In this paper, we present an end-to-end approach for environmental sound classification
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …

Acoustic event and scene analysis has seen extensive development because it is valuable
in applications such as monitoring of elderly people and infants, surveillance, life-logging …

In this paper, we propose to use Convolutional Restricted Boltzmann Machine (ConvRBM)
to learn filterbank from the raw audio signals. ConvRBM is a generative model trained in an …

Acoustic Scene Classification (ASC) is one of the core research problems in the field of
Computational Sound Scene Analysis. In this work, we present SubSpectralNet, a novel …

Recent studies have demonstrated deep learning approaches directly from raw data have
been successfully used in image and text. This approach has been applied to audio signals …

In this paper, we study the usefulness of various matrix factorization methods for learning
features to be used for the specific acoustic scene classification (ASC) problem. A common …

Most of the time it is nearly impossible to differentiate between particular type of sound
events from a waveform only. Therefore, frequency-domain and time-frequency domain …